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Identification of Potential miRNAs as Early Biomarkers for Oral Squamous Cell Carcinomas from a Molecular Oncologic and ...

Permanent Link: http://ufdc.ufl.edu/UFE0042082/00001

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Title: Identification of Potential miRNAs as Early Biomarkers for Oral Squamous Cell Carcinomas from a Molecular Oncologic and Salivary Diagnostic Approach
Physical Description: 1 online resource (99 p.)
Language: english
Creator: Patel, Rushi
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2010

Subjects

Subjects / Keywords: cancer, carcinoma, detection, diagnostics, early, micrornas, molecular, oncology, oral, potential, salivary, squamous
Molecular Cell Biology (IDP) -- Dissertations, Academic -- UF
Genre: Medical Sciences thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: In 2010, cancer will become the leading cause of death worldwide. Oral cancer ranks as the sixth deadliest cancer and accounts for more than one death per hour in the United States alone. A significant number of patients present with the late stage disease furthering the poor mortality rate. However, if detected early, patient survival rate greatly increases to upwards of 90%. Therefore, accurate and highly discriminative biomarkers and therapeutic agents of oral cancer are a necessary tool in order to further improve patient outcomes. Although there are recent publications on microRNA (miRNA) profiling using head and neck cancer cells and tumors there is ambiguity of the importance of miRNA subsets in oral cancer, necessitating further work. I seek to determine the potential signature miRNA expression profiles exclusive to oral cancer that can not only lead to early detection but also to help facilitate the identification of novel treatment targets.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Rushi Patel.
Thesis: Thesis (Ph.D.)--University of Florida, 2010.
Local: Adviser: Chan, Edward K.
Electronic Access: RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2012-08-31

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Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2010
System ID: UFE0042082:00001

Permanent Link: http://ufdc.ufl.edu/UFE0042082/00001

Material Information

Title: Identification of Potential miRNAs as Early Biomarkers for Oral Squamous Cell Carcinomas from a Molecular Oncologic and Salivary Diagnostic Approach
Physical Description: 1 online resource (99 p.)
Language: english
Creator: Patel, Rushi
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2010

Subjects

Subjects / Keywords: cancer, carcinoma, detection, diagnostics, early, micrornas, molecular, oncology, oral, potential, salivary, squamous
Molecular Cell Biology (IDP) -- Dissertations, Academic -- UF
Genre: Medical Sciences thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: In 2010, cancer will become the leading cause of death worldwide. Oral cancer ranks as the sixth deadliest cancer and accounts for more than one death per hour in the United States alone. A significant number of patients present with the late stage disease furthering the poor mortality rate. However, if detected early, patient survival rate greatly increases to upwards of 90%. Therefore, accurate and highly discriminative biomarkers and therapeutic agents of oral cancer are a necessary tool in order to further improve patient outcomes. Although there are recent publications on microRNA (miRNA) profiling using head and neck cancer cells and tumors there is ambiguity of the importance of miRNA subsets in oral cancer, necessitating further work. I seek to determine the potential signature miRNA expression profiles exclusive to oral cancer that can not only lead to early detection but also to help facilitate the identification of novel treatment targets.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Rushi Patel.
Thesis: Thesis (Ph.D.)--University of Florida, 2010.
Local: Adviser: Chan, Edward K.
Electronic Access: RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2012-08-31

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2010
System ID: UFE0042082:00001


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1 IDENTIFICATION OF POTENTIAL MICRORNAS AS EARLY BIOMARKERS FOR ORAL SQUAMOUS CELL CARCINOMAS FROM A MOLECULAR ONCOLOGIC AND SALIVARY DIAGNOSTIC APPROACH By RUSHI SHIRISH PATEL A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2010

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2 2010 R ushi Shirish Patel

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3 This is in tribute to those who are the fou ndation behind any success I may encounter. To my mother and father who supported me unconditionally through all of my paths. To my brother and sister in law who have helped guide me in the good times and bad. To my cousins Raju, Bitu, Parag, and Palak who have provided love and suppor t. To the Eight, whose relationship I will treasure and be thankful for until the end of my days To my mentor Dr. Chan who has given me guidance, wisdom, and the strength to persevere. And lastly, to every person wh o told another that they could not ac hieve something. To everyone who doubts the will and strength of the human spirit. Let me serve as a small, but proud symbol to the contrary

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4 ACKNOWLEDGMENTS It is with great pride and humility that I thank and recognize the following individuals for their unwavering support and assistance in my academic journey. It is no understatement when I mention that any positive and influential good that has sprung forth from my existence is a direct reflection on the man and woman who raised me. I was blesse d in my upbringing. My mother shielded me from all the worries that young men encounter in their coming of age. My father taught me what it means to be a good man. Ink on paper will never give justice to his character and the foundation he provided. I would be remiss if I did not mention the appreciation of all my extended family for the inspiration they bestowed upon me. I would like to thank and recognize the guidance I have received from my mentor, Dr. Edward Chan. He has provided a vision for me th at has helped mold me from a clinician to a scientist. And without the support from Dr. Chan or Dr. William McArthur, I would have never pursued research. In addition, I also thank Dr. Emma Lewis for brightening the roads which unleashed my newly discover ed passion for Oral and Maxillofacial Surgery. I express deeply profound collective gratitude to the members of my committee, Dr. Donald Cohen, Dr. Henrietta Logan, Dr. Naohiro Terada and Dr. Rolf Renne for their support and advice throughout my research a s well as other endeavors including the Andrew J. Semesco Foundation grant my T32 grant and Medical Guild incentive award. I thank all the past and present members of the Chan Lab for their uncanny ways of instilling a camaraderie filled atmosphere in our work environment. I would also like to extend a special thank you to Dr. Andrew Jakymiw for his friendship and guidance and for teaching me the basics when I first joined the lab. To the long list of unnamed who

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5 have helped mold me both professionally and personally, I extend my quiet, but sincere and humble gratefulness.

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6 TABLE OF CONTENTS page ACKNOWLEDGMENTS .................................................................................................. 4 LIST OF TABLES ............................................................................................................ 9 LIST OF FIGURES ........................................................................................................ 10 ABSTRACT ................................................................................................................... 11 CHAPTER 1 INTRODUCTION .................................................................................................... 13 Oral Cancer Statistics ............................................................................................. 13 RNA interference (RNAi) ......................................................................................... 13 Biogenesis and Maturation of miRNAs ................................................................... 14 Cancer and miRNAs ............................................................................................... 15 miRNA Profiling of Oral Squamous Cell Carcinomas (OSCCs) .............................. 16 Biomarkers in OSCCs ............................................................................................. 17 Salivary miRNAs as Biomarkers for OSCCs ........................................................... 17 2 DICER AND let 7 microRNA IN ORAL CANCER ................................................... 19 Introduction ............................................................................................................. 19 Materials and Methods ............................................................................................ 19 Cell Culture ....................................................................................................... 19 Western Blot ..................................................................................................... 20 Indirect Immunofluorescence ........................................................................... 21 RNA Isolation and RealTime Polymerase Chain Reaction (PCR) ................... 22 Transfections with miRNAs and siRNAs ........................................................... 22 Cell Proliferation Assay .................................................................................... 23 Tumor Xenografts ............................................................................................. 23 Statistical Analysis ............................................................................................ 24 Results .................................................................................................................... 24 Dicer Overexpression in Head and Neck Squamous Cell Carcinoma (HNSCC) Cell Lines ...................................................................................... 24 Dicer Up regulation in OSCCs .......................................................................... 24 The Cellular Localization of Dicer in a Mouse Model for Human Oral Cancer is Consistent with OSCCs ............................................................................. 25 let 7b Levels are DownRegulated in HNSCC Cell Lines ................................. 26 Dicer Depletion Inhibits Cell Proliferation of Oral Cancer Cells ........................ 27 Discussion .............................................................................................................. 28

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7 3 microRNA SIGNATURES IN ORAL CANCERS ..................................................... 41 Introduction ............................................................................................................. 41 Materials and Methods ............................................................................................ 42 Human Tissue Samples ................................................................................... 42 Cell Culture ....................................................................................................... 42 Tumor Xenografts ............................................................................................. 42 RNA Isolation ................................................................................................... 43 Real Time PCR ................................................................................................ 43 Sample and Exiqon Microarray Processing for the CAL 27 Tumors ................. 44 Sample and Agilent Microarray Processing for the Human Tumors ................. 45 Statistical Analysis ............................................................................................ 45 Results .................................................................................................................... 46 Measurement of RNA Integrity Prior to Analysis .............................................. 46 miRNAs are Significantly Differentially Expressed in OSCC Tissues ............... 46 Differentially Expressed miRNAs in the OSCC Tissues ................................... 46 Unsupervised Hierarchical Clustering of Tumor Tissues .................................. 47 qPCR Validation of Six miRNAs from the Human Tumor Microarray Using Two Analyses ................................................................................................ 48 Floor of the Mouth Orthotopic CAL 27 Xenogr aft Mouse Model System .......... 49 Fold Change Comparisons Between the Microarray of the Human Tumors and CAL 27 Tumors ...................................................................................... 49 qPCR Validatio n of the Tumor Study vs. in vivo and in vitro CAL 27 Cells ....... 50 Discussion .............................................................................................................. 51 4 MAJOR microRNA SIGNATURES IN HUMAN SALIVA .......................................... 65 Introduction ............................................................................................................. 65 Materials and Methods ............................................................................................ 65 Donors .............................................................................................................. 65 Saliva Collection ............................................................................................... 66 Salivary RNA Extraction ................................................................................... 66 Real Time PCR ................................................................................................ 67 miRNA Array Analyses ..................................................................................... 68 Statistical Analysis ............................................................................................ 69 Results .................................................................................................................... 69 RNA Purification from Human Saliva ................................................................ 69 The Variability of Total RNA as a Result of the Bacterial RNA Contribution in Saliva ......................................................................................................... 70 High RNA Yield in Saliva of Healthy Donors .................................................... 71 The Identification of Previously Undetected Salivary miRNAs .......................... 72 Discussion .............................................................................................................. 74 The Isolation of High Quality and High Yield RNA from Saliva ......................... 74 miRNA Signatures in Human Sal iva ................................................................. 77

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8 5 CONCLUSIONS ..................................................................................................... 86 miRNA Machinery in OSCCs ............................................................................ 86 miRNA Sign atures Specific to Oral Cancer ...................................................... 86 Elucidation of Salivary miRNA Biomarkers Through the Introduction of Novel Methods .............................................................................................. 88 LIST OF REFERENCES ............................................................................................... 90 BIOGRAPHICAL SKETCH ............................................................................................ 98

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9 LIST OF TABLES Table page 2 1. Staging and nodal st atus of patients with OSCC s .............................................. 34 3 1. Demographic and clinical information of the 17 OSCC samples used for analysis .............................................................................................................. 55 3 2. qPCR validation from the human tumor microarray using two analyses ............. 60 3 3. Fold change comparisons between the microarrays of the human tumors and CAL 27 tumors .................................................................................................... 63 3 4. qPCR validation of the tumor study vs in vivo and in vitro CAL 27 Cells ............ 64 4 1. Inter operator variability in the collection of total RNA from the saliva of 20 healthy donors .................................................................................................... 83 4 2. Identification of the 25 most abundantly expressed miRNAs in saliva by miRNA array analyses of 12 donor samples and comparison of the mean CT values with that of other publ ished reports. ........................................................ 85

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10 LIST OF FIGURES Figure page 2 1. Overexpression of Dicer in HNSCC cell lines ..................................................... 35 2 2. Dicer is up regulated in OSCCs .......................................................................... 36 2 3. The cellular localization of Dicer in a murine xenograft tumor is consistent with OSCCs ........................................................................................................ 37 2 4. let 7b expression is reduced in HNSCC cell lines ............................................... 38 2 5. Transfection of let 7b and siDicer in oral cancer cells ........................................ 39 2 6. Knockdown of Dicer inhibits cell proliferation of oral cancer cells ....................... 40 3 1. RNA collected from eighteen OSCCs derived from the tongue and five normal tongue tissues were detected using an Agilent bioanalyzer ................... 56 3 2. Differential expression of miRNAs based on the microarray analyses .............. 57 3 3. Variable expressi on patterns of the 68 miRNAs exhibiting p values of less t han 0.01 from the microarray ............................................................................. 58 3 4. Heatmap representation of the Pearsons correlation coefficients clustered using an unsupervised hierarchical clustering method ....................................... 59 3 5. Picture of a NOD.CB17 Prkdcscid mouse from Jac kson Laboratory, Bar Harbor, ME ......................................................................................................... 61 3 6. The tumor formation 16 days post injection of CAL 27 cells into the floor of the mouth both clinically and microscopically ..................................................... 62 4 1. Stabilizatio n of saliva for RNA isolation .............................................................. 80 4 2. The variable expression of total RNA in the saliva from donors is a result of the bacterial contribution and not fluctuations in mammalian RNA ..................... 81 4 3. Relatively high RNA yield collected across the saliva of 20 healthy d onors ....... 82 4 4. The CT values of the normalizers, snU6 and RNU48, between high and low salivary RNA producers ...................................................................................... 84

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11 Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy IDENTIFICATION OF POTENTIAL MICRORNAS AS EARLY BIOMARKERS FOR ORAL SQUAMOUS CELL CARCINOMAS FROM A MOLECULAR ONCOLOGIC AND SALIVARY DIAGNOSTIC APPROACH By Rushi Shirish Patel August 2010 Chair: Edward K. L. Chan Majo r: Medical Sciences Molecular Cell Biology In 2010, cancer will become the leading cause of death worldwide. Oral cancer ranks as the sixth deadliest cancer and accounts for more than one death per hour in the United States alone. A significant number of patients present with late stage disease furthering the poor mortality rate. Ho wever, if detected early, patient survival rate greatly increases to upwards of 90%. Therefore, accurate and highly discriminative biomarkers and therapeutic agents of oral cancer are a necessary tool in order to further improve patient outcomes. One of t he greatest advances in science during the past 10 years is the discovery of microRNAs (miRNAs) as key regulators across many biological processes including disease states such as cancer. The body of work presented applies the use of miRNAs to study oral cancer by investigating cells in vitro animal model systems and primary tumors Although there are recent publications on miRNA profiling using head and neck cancer cells and tumors there is ambiguity of the importance of the miRNA machinery and subsets of miRNAs in oral cancer, necessitating further work.

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12 Recent reports have demonstrated that Dicer, a RNase III endonuclease required for miRNA maturation, is aberrantly expressed in different types of cancer. Furthermore, Dicer has been reported to be r egulated by the let 7 family of miRNA genes. To better understand the overexpression of Dicer in oral cancer cells, we first evaluated the regulation of Dicer by let 7b. We found that they shared an inverse correlation in their expression levels and upon introduction of let 7b or small interfering RNAs targeting Dicer the proliferation of oral cancer cells was significantly inhibited. Furthermore, we examined the potential miRNA signatures exclusive to oral cancer by profil ing the expression of all miRNA s in oral cancers originating from the tongue compared to controls and found many to be aberrantly expressed. The data presented serve as a launching pad for future projects studying not only biomarkers for disease but pertinent mechanistic processes cont ributing to the formation and progression of oral cancer. Finally, we investigated a minimally invasive and easily accessible body medium housing biological information perhaps reflective of disease status in order to set the foundation to allow oral canc ers to be detected in their nascent stages. Moreover, we harvested high quality and high yield RNA from saliva for the first time. Therefore, we establish ed a methodology to possibly utilize the expression of miRNAs specific to oral cancer by examining s aliva These data illustrate that high quality miRNA s can be isolated and detected from saliva and, perhaps in future studies, specific changes in their levels may be shown to be reflective of early cancer detection

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13 CHAPTER 1 INTRODUCTION Oral Cancer Statistics Oral cancer is the sixth deadliest cancer in the world. In the United States alone, oral cancer accounts for only 3% of the annually diagnosed malignancies in men; a percentage seemingly insignificant without mortality statistics (Jemal et al., 2009; Warnakulasuriya, 2009) However, each year approximately 36,000 Americans will be diagnosed with oral cancer and close to 8,000 Americans will die from it (Jemal et al., 2009) De spite vast amounts of research and advances in the fields of oncology and surgery, mortality rates for oral cancer remain unchanged (Massano et al., 2006) Traditionally, risk factors for the development of oral cancer stem from the use of alcohol and tobacco. While it is of importance to note the parallels of lengt h and duration of use ; these risk factors are not indicators for the disease. Hence the search for a biological measure of disease is of vital significance. In order to develop new therapies and measures for the treatment of oral cancer ; novel and creat ive molecular insights into oral cancer biology are required. RNA interference (RNA i ) RNA interfererence, or RNAi, is a post transcriptional gene regulatory mechanism that can specifically silence gene expression by repressing translation and/or degrading mRNA by means of small noncoding doublestranded RNAs (dsRNAs) (Rana, 2007) Endogenous, small noncoding RNAs known as microRNAs (miRNAs) are a specific class of 21to 23 nt non coding evoluti onary conserved RNAs that mediate gene expression at the post transcriptional level by base pairing to partially complementary sites in the 3 untranslated region (3 UTR) of mRNAs (Rana, 2007) Hu man miRNAs

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14 regulate diverse cellular and molecular processes including cellular proliferation, differentiation, and apoptosis Human miRNAs are predicted to regulate >60% of all protein encoding genes within the human genome (Friedman et al., 2009; Rana, 2007) Therefore miRN As in humans have become established as one of the key regulatory molecules in the expression of coding genes (Bartels and Tsongalis, 2009; Hu et al., 2008; Zhang et al., 2007a) Many independent groups have reported differences in miRNA expression profiles between diseased and normal tissues ; thus illustrating their use for diagnostics (Dalmay, 2008) S ince miRNAs control many genes known to play important roles in cancer, they ca n act as either tumor suppressors or oncogenes. For example, it has been proposed that the downregulation of specific tumor suppressor miRNAs may upregulate the protein products of oncogenic genes Conversely, the u p regulation of specific oncogenic miRNAs reduces the levels of tumor suppressor genes Both scenarios can lead to a cancerous state. (Gome s and Gomez, 2008) Biogenesis and Maturation of miRNAs The biogenesis of miRNAs begins within the nucleus where miRNA genes are transcribed by RNA polymerase II into primary transcripts (pri miRNAs) (Jinek and Doudna, 2009) The pri miRNAs are then cleaved by the DroshaDGCR8 complex into precursor miRNAs (pre miRNAs) (Jinek and Doudna, 2009) Pre miRNAs are 7090nt long molecules with a hairpin structure that are subsequently exported into the cytoplasm where they are further processed by Dicer (Jinek and Doudna, 2009) Dicer is a highly conserved RNase III type enzyme that is essential for the RNAi and miRNA pathways (Rana, 2007) Dicer processes pre miRNAs into mature ~21 bp miRNA duplexes The miRNA duplexes are then subsequently incorporated into the RNA

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15 induced silencing complex (RISC) (Dalmay, 2008; Rana, 2007) There the passenger strand of the miRNA duplex is removed, allowing the guide strand to then target RISC to mRNAs containing partially complementary sequences in the 3 UTR (Dalmay, 2008; Rana, 2007) Subsequently, the targeted mRNAs become either translationally repressed or degraded within cytoplasmic structures termed, GW/P bodies (Jakymiw et al., 2007; Rana, 2007) Cancer and miRNAs The discovery of RNAi has stimulated research on the role of this cellular proc ess in the development and progression of cancer (Merritt et al., 2008) Although alterations in miRNA expression have been reported in cancer, the mechanisms of this dysregulation have not been fully elucidated (Calin and Croce, 2006) In some cases, genomic changes and changes in transcriptional regulation of miRNA expression have been found to correlate with changes in miRNA expression (Blenkiron et al., 2007; Zhang et al., 2006) Alternatively, global chan ges in miRNA expression in human cancers have also been linked to the dysregulation of genes required for miRNA biogenesis (Blenkiron et al., 2007; Zhang et al., 2006) Interestingly, during the last several years a number of reports have found Dicer to be aberrantly expressed in different types of cancer. More specifically, Dicer has been found to be overexpressed in prostate and precursor lesions of lung adenocarcinomas (Chiosea et al., 2006; Chiosea et al., 2007) or reduced in ovarian and lung cancer (Karube et al., 2005; Merritt et al., 2008) Furthermore, both low and high levels of Dicer correlate with poor prognosis in cancer patients (Chiosea et al., 2006; Chiosea et al., 2008; Karube et al., 2005; Merritt et al., 2008) The discrepancies in the dysregulation of Dicer expression among the various tumor types have been attributed t o tissue specific differences

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16 and/or the degree of aggressiveness of the cancer (Grelier et al., 2009) Dicer has been reported to be regulated by the let 7 family of miRNA genes (Forman et al., 2008; Selbach et al., 2008; Tokumaru et al ., 2008) miRNA Profiling of Oral Squamous Cell Carcinomas (OSCCs) The uncovering of the specific roles and correlation of the post transcriptional modification all miRNAs play on the genome will allow for a significant step forward in the search of biological markers to measure onset and likelihood of oral cancer. The significant findings of the role of let 7 in oral cancer cells indicate the need to profile all miRNAs. Oral c ancer specific miRNA signatures will be useful for determining the molecular basis of the tumor which in turn can be a beneficial translation into shaping a patients course of treatment. The miRNA signature profiles across a variety of cancers are surprisingly informative compared to messenger RNA profiles (Lu et al., 2005) The expression patterns of miRNAs unlike mRNAs reflect the developmental lineage of tumors as well as successfully classify poorly differentiated tumors (Gomes and Gomez, 2008; Lu et al., 2005) We ask the question if miRNA expression profiles could classify and perhaps be used as biomarkers for human head and neck squamous cell carcinomas (H NSCCs) in specific oral cancer ? Thus it was important to f irst determine which miRNAs are extremely upand downmodulated in cancer compared to controls and thereafter focus on their targets that could play pivotal roles in the progression of oral cancer. The long term goals will be to confirm these interactions in the in vitro and in vivo environments. It is hypothesized that miRNA profiling of oral cancers will eventually establish molecular diagnosis, prognosis, and therapy.

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17 Biomarkers in OSCCs A sig nificant number of patients present with the late stage disease furthering the poor mortality rate, due to inadequate screening protocols and poor access to proper health care (Chang et al., 2008; EsquelaKerscher and Slack, 2006; Zhang et al., 2007a) However, if detected early, patient survival rate greatly increases to upwards of 90% (Gomes and G omez, 2008) Therefore, accurate and highly discriminative biomarkers and therapeutic agents of oral cancer are a necessary tool in order to further improve patient outcomes (D'Silva and Ward, 2007; Henson et al., 2007) The key focus and inspiration of this study was the translation of research discovered on the bench to be used on the bedside: The identification of miRNA signatures specific to oral cancer, and the subsequent ability to detect via a noninvasive b iological medium such as saliva. The identification of biological markers of disease is a major impetus in todays research arena. Ideal biomarkers have the capacity to identify with a strong degree of accuracy a disease before it can be diagnosed clinically For example, certain biomarkers have the ability to detect cancers at early stages of the disease (Chin and Slack, 2008) Thus, the search for a minimally invasive and easily accessible body medium housing bi ological information; perhaps reflective of disease status is crucial to increasing survivability. Salivary miRNAs as B iomarkers for OSCCs It is expected that RNA based methods will soon overshadow the restrictions of current widely used forensic analys es for body fluid identification. The isolation and availability of stable RNA markers with tissue specific expression patterns will only help in phasing out shortcomings of the current methods requiring protein analysis or chemoluminescence (Zubakov et al., 2008) Therefore, we aim to utilize this aberrant

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18 expression of miRNAs potentially seen in cancer and develop a noni nvasive and clinically accessible screening protocol. In particular and of interest is the utilization of saliva to discern miRNA biomarkers that can potentially be used for early oral cancer detection.

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19 CHAPTER 2 DICER AND LET 7 MICRORNA IN ORAL CANCER Introduction HNSCCs, including OSCCs, also have aberrant miRNA expression levels (Chang et al., 2008; Childs et al., 2009; Henson et al., 2009; Li et al., 2009; Tran et al., 2007; Wong et al., 2008) Although alt erations in miRNA expression levels in HNSCCs can be attributed in some cases to the loss or amplification of chromosomal material (Henson et al., 2009) ; there is also the possibility, as described above, that thes e alterations could be due to potential defects in the miRNA biogenesis machinery, such as abnormal Dicer expression levels. Despite the growing evidence that Dicer appears to be aberrantly expressed in cancer, the regulation of this gene remains unclear. Interestingly, several recent studies have demonstrated that Dicer expression can be regulated by let 7 miRNA (Forman et al., 2008; Selbach et al., 2008; Tokumaru et al., 2008) The let 7 family of miRNA genes a re tumor suppressor miRNAs that have been found to be downregulated in lung, colorectal, and gastric cancers (Akao et al., 2006; Takamizawa et al., 2004; Yanaihara et al., 2006; Zhang et al., 2007b) Materials and Methods Cell Culture Human tongue SCC cell lines CAL 27 and SCC 25 and human HNSCC cell lines FaDu and RPMI 2650 were purchased from American Type Culture Collection (ATCC, Manassas, VA). Each of the cell lines were cultured in ATCC specified complete growth media in a 37C incubator with 5% CO2. Primary cultures of human gingival epithelial cells (pGECs) were obtained from gingival explants as described (Mao et al., 2007) and following Institutional Review Board (IRB) guidelines. The primary cells were cultured in

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20 keratinocyte growth medium (KGM; Cambrex, East Rutherford, NJ) at 37C in 5% CO2. pGECS were used for experimentation between passages 46. Western Blot Cultured or transfected cells were washed wit h phosphatebuffered saline (PBS) and lysed using RIPA buffer (50 mM Tris, pH 7.5, 150 mM NaCl, 0.5% sodium deoxycholate, 0.1% SDS, and 1% Triton X 100) with Complete EDTA free protease inhibitor (Roche, Palo Alto, CA, USA). The protein lysates were then resolved by SDS PAGE on a 10% gel and transferred to nitrocellulose. The nitrocellulose membrane was cut just below the 100 kDa molecular weight protein marker into two pieces and blocked in 5% nonfat dried milk in PBS Tween for 1 hour at room temperatur e. The top portion of the membrane containing the higher molecular weight proteins was incubated with mouse monoclonal anti Dicer antibody (1:100; clone 13D6; Abcam, Cambridge, MA) and the bottom portion of the membrane containing the lower molecular wei ght proteins was incubated with mouse monoclonal anti tubulin antibody (1:10,000; SigmaAldrich, St. Louis, MO) for 1 hour at room temperature. The membranes were then washed four times with PBS Tween and incubated with horseradishperoxidaseconjugated g oat anti mouse IgG (1:10,000; SouthernBiotech, Burmingham, AL) for 1 hour at room temperature. Immunoreactive bands were detected by the SuperSignal Chemiluminescent system (Thermo Scientific, Rockford, IL) according to the manufacturers instructions. T o quantify Dicer protein expression levels in HNSCC cell lines and pGECs, ImageJ software (Abramoff et al., 2004) was used to measure the integrated density of the Dicer signal normalized to tubulin levels.

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21 Indirect Immunofluorescence After receiving IRB approval, six randomly selected formalin fixed paraffinembedded oral carcinoma tissues and one normal gingival epithelial tissue specimen were obtained from the University of Florida Oral Pathology Biopsy Service archives and through Dr. Ikramuddin Aukhil in the Department of Periodontology, respectively. The staging and nodal status of the six patients with OSCCs are listed in Table 1. 4 m sections were cut and mounted on Superfrost Plus glass slides (Fisher Scientific, Pittsburgh, PA). The sectioned slides were deparaffinized using CitriSolv (Fisher Scientific, Pittsburgh, PA), hydrated by submersing in three separ ate concentrations of ethyl alcohol (100%, 95%, and 70%) and rinsed continuously in distilled water for 5 minutes. Afterwards, antigen retrieval was performed by incubating slides in a 1x Antigen Retrieval Citra Plus Solution (BioGenex, San Ramon, CA), ac cording to the manufacturers recommendation. Briefly, the slides were placed into the citratebased antigen retrieval solution at 95C for 20 minutes. The heated slides were cooled down to room temperature for another 20 minute interval, rinsed with dis tilled water for 5 minutes, and then blocked for 30 minutes with 1.5% Normal Horse Serum (Vector Laboratories, Burlingame, CA). Afterwards, the sections were incubated with rabbit polyclonal anti Dicer (1:50; Sigma Aldrich, St. Louis, MO) and mouse monocl onal anti EGFR (1:50; Dako, Carpinteria, CA) antibodies for 1 hour at room temperature. After washing with PBS, the slides were incubated with the corresponding secondary fluorochromeconjugated goat antibodies at room temperature for 1 h. Alexa Fluor 48 8 (1:400) and Alexa Fluor 568 (1:400) (Invitrogen, Carlsbad, CA) were the primary fluorochromes used. Finally, glass coverslips were mounted ont o the slides using VECTASHIELD Mounting Medium with 4 diamidino2 phenylindole (DAPI) (Vector

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22 Laboratories, Burlingame, CA). Fluorescence images were acquired with a Zeiss (Thornwood, NY) Axiovert 200M microscope, using a 40x 0.75 NA objective and Zeis s AxioVision software. The images were analyzed using Adobe (San Jose, CA) Photoshop CS4 software. Both the cancerous and control regions were confirmed by a board certified Oral and Maxillofacial Pathologist. RNA Isolation and Real T ime P olymerase C ha in R eaction (PCR) Total RNA including miRNAs were extracted and purified from cultured or transfected cells us ing the mirVana miRNA Isolation kit (Ambion/Applied Biosystems, Austin, TX), following the manufacturers instructions. RNA was quantitated using a NanoDrop ND 1000 spectrophotometer (Thermo Scientific, Wilmington, DE). For Dicer mRNA and 18S rRNA quan titation, total RNA was reverse transcribed using the High Capacity cDNA Reverse Transcription kit (Applied Biosystems, Foster City, CA). For let7a let 7b and RNU6B quantitation, total RNA was reverse transcribed using TaqMan specific RT primers and the TaqMan microRNA Reverse Transcription kit (Applied Biosystems, Foster City, CA). Afterwards, quantitative real time PCR was performed in an Applied Biosystems (Foster City, CA) StepOne Real Time PCR machine using predesigned TaqMan gene/miRNA specifi c assays for Dicer (Hs00229023_m1; assay location: nucleotide 2440/2496), 18S, let 7a let 7b and RNU6B (Applied Biosystems, Foster City, CA) combined with TaqMan Fast Universal PCR Master Mix (Applied Biosystems, Foster City, CA), according to the manuf acturers instructions. Transfections with miRNAs and siRNAs The let 7b miRNA and small interfering RNA (siRNA) targeting Dicer (siDicer) were previously described (Hutvagner et al., 2001; Pauley et al., 2006) and were

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23 synthesized by Thermo Scientific Dharmacon Inc. (Lafayette, CO). The siRNA targeting GFP (siGFP; target sequence, 5' GGC UAC GUC CAG GAG CGC ACC3') was commercially available and purchased from Thermo Scientific Dharmacon Inc. (Lafayette, CO). CAL 27 cells grown to 3050% confluency were either mock transfected or transfected with 100 nM of siGFP, siDicer, or let 7b using Lipofectamine 2 000 (Invitrogen, Carlsbad, CA), according to the manufacturers recommendation. Protein and mRNA extractions were performed 72 hours after transfection, after which the samples were further analyzed by Western blotting and real time PCR, according to the protocols described above. For the cell proliferation assay the cells were trypsinized 24 hours post transfection and reseeded on a 96well plate as described below. Cell Proliferation Assay Cell proliferation was quantitated using CellTiter 96 Aqueous One Solution Cell Proliferation Assay (Promega, Madison, WI) according to the manufacturers protocol. Quantitation of cell number was achieved by reseeding 5,000 cells on a 96well plate 24 hours after transfection. At 3, 6, and 8 days post transfecti on a BioRad (Hercules, CA) Model 680 microplate reader was used to read the absorbance of each well at 490 nm. The absorbance values were then converted to number of cells based on a calculated standard curve. Tumor Xenografts After receiving IACUC appr oval, CAL 27 cells grown to 70% confluence were suspended in Dulbeccos Modified Eagles Medium containing 10% fetal bovine serum / Matrigel (1:1; BD Biosciences, Bedford, MA) at 500,000 cells per 50 l and injected submucosally in the floor of mouth of anesthetized eight week old NODSCID mice (NOD.CB17 Prkdcscid strain; Jackson Laboratory, Bar Harbor, ME). Oral tumors were

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24 grown for two weeks, after which the animals were sacrificed. The tumor tissue was then harvested and fixed in 10% formalin solution. Tissues were paraffinembedded and 5m thick sections were stained with hematoxylin and eosin (H & E). The sections were also processed for indirect immunofluorescence analysis as described above. H & E stained tissue sections were imaged with a Leic a (Bannockburn, IL) DMLB2 microscope, using a 2.5x objective and Media Cybernetics (Bethesda, MD) QCapture Pro software. Statistical Analysis Comparisons between groups were performed using oneway ANOVA. A value of P < 0.05 was considered statistically s ignificant. Results Dicer Overexpression in Head and Neck Squamous Cell Carcinoma ( HNSCC) Cell Lines Dicer protein expression in several HNSCC cell lines was examined and compared to normal primary gingival epithelial cells (pGECs) by Western blot analysis ( Figure 21 ). Quantitation of Dicer expression levels using ImageJ software demonstrated that HNSCC cell lines (FaDu, SCC 25, CAL 27, and RPMI 2650) exhibited between 4 to 24 fold differences in Dicer protein levels compared to pGECs. More specifically, two OSCC derived cell lines (SCC 25 and CAL 27), had between 10 to 14 times the level of Dicer compared to pGEC cells. Serial dilutions of RPMI 2650 cell lysates were also included to aid in visualizing the degree of Dicer overexpression. Dicer Up regula tion in OSCCs The aberrant expression of Dicer protein observed in HNSCC cell lines, including oral cancer cell lines, prompted us to examine the Dicer protein expression levels and

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25 cellular localization patterns on six randomly selected histological secti ons of OSCCs in comparison to normal gingival epithelial tissue. To help demarcate the basal epithelium from the underlying connective tissue within the normal tissue and to also help identify tumors of epithelial cell origin, the OSCCs were simultaneousl y stained for epidermal growth factor receptor (EGFR). EGFR is a known marker for epithelial derived HNSCC cells, but it is also found to be expressed in normal proliferating epithelial cells and thus can be found in the growth zone (i.e. basal third regi on) of the normal oral epithelium (Hanahan and Weinberg, 2000; Herbst, 2004) Indirect immunofluorescence (IIF) analysis demonstrated cytoplasmic staining of Dicer and its upregulation in OSCCs in comparison to t hat observed in normal epithelial tissue, which did not have any detectable levels of Dicer ( Figure 21 ). Interestingly, Dicer was also found to strongly localize to discrete cytoplasmic foci within the cancer cells ( Figure 21 inset panels, arrows). II F analysis of the six different OSCCs demonstrated moderate to strong diffuse cytoplasmic and discrete cytoplasmic foci staining of Dicer in 83% (5/6) of the samples examined in comparison to that observed in normal epithelial tissue. The two OSCCs that w ere most representative in terms of Dicer staining pattern of the six OSCCs examined and that had the strongest diffuse cytoplasmic and discrete foci expression of Dicer compar ed to normal tissue are shown ( Figure 2 1) The Cellular Localization of Dice r in a Mouse Model for Human Oral Cancer is Consistent with OSCCs To confirm the Dicer staining pattern observed in OSCCs, we also analyzed Dicer localization in a mouse floor of mouth model for human oral cancer. The murine xenograft tumor was generated using the CAL 27 cell line. Hematoxylin and eosinstained sections of the harvested tissue confirmed the presence of the murine CAL 27

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26 xenograft tumor ( Figure 23A ). Furthermore, IIF analysis of the CAL 27 xenograft tumor demonstrated Dicer localization within the cytoplasm and in discrete cytoplasmic foci consistent with staining observed in OSCCs ( Figure 23B ). To help demarcate the tumor, the tissue was also costained for EGFR. Of interest were the reproducible findings that Dicer strongly localized t o discrete cytoplasmic foci in both human OSCCs and in the xenograft tumor. Because the Dicer foci strongly resembled GW/P bodies, cytoplasmic structures that are linked to RNAi function (Eystathioy et al., 2002; J akymiw et al., 2005; Jakymiw et al., 2007) and studied by our lab; we also costained Dicer with a GW/P body marker in the xenograft tumor tissue. The staining, however, revealed that the cytoplasmic Dicer foci did not colocalize with GW/P bodies (data not shown). let 7b Levels are Down R egulated in HNSCC Cell Lines Having demonstrated that Dicer protein was overexpressed in HNSCC cell lines and in OSCCs; we next wanted to determine whether this upregulation was potentially due to the aberrant expression of the Dicer transcript. Real time PCR analysis of Dicer mRNA expression levels in the HNSCC cell lines, interestingly, demonstrated that the levels of Dicer mRNA were only significantly upregulated in two (FaDu and SCC 25) out of the four HNSCC cell lin es in comparison to pGECs ( Figure 24A ). The fact that Dicer mRNA was not significantly overexpressed in the other two HNSCC cell lines (CAL 27 and RPMI 2650) even though the protein was (see Figure 21 ), suggested that Dicer protein expression levels may be regulated at the post transcriptional level. Because several recent reports have demonstrated that Dicer expression can be regulated by let 7a and let 7b (Forman et al., 2008; Selbach et al., 2008; Tokumaru et al., 2008) we performed real time PCR to examine the expression levels of these two

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27 miRNAs in the HNSCC cell lines compared to pGECs ( Figure 24B ). Let 7b but not let 7a, was significantly found to be reduced in three (SCC 25, CAL 27, and RPMI 2650) out of the four HNSCC cell lines in comparison to pGECs. In particular, a ~4095% reduction in let 7b levels was observed in the HNSCC cell lines compared to control cells. Only FaDu cells exhibited no change in both let 7a and let 7b expression in compari son to pGECs. Dicer Depletion Inhibits Cell Proliferation of Oral Cancer Cells Due to the aberrant expression of let 7b in HNSCC cells, the apparent inverse correlation of let 7b levels with Dicer protein expression, and the fact that Dicer protein levels have been previously demonstrated to be specifically regulated by the direct targeting of let 7b (Forman et al., 2008; Selbach et al., 2008) we next wanted to confirm whether Dicer was similarly regulated by let 7 b in oral cancer cells. Therefore, CAL 27 cells were either mock transfected or transfected with control nontargeting green fluorescent protein (GFP) siRNA (siGFP), an siRNA targeting Dicer (siDicer), or let 7b. The siDicer has been previously demonstra ted to suppress Dicer levels (Hutvagner et al., 2001) and was therefore used as a positive control. Western blot analysis confirmed that siDicer and let 7b were capable of suppressing Dicer protein expression leve ls 72 hours post transfection compared to siGFP and mock transfected cells ( Figure 2 5A ). More specifically, based on serial dilutions of siGFP transfected cell lysates that were used to aid in quantitating the degree of Dicer overexpression, cells transf ected with siDicer and let 7b exhibited ~60% and ~30% knockdown of Dicer protein relative to siGFP transfected cells, respectively. Furthermore, real time PCR analysis of Dicer mRNA levels demonstrated that siDicer, but not let 7b was capable of repressi ng Dicer mRNA levels ( Figure 25B ). Having demonstrated that siDicer and let 7b were capable

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28 of suppressing Dicer protein levels, we next wanted to assess the effects of Dicer depletion on oral cancer cell proliferation. Therefore, a cell proliferation experiment was carried out where CAL 27 cells were either mock transfected or transfected with siGFP, siDicer, or let 7b, after which cell numbers were assayed 3, 6, and 8 days post transfection (Figure 26 ). The growth curve showed that silencing of Dicer with either siDicer or let 7b significantly inhibited cell proliferation over a period of eight days compared to control siGFP or mock transfected cells. Furthermore, the growth inhibitory effect was greater for siDicer compared to let 7b The largest average percent inhibition on cell proliferation was observed three days post transfection with siDicer and let 7b transfected cells exhibiting a >100% and 83% inhibitory effect relative to control siGFP transfected cells, respectively. The inhibitory effect decreased over time, but was still effectual even up to eight days post transfection with siDicer and let 7b transfected cells still maintaining a 70% and 40% inhibitory effect on oral cancer cell proliferation, respectively. Discussion Alterations of D icer expression levels in various types of cancer have been observed (Chiosea et al., 2006; Chiosea et al., 2007; Chiosea et al., 2008; Karube et al., 2005; Merritt et al., 2008) but nothing has been reported about the status of Dicer in HNSCCs, in particular OSCCs until now. Furthermore, not much is known about the regulatory mechanisms that cause these alterations of Dicer expression levels in cancer. Therefore, this study explored the potential roles of Dicer and the regulatory mechanisms governing its expression levels in the pathogenesis of HNSCCs with a focus on oral cancer. Dicer protein was demonstrated to be overexpressed in all four HNSCC cell lines examined, including two tongue SCC derived cell lines, relative to

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29 normal primary gingival epithelial cells. Furthermore, 83% (5/6) of OSCCs examined had increased Dicer protein expression, where Dicer was found to localize diffusely within the cytoplasm and in discrete cytoplasmic foci of the cancer cells, compared to cells within normal gingival epithelial tissue, which did not have any detectable levels of Dicer. The Dicer staining pattern observed in OSCCs was also confirmed in a mouse model of human oral cancer. Together, the above findings demonstrate that based on the samples analyzed, HNSCCs, including OSCCs, have increased Dicer protein levels and are consistent with several reports that have similarly found increased Dicer protein expression in other types of cancer, including prostate and ovarian carcinomas, and in SCCs of the lung (Chiosea et al., 2006; Chiosea et al., 2007; Flavin et al., 2008) It is important to note that a limitation of our study was the use of a small number of cell lines and tissues for Dicer expression analysis. Even though we found Dicer to be overexpressed in the HNSCC cell lines and tissues we examined, a more comprehensive study will be needed that will include testing of a greater number of cell lines and tissues to more concl usively ascertain the extent of highlevel expression of Dicer in HNSCCs. In the examination of Dicer mRNA in the HNSCC cell lines, only two of the cell lines (FaDu and SCC 25) had significant upregulation of Dicer mRNA that could account for the increas ed protein expression. The other two cell lines (CAL 27 and RPMI 2650) did not have significant increases in Dicer mRNA, yet the Dicer protein levels were overexpressed. In fact, the RPMI 2650 cell line had reduced levels of Dicer mRNA compared to the control cells and yet had the highest level of Dicer protein expression of all the HNSCC cell lines analyzed. These findings suggest that Dicer

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30 expression is regulated at the post transcriptional level. Interestingly, our data corroborates several studies that have similarly reported that Dicer mRNA does not correlate well with protein expression and that the regulation of Dicer expression seems to be largely at the post transcriptional level (Grelier et al., 2009; W iesen and Tomasi, 2009) Recently, it was reported that Dicer expression was regulated by let 7 (Forman et al., 2008; Selbach et al., 2008; Tokumaru et al., 2008) To better understand the discrepancy in Dicer mR NA and protein expression levels observed in the HNSCC cell lines, the levels of let 7a and let 7b two miRNAs previously found to regulate Dicer expression, were examined. Our analysis demonstrated that let 7b was significantly reduced in three out of the four HNSCC cell lines, including the two oral cancer cell lines, relative to control cells. This finding implied that the increased Dicer protein expression was due to the aberrant expression of let 7b in HNSCC cells. The fact that RPMI 2650 cells had extremely low levels of let 7b could explain why the protein was highly upregulated even though the mRNA was reduced relative to normal cells. Furthermore, to validate let 7b regulation of Dicer expression levels, an exogenous mature let 7b was transfect ed into the CAL 27 oral cancer cell line which led to a reduction in Dicer protein levels, but not Dicer mRNA. This data suggested that let 7b mediated reduction of Dicer protein was potentially due to translational repression and not mRNA degradation and confirmed a similar observation made by Selbach et al. who reported that Dicer was likely a direct translational target of let 7b (Selbach et al., 2008) Of note, although our study focused on the analysis let 7a and let 7b levels in HNSCC cell lines, there is the possibility that other let 7 fa mily members may also contribute to

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31 the aberrant expression of Dicer in HNSCCs. Interestingly, let 7d has recently been reported to be reduced in primary human HNSCC tumors (Childs et al., 2009) Let 7d is a regulator of Dicer expression (Tokumaru et al., 2008) and could therefore, also potentially contribute towards the overexpression of Dicer in HNSCCs. Due to the fact that Dicer protein was increased in HNSCC cell lines and OSCCs, the biological consequence of Dicer in oral cancer cells was examined, in particular its role in cell proliferation. The addition of either an exogenous siRNA targeting Dicer or mature let 7b significantly inhibited the proliferation of oral cancer cells compared to control cells as early as three days post transfection. The Dicer siRNA had a greater repressive effect on cell proliferation compared to let 7b. This was most likely due to the fact that the siRNA had a stronger knockdown effect on Dicer protein compared to let 7b. This is not unusual as miRNAs have been proposed to act as biological rheostats that make finescale adjustments to protein output (Baek et al., 2008) Regardless, our data demonstrates that Dicer appears to be a critical component for cell growth and supports earlier studies that have shown Dicer to be im portant in regulating cell cycle events and cell proliferation (Carmichael et al., 2004; Hatfield et al., 2005; Murchison et al., 2005) Moreover, let 7b appears to be an important regulator of Dicer levels and ca n affect the cell proliferation of oral cancer cells. Of note, let 7 has been reported to repress cell proliferation and is thought to be a master regulator of cell proliferation pathways (Johnson et al., 2007) Interestingly, Dicer was identified as one of several let 7 targets that can potentially influence cell division (Johnson et al., 2007) Although our group and others have demonstrated that specific types of tumors have elevated levels of Dicer protein, there is evidence that also demonstrates that

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32 specific cancers can have decreased Dicer expression levels, such as in advanced lung adenocarcinomas (Karube et al., 2005) One simple explanation for these discrepancies could be due to tissue specific differences. Another possibility is that some of these studies focus primarily on the measurement of Dicer mRNA levels and not protein, and if one recalls, our data clearly demonstrated that Dicer mRNA does not correlate well with protein expression. Therefore, data obtained from PCR analyses needs to be carefully interpreted because even though the level of Dicer mRNA may be found to be lower in comparison to normal cells, it does not necessarily mean the protein level will be similarly repressed. Take for example our findings for the RPMI 2650 cell line, where the Dicer mRNA was reduced compared to normal cells, yet the protein was still highly expressed. This example highlights the complexities of Dicer expression regulation and demonstrates that Dicer can be regulated by post transcriptional regulatory mechanisms. This could also help explain some of the discrepancies observed in Dicer expression within the same tumor types. The fact that Dicer expression appears to be related to the aggressiveness and metastatic spread of cancer and that the Dicer gene is predicted to produce 14 mRNA variants (Grelier et al., 2009) further reaffirms the complexities associated with the regulation of the expression of this gene. Regardless, the fact that Dicer expression varies in different cancer types suggests that Dicer dysregulation appears to be important for cancer progression. In conclusion, based on the samples analyzed, our present study demonstrated that Dicer protein was upregulated in oral cancer cells and that its levels appeared to be regulated by let 7b. Moreover, Dicer knockdown inhibited cell proliferation of oral cancer cells. Together, this work implies that Dicer upregulation in conjunction with let -

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33 7b down regulation contributes to oral cancer progression. Silencing the expression of Dicer using RNAi strategies could be potentially used as an adjunct therapy to curb the proliferation of cancer cells. This work was published in 2010 in Genes, Chromosomes and Cancer volume 49, issue 6, pages 549559.

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34 Table 2 1. Staging and nodal status of patients with OSCCsa OSCC b Tumor Stage Nodal status 1 III Positive 2 I Negative 3 II Negative 4 I Neg ative 5 I Negative 6 III Negative aAll biopsies were taken from primary tumors. bOSCC

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35 Figure 21. Overexpression of Dicer in HNSCC cell lines. Western blot analysis of Dicer expression in several HNSCC cell lines (FaDu, SCC25, CAL 27, and RPMI 265 0) compared to normal primary gingival epithelial cells (pGEC). Tubulin antibody was used to check for equal loading of samples. Serial dilutions of RPMI 2650 cell lysates (50%, 25%, and 12.5%) were included to aid in visualizing the degree of Dicer overexpression. The data is representative of two independent experiments. f/d; fold difference, quantitative measurement of the relative Dicer protein fold expression differences using ImageJ software.

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36 Figure 2 2. Dicer is up regulated in OSCCs. Indirec t immunofluorescence analysis of Dicer (green) expression in formalin fixed paraffinembedded normal human gingival epithelial and two OSCC tissues. EGFR (red) detection was used to help demarcate the normal basal epithelium from the underlying connective tissue and also help identify epithelial derived cancerous regions in the OSCC tissues. Inset panels are higher (3x) magnification views of the boxed areas demonstrating the discrete cytoplasmic foci staining of Dicer (arrows). Nuclei (blue) were countersta ined with DAPI. Scale bar: 25 m.

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37 A B Figure 23. The cellular localization of Dicer in a murine xenograft tumor is consistent with OSCCs. (a) Hematoxylin and eosinstained CAL 27 murine xenograft tumor. Arrows demarcate the boundary between the xenograft tumor and the overlying muscle tissue of the mouse tongue. Scale bar: 500 m. (b) Indirect immunofluorescence analysis of Dicer (green) expression in formalin fixed paraffinembedded xenograft tumor tissue. The discrete cytoplasmic foci staini ng of Dicer are indicted by arrows. EGFR (red) detection was used to help demarcate the xenograft tumor. Nuclei (blue) were counterstained with DAPI. Scale bar: 10 m.

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38 A B Figure 24. let 7b expression is reduced in HNSCC cell lin es. Real time PCR a nalysis of A ) Dicer mRNA and B ) let -7 microRNA expression levels in several HNSCC cell lines (FaDu, SCC25, CAL 27, and RPMI 2650) compared to normal primary gingival epithelial cells (pGEC). Data shown were obtained from three biological replicates. S.E.M.; standard error measurement. *P < 0.05.

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39 A B Figure 2 5. Transfection of let 7b and siDicer in oral cancer cells CAL 27 cells were either mock transfected or transfected with siGFP, siDicer, or let 7b A) Western blot analysis of Dicer protein levels 72 hours post transfection. Serial dilutions of siGFP lysates (50%, 25%, and 12.5%) were used to help comparatively quantitate the degree of protein knockdown. Tubulin was used as a loading control. The experiment was repeated two additional tim es with similar results. B) Real time PCR analysis of Dicer mRNA levels 72 hours post transfection. The data is representative of three independent experiments.

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40 Figure 26. Knockdown of Dicer inhibits cell proliferation of oral cancer cells 5 000 cells were seeded on a 96well plate 24 hours after either mock transfection or transfection with siGFP, siDicer, or let 7b Cell numbers were assayed 3, 6, and 8 days post transfection. The results represent the mean standard error measurement. Indicat ed at each time point are also the average percent inhibitory effects on cell proliferation for siDicer and let 7b relative to siGFP transfected cells. The data is representative of three independent experiments performed in triplicate. *P < 0.05 in comparison to control siGFP or mock transfected cells.

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41 CHAPTER 3 micro RNA SIGNATURES IN ORAL CANCERS Introduction Human oral cancer primarily relies on classical forms of treatment including surgery, radiation, and chemotherapy or a combination of these methods. Despite vast amounts of research and advances in the fields of oncology and surgery, mortality rates remain unchanged with a survival rate that is less than 50%. This poor prognosis is a consequence of either two possibilities: late detection of HNSC C due to poor markers of risk and inadequate treatment options (Henson et al., 2007) Therefore, new treatment strategies are needed and early diagnosis is clearly of great importance. It has been implicated in t he recent literature that a class of noncoding small RNAs, known as miRNAs have crucial roles across many biological processes including disease states as well as cancers. They have become established as one of the major regulatory guardians of coding g enes in the human genome (Bartels and Tsongalis, 2009) and m any independent groups have published varying miRNA expression prof iles between cancerous and normal tissues, signifying their use for diagnostics (Dalmay, 2008) By mediating gene expression at the post transcriptional level, miRNAs can regulate highly complex signal transduction pathways and their biological role s in cancer suggest an association with prognosis and therapeutic outcome. Therefore, the aim of this study was to examine which miRNAs are aberrantly expressed in OSCC s and to establish a model system which facilitates the study of miRNA expression as bi omarkers for oral cancer.

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42 Materials and Methods Human Tissue Samples All human OSCC and normal tongue tissues were obtained as deidentified samples from the Tissue Bank at the Moffitt Cancer Center and approved by the University of Florida institutional review board. Surgical specimens were collected from patients who underwent surgery at the Moffitt Cancer Center. The cancer samples contained greater than 80% tumor cells, confirmed by microscopic examination by a head and neck pathologist. Their demog raphic and clinical information is included ( Table 3 1 ) The normal tongue specimens were taken from a non cancerous region. The tissues were snap frozen and stored at 70C until further use. Cell Culture Human tongue CAL 27 cell line was purchased from A merican Type Culture Collection (ATCC, Manassas, VA). The cell line was cultured in ATCC specified complete growth media in a 37C incubator with 5% CO2. Tumor Xenografts After receiving IACUC approval, CAL 27 cells grown to 70% confluence were suspended in Dulbeccos Modified Eagles Medium containing 10% fetal bovine serum /Matrigel (1:1; BD Biosciences, Bedfor d, MA) at 500,000 cells per 50 l and injected submucosally in the floor of mouth of anesthetized eight week old NODSCID mice (NOD.CB17 Prkdcsc id strain; Jackson Laboratory, Bar Harbor, ME). Oral tumors were allowed to be established and grown for two to three weeks, after which the animals were sacrificed. The tumor tissue were carefully dissected out so as to exclude normal mouse tissue and t he volume was calculated. Ther e after the tumorous mass was

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43 incised in 0. 2 5 cm sections for appropriate penetration of Ambions RNAlater Solution in which they were stored at 20C for at least 24 hours to help avoid RNA degradation. RNA I solation The human oral cancer tumors and normal tongue tissues, which measured approximately 60mm3 in volume, were disrupted using the PowerGen 125 (Fisher Scientific, Pittsburgh, PA ) in 600 L of Lysis/Binding buffer from the mirVana miRNA Isolation kit ( Ambion/ A pplied Biosystems, Austin, TX). Total RNA including miRNAs from tissue samples and cell lines were extracted and purified using the mirVana miRNA Isolation kit for tissues ( Ambion/Applied Biosystems, Austin, TX) and mirVana miRNA Isolation kit ( Ambion/ Applied Biosystems, Austin, TX), following the manufacturers instructions. RNA was quantitated using a NanoDrop ND 1000 spectrophotometer (Thermo Scientific, Wilmington, DE). T he Agilent 2100 Bioanalyzer from the In terdisciplinary Center for Biotechnology Research at the University of Florida was used to detect the size distribution of total RNA as well as determine the quality of the RNA. Real Time PCR For snU6 RNA, miR424, miR 135b, miR 15b, miR 375, miR 494, and miR 4865p miRNA quantitation, total RNA was reversetranscribed using TaqMan specific RT primers and the TaqMan microRNA Reverse Transcription Kit (Applied Biosystems Foster City, CA ). Afterward quantitative real time PCR was performed in an Appli ed Biosystems StepOne Real Time PCR machine using predesigned TaqMan gene/miRNA specific assays for snU6, miR424, miR 135b, miR 15b, miR 375, miR 494, and miR 4865p ( Applied Biosystems Foster City, CA ) combined with TaqMan

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44 Fast Universal PCR Master Mi x (Applied Biosystems Foster City, CA ) according to the manufacturers instructions. Sample and Exiqon M icroarray P rocessing for the CAL 27 T umors Total RNA was isolated from two CAL 27 tumors and two normal tongue tissues using Trizol Reagent according to the manufacturer's protocol (Invitrogen Corporation, Carlsbad, CA) and subsequently purified with a Qiagen RNEasy Mini Kit (Qiagen, Inc., Valencia, CA) using a modified protocol to retain small RNA. Briefly, 3.5 volumes of ethanol were added to the s ample after the addition of RL T while the subsequent binding and washing steps remained unchanged. RNA was quantitated using a NanoDrop ND 1000 spectrophotometer (Thermo Scientific, Wilmington, DE) for quality control. 1 g of sample RNA, and 1 g of m ercury LNA microRNA Array Spike in miRNA (Exiqon, Inc., Woburn MA) were then fluorescently labeled with Hy3 using the miRCURY LNA microRNA Labeling Kit (Exiqon, Inc., Woburn MA ). Each labeled sample was hybridized to a miRCURY LNA microRNA Array v.11.0. Hybridization and washing were carried out using a Tecan HS 4800 Pro Hybridization Station (Tecan Group Ltd., Mannedorf, Switzerland) using a hybridization protocol supplied by Exiqon, Inc. The microarrays were scanned with an Agilent G2505B Microarray Sc anner (Agilent Technologies, Inc., Santa Clara, CA) at a 10 m resolution with a 100% PMT sensitivity setting. Image analysis and extraction was performed using ImaGene 8.0 analysis software (BioDiscovery, Inc., El Segundo, CA ) using the latest .gal file f rom Exiqon. The raw intensity data were exported and analysis was performed by first subtracting the background signal, determined by taking the average of the ten lowest reading s on the array. Spike in signals and house keeping genes were then averaged

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45 a nd used to obtain a multiplication factor in normalizing the signal intensity between arrays, followed by further clustering analysis using Clustal and Treeview. Sample and Agilent M icro array Pr ocessing for the H uman T umors Samples for miRNA profiling stud ies were processed by Asuragen Services (Austin,TX), according to the companys standard operating procedures. Following incoming sample quality control (QC) assessment, labeling of total RNA was performed according to the published method of Wang et al. (Wang et al., 2007) Briefly, 100 ng total RNA per sample was dephosphorylated with calf intestinal ph osphatase and the pCp Cy3 labeling molecule was ligated to the 3 ends of the RNA molecules. Labeled RNA was purified using BioSpin6 (BioRad, Hercules CA) and hybridized to Agilent Human miRNA Microarrays Rel12.0 (Agilent, Santa Clara CA) according to Ag ilent miRNA protocol v2.1. Hybridization, washing, imaging, and signal extraction were performed according to Agilent recommended procedures. Slides were scanned on Agilent DNA microarray scanner (model G2565A). The scanned signals were extracted using Agilent Feature Extraction (FE) software version 9.5.3.1 to perform background correction, outlier rejection for each set of replicate features, and calculation of total gene signal for each probe on the array. Arrays within a specific analysis experiment were normalized together according to the variance stabilization published method (Huber et al., 2002) Sample performance on arrays was reported according to QC metrics established by Agilent. Statistical Analysis For statistical hypothesis testing, oneway ANOVA was performed across all samples, and twosample t tests were performed for all pairwise comparisons. Significance was assigned to probes demonstrating False Discovery Rate (FDR)

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46 correct ed P values < 0.05 as described in the method of Benjamini et al. (Benjamini Y, 1995) For the miRNA PCR analyses, snU6 was used for miRNA normalization. Results Measurement of RNA I ntegrity P rior to A nalysis The presence of intact ribosomal RNA across the eighteen squamous cell carcinomas tongue cancers and five normal tongue tissues was examined for quality control measures prior to microarray and qPCR analysis (Figure 31). Because samples A5 and B7 were shown to contain nonintact ribosomal RNA they were excluded from the analysis. Also, sample B11 was discovered to have insufficient amount for further testing and was therefore also excluded from the analysis. Therefore, we analyzed sev enteen squamous cell carcinomas derived from the tongue and three normal tongue tissues in the human tumors study. miRNAs are Significantly D ifferentially E xpressed in OSCC T issues These twenty samples underwent miRNA expression array analysis by Asuragen Services (Austin, TX) For this study, only the mature and guide miRNA strands were examined and miRNA star (sense strands) were excluded. Of the 866 defined miRNAs screened, 110 miRNAs were found to be differentially expressed between the oral cancer ti ssues and normal tissues which exhibited p values less than 0.05, seen above the inferior red horizontal line (Figure 32). The six miRNAs, identified by an arrow were chosen for experimental validation due to their statistically significant level of diff erential expression. Differentially E xpressed miRNAs in the OSCC T issues The aberrant expression of these miRNAs observed to be statistically significant prompted us to first examine those that were calculated to have p values less than 0.01.

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47 Only 67 were found to be differentially expressed significantly (p<0.01) between the oral cancer and normal groups (Figure 33). The degree of upregulated miRNA expression in the cancer tissues was 1.39 to 9.87 fold, while the downregulated miRNA expression ranged from 1.76 to 15.79 fold. Unsupervised H ierarchical C luster ing of T umor T issues Scatter plots were used to compare samples on an X Y correlation. The degree of linear relationships between the twenty samples in an unsupervised heatmap and the result of clu stering applied to significantly (p<0.05) expressed miRNAs only (Figure 34). The dendrograms of rows and columns are similarly obtained by clustering of the samples. The Pearsons correlation ranged from 0.7 to 1.0. The diagonal line represents each respective sample compared to itself thus having a value of 1.0, a perfect correlation with itself as expected The closer to 1, the closer to a perfect positive linear relationship between the samples. The samples retrospectively were identified by their T umor Node Metastasis (TNM) pathological staging at the time of surgery. The analysis demonstrated there to be strong correlations (0.9 and above). For example, the 10 samples at the bottom of the heatmap are shown to correlate well with each other and al so further subcategorize into a smaller stronger correlative box which include the 6 bottom samples. In three of these six samples the TNM pathological staging scores were 4 while the other three had either a 2 or no classification, which is indicated as NONE. Furthermore, it appears as though samples exhibiting low TNM pathological staging scores, samples 710 labeled from the top, or 1, 2, 2 and NONE, also correlate with each other These examples are perhaps as a result of their similarity in TNM pat hological staging. A larger sample size and statistical

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48 analysis may be required to confirm the degree of pathological staging and miRNA expression between tumor samples. qPCR V alidation of S ix miRNAs from the Human Tumor M icroarray U sing T wo A nalyses Hav ing demonstrated that 110 miRNAs were shown to be statistically over and underexpressed in oral cancer from the microarray, we next wanted to choose the three most upand downregulated miRNAs to validate via qPCR. Of the most abundantly differentially e xpressed miRNAs between both groups, miR 424, miR 135b, miR 15b, miR375, miR 494, and miR 486 5p were chosen to validate. The range for the overexpressed miRNAs in oral cancer tissues from the microarray was 6.43 to 9.87 fold, while the range for the mos t downregulated miRNAs was 5.11 to 15.79 fold (Table 32). Two of the commonly used analyses for qPCR quantitation were performed in order to experimentally validate these differentially expressed miRNAs from the microarray (Table 32). The two analyses differ in the way that ddCT was calculated. Analysis 1 was carried out by selecting the lowest dCT from each group and using that value to calculate ddCT. However, in Analysis 2, the dCT average of the normal samples was used to normalize the expression of these miRNAs in the tumor samples. In both analyses, the ratio of the average relative expression across the tumor and control groups was used in order to calculate fold change. Depending on the method of analysis, the potential significance of a tar get may vary. For example, in analysis 1 four of the six targets were found to be statistically significant between the cancer and control groups. However, in analysis 2, only two of the six targets had p values that were significant.

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49 Floor of the Mouth Orthotopic CAL 27 Xenograft Mouse Model System To confirm the use of a representative oral cancer cell line for downstream mechanistic approaches for miRNA studies, we analyzed and created the first reported CAL 27 floor of mouth tumor bearing mouse model (Figure 3 5). The murine xenograft tumor was generated using the CAL 27 cell line. H&E stained sections of the harvested tissue confirmed the presence of the murine CAL 27 xenograft tumor (Figure 36). This same model system was used to confirm the Dic er staining pattern observed in OSCC s (Figure 1 3B). Fold Change Comparisons Be tween the Microarray of the Human Tumors and CAL 27 Tumors Once our initial observation that miRNAs are similarly differentially expressed between the Agilent Microarray and q PCR validation of the six targets, we carried out an analysis of the miRNA expression of these six targets between the miRNA microarrays of the human tumors and CAL 27 tumors (Table 33). The miRNAs analyzed between the different microarray platforms, Agi lent Human miRNA Microarrays Rel12.0 for the human tumors and Exiqons miRCURY LNA microRNA Array v.11.0. for the CAL 27 tumors, did coincide with the direction of upor downregulation in the tumors compared to the normal tongues. As expected, however, there were differences in the degree of relative over or under expression of these miRNAs between the arrays. With respect to the three miRNAs that were upregulated, miR 424, miR 135b and miR 15b, there was up to an approximate threefold difference in th e level of expression between the arrays. In the case of the downregulated miRNAs, there was an approximate four fold discrepancy in the fold change between the microarrays specific to miR 4865p. Although relatively modest, these differences can

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50 be due to the different technologies employed by both arrays, including the statistical approach to formulating fold change, hybridization temperatures utilized, and the differing procedures of hybridization or washing. In addition, the difference can also be due to the differences in probe designs. Moreover, given the relatively small size of the samples that were analyzed by the two microarrays, a single outlier could potentially skew the analysis. Potentially a larger study may be required to further validat e these results with greater confidence. qPCR V alidation of the Tumor S tudy vs in vivo and in vitro CAL 27 C ells The results found from the above sections were then compared to the CAL 27 in vivo mouse model system and CAL 27 cell lines (Table 34). Bec ause analysis 1 was used to generate the fold changes observed between the respective targets in the CAL 27 in vivo mouse model system and CAL 27 cell lines, the analysis 1 fold changes from (Table 32) were illustrated here in column three. Real time PCR analysis of the most significantly up regulated miRNAs from the tumor study demonstrate that the degree and direction of relative expression can vary in both the in vivo and in vitro CAL 27 cells. Furthermore, with respect to this finding, the expression of the three up regulated miRNAs from the tumor study do not correlate well with the CAL 27 cell lines and the CAL 27 in vivo mouse model system. Conversely, the average expression of the downregulated miRNAs in the human tumor cohort compared to the CA L 27 tumors and CAL 27 cell lines were comparable in the direction of relative expression, further substantiating the use of these model systems in the characterization of one or all of these downregulated miRNAs. All analyses were normalized using the t hree normal human tongue tissues.

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51 Discussion Presently, a limited number of studies have attempted to investigate the importance of miRNAs in oral cancer in spite of its essential regulatory mechanisms in animals. Therefore, this study explored which m iRNAs are extremely upand downmodulated in cancer compared to controls so in the future we can understand the mechanistic roles these miRNAs play in the progression of oral cancer. The long term goals will be to confirm these interactions in the in vit ro and in vivo environments. It is important to note that a limitation of our study was the use of a small number of control tissues used for analysis. Although we found differentially expressed miRNAs that were statistically significant a more comprehe nsive study will be needed that will include testing a greater number of controls to more conclusively ascertain the importance and relevance of these miRNAs. Many of the statistically significant miRNAs that were differentially expressed in this report have also been collectively observed in other published studies, validating the results. However, the emergence of previously unidentified miRNAs in oral cancer has been elucidated and important targets can potentially be discerned from our findings. For example, to the best of our knowledge miRNAs such as miRs 424, 135b, 15b, 4865p, 4905p, 318, 513b, and 301 to name a few have not been detailed in previous reports. Conversely, however, miR 21 whose expression was found to be 5.25 fold higher in the oral cancer tumors compared to the controls from this report, has also been widely published to be overexpressed in many cancers and related to tumor progression. These include colorectal neoplasias (Link et al., 2010) pancreatic ductal adenocarcinoma (du Rieu et al., 2010; Link et al., 2010) osteosarcomas (Ziyan et al., 2010) lung cancer (Roa et al., 2010) gastric cancer (Guo et al., 2009; Zhang et al.,

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52 200 8) breast cancer (Mei et al., 2010) esophageal squamous cell carcinoma and esophageal adenocarcinoma (Mathe et al., 2009) cervical cancer (Yao et al., 2009) and even oral cancers (Avissar et al., 2009a; Chang et al., 2008; Li et al., 2009; Tran et al., 2007; Wong et al., 2008) to name a few. miR 21 has been shown to regulate cell growth cytochrome c release, and apoptosis in oral cancer cells (Chang et al., 2008) Furthermore, it has been indicated to serve as an apoptosis inhibitor contributing to the poor prognosis of squamous cell carcinomas derived from the tongue. In this same report, miR 21 was reversely correlated with the expression of TPM1 and PTEN, two tumor suppressing genes (Li et al., 2009) Furthermore, the mean fold change of miR 21 was 17.73 fold in early stage tongue squamous cell carcinomas, and 39.51 fold in advanced stage tongue squamous cell carcinomas, both of which were compared to matched normal tongue samples. Taken together, the literature supports miR 21, a well recognized oncogenic miRNA, to be a useful biomarker for a variety of malignant tumors. Similarly, our data on the overexpression of miR 155 corroborate studies profiling oral cancer tissues and cell lines (Chang et al., 2008; Wong et al., 2008) In addition, miR155 has been overexpressed in its levels in breast cancer (Jiang et al., 2010; Kong et al., 2010) as well as specific subtypes of acute myelogenous leukemia (Cammarata et al., 2010) Interestingly, however, one report demonstrates the downregulation of this miRNA in an oral cancer Syrian hamster model system (Yu et al., 2009) Thus, it is imp ortant to identify and establish an animal model system that parallels the expression of the miRNAs of interest found in human tumors. For example, the relative expression and significance of miR 375 across the two model systems, CAL 27 cultured cells and

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53 the CAL 27 xenograft mouse model, correlate well to that observed in the human study (Table 34). The importance of miR 375 downregulation is not only isolated to oral cancer, but also observed in gastric cancer (Ding et al., 2010) and some hepatocellular carcinomas (Ladeiro et al., 2008) In gastric cancer the overexpression of 143 3zeta, a potent antiapoptotic gene, was validated to be as result of the underexpression of miR 375 (Ding et al., 2010) Similarly, the repression of miR 375 directly correlated with activation of catenin, a major oncogene in hepatocellular carcinoma (Ladeiro et al., 2008) Furthermore, miR 375 was underexpressed 21.88 fold in oral and laryngeal cancers compared to normal tissues (Avissar et al., 2009a) Compared to tumors arising from the oral cavity, the expression of miR 375 was found to be increased in pharyngeal and laryngeal tumors (Avissar et al., 2009b) The finding is consistent with miRNA signatures being tumor and tumor subtype specific (Lu et al., 2005) In another report, miR 375 was downregulated in head and neck tumors, of which the predominant subsite was derived from the oral cavity, approximately 32 fold. Yet, validation of miR 375 decreasing cell proliferation and clonogenicity was carried out in head and neck cancer cell lines as opposed to oral cancer cell lines (Hui et al., 2010) Therefore, representative model systems are critical to understanding the biological roles of miRNAs in oral cancer. The utility of establishing representative model systems for the study of a disease becomes immeasurable. The molecular analysis of optimized animal model systems in cancer will help pave the way to define the effects of gene transcription and pr otein express ion on invasion and metastasis (Henson et al., 2007) The downregulated miRNAs validated from the human study were also confirmed in the direction of

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54 regulation in both CAL 27 cell lines and the CAL 2 7 xenograft mouse model system, suggesting the benefit of the use of both or either of these in the elucidation of specific miRNA/mRNA interactions. After characterizing important miRNAs and the roles they play on subsequent pathways in CAL 27 tissue cult ure cells, the use of the CAL 27 mouse model system can be potentially employed to study these pathways in an in vivo host. Furthermore, downstream therapeutics may also be administered to such a system by perhaps silencing the expression of important upregulated miRNAs or artificially expressing miRNAs that are silenced in oral cancer. In this way, the CAL 27 mouse model system can be used to help facilitate the studies of miRNA expression as biomarkers for oral cancer. In conclusion, based on the sampl es analyzed, our present study demonstrated a plethora of new and previously identified miRNAs found to be important in oral cancer consistent with other reports. Moreover, the use of the oral cancer model system has been established and defined to help c haracterize the effects of some miRNAs. Further investigation of the miRNA biology in oral cancer using the information provided in this report, may help identify biomarkers to OSCC s.

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55 Table 31. Demographic and clinical information of the 17 OSCC samples used for analysis Moffitt accession # Year First Seen at Moffitt Histology/Behavior Primary Site Vital Status Age at diagnosis Current Age Survival ( d ays) TNM, Clin, Stage Group TNM, Path, Stage Group Distant Metastasis Site 661 1995 Invasive SCC with no nodal involvement Base of tongue Alive 68 82 5220 3 2 N 1814 1997 Infiltrating SCC with no nodal involvement Right lateral tongue Dead 51 2400 1 1 N 577 1994 Invasive SCC with no nodal involvement Left lateral tongue Dead 62 720 1 2 N 426 1994 Exten sively invasive keratinizing SCC with no nodal involvement Tongue/Oropharynx Dead 63 420 4 4 N 470 1995 Invasive SCC with no nodal involvement Base of tongue/tonsil Dead 56 900 4 N N 1058 1996 MD invasive keratinizing SCC Tongue Dead 34 450 4 4 N 1117 1996 Infiltrating SCC with no nodal involvement Tongue Dead 61 360 3 4 N 1231 1996 PD SCC with no nodal involvement Base of tongue Dead 57 2280 3 N N 1284 1996 PD infiltrating SCC with no nodal involvement Base of tongue Dead 68 960 4 4 N 13 58 1996 MD extensively invasive keratinizing SCC with no nodal involvement Tongue Dead 69 3720 3 2 N 1373 1996 PD invasive keratinizing SCC with no nodal involvement Tongue Dead 41 630 4 4 N 1599 1997 WD SCC with no nodal involvement Ventral tongue D ead 81 3480 2 2 N 1715 1997 SCC with no nodal involvement Base of tongue Dead 60 450 3 N N 3117 1998 MD invasive and ulcerative SCC with no nodal involvement Tongue Dead 61 300 3 4A N 3019 1999 Large cell non keratinizing SCC with no nodal involve ment Base of tongue Dead 67 2580 3 4A N 4182 2000 PD invasive SCC with no nodal involvement Tongue Dead 45 600 N 2 N 3982 2000 MD SCC with no nodal involvement Ventral tongue Alive 65 74 2250 4A 4A N Clin= Clinical; Path= Pathologic; SCC= Squamous Cell Carcinoma ; N= None; MD= Moderately Differentiated; WD= Well Differentiated; PD= Poorly Differentiated

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56 A B Figure 31. RNA collected from eighteen OSCCs derived from the tongue and five normal tongue tissues were detected using an Agilent bioanalyz er. L, RNA ladder showing sizes of the nucleotides on the left. Representative lanes 112 in part A) and 111 in part B) showing sizes of mRNA species identified using an Agilent bioloanalyzer electrophorogram.

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57 Figure 32. Differential expression of m iRNAs based on the microarray analyses Volcano plot display of miRNA expression between the OSCC tumors versus normal tongues. Individual spots represent a particular miRNA and larger spots denote a higher percentage of the specific miRNA present. The c ircles were colored according to the average expression of the probe across the two groups. Spots superior to the most inferior red line across the plot were calculated to have p values less than 0.05, whilst spots superior to the most superior red line across the plot were calculated to have p values less than 0.01. Spots furthest away from the 0 x axis in a horizontal direction are miRNAs that were most differentially expressed between both groups. Those that are positive to the 0 point were more highl y expressed in the tumors versus normal tongues and those that are negative to the 0 point were more highly expressed in the normal tongues compared to the tumors. Six selected miRNAs for experimental validation are identified by arrows.

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58 Figure 33. Va riable expression patterns of the 68 miRNAs exhibiting p values of less than 0.01 from the microarray. The results represent the mean expression in Log2 difference between the oral cancer tumor and control groups. The miRNAs exhibiting a positive Log2 ex pression difference were upregulated in the tumors compared to the controls, whereas those having a negative Log2 expression difference were upregulated in the controls compared to the tumors

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59 Figure 34. Heatmap representation of the Pearsons correlation coefficients clustered using an unsupervised hierarchical clustering method. The plot is symmetrical across the diagonal. Each box represents a correlation value between a pair of samples. Samples having high correlation values, cluster together whi ch is indicated by the dendr o gram length and color of the box

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60 Table 32 qPCR validation from the human tumor microarray using two analyses qPCR Microarray Analysis 1 Analysis 2 miRNA FC p value FC p value F C p value hsa miR 424 2.47 2.59E 2 2.23 9.70E 2 9.87 4 93E 6 hsa miR 135b 1.63 3.28E 1 1.79 4.00E 1 8.27 2.95E 3 hsa miR 15b 1.60 4.49E 2 1.69 8.79E 2 6.43 2.36E 5 hsa miR 375 0.07 1.57E 2 0.23 1.73E 2 0.06 7.31E 3 hsa miR 494 0.90 7.18E 1 1.01 9.83E 1 0.18 2.63E 4 hsa miR 48 6 5p 0.25 4.05E 2 0.31 5.30E 3 0.20 3.20E 3 FC=Fold Changes; *Values are significant

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61 Figure 3 5. Picture of a NOD.CB17 Prkdcscid mouse from Jac kson L aboratory, Bar Harbor, ME. B). The circle identifies the CAL 27 xenograft tumorous mass at the f loor of the mouth readily visible extraorally 16 days post injection. A B

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62 Figure 36. The tumor formation 16 days post injection of CAL 27 cells into the floor of the mouth both clinically and microscopically The (H&E) section is seen under 100X magnific ation just inferior to the base of the tongue.

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63 Table 33 Fold change comparisons between the microarrays of the h uman t umors and CAL 27 tumors Human Tumors vs Normal Tongues CAL 27 Tumors vs Normal Tongues miRNA FC FC hsa miR 424 9.87 3.15 hsa miR 135b 8.27 5.94 hsa miR 15b 6.43 5.60 hsa miR 375 0.20 0.27 hsa miR 494 0.18 0.28 hsa miR 486 5p 0.06 0.26 FC=Fold Changes.

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64 Table 34 qPCR validation of the tumor study vs in vivo and in vitro CAL 27 Cells Microarrays qPCR Human Tumors Human Tu mors CAL 27 Tumors CAL 27 Cell Lines miRNA FC FC FC F C hsa miR 424 9.87 2.47 0.33 0.34 hsa miR 135b 8.27 1.63 1.97 0.76 hsa miR 15b 6.43 1.60 0.95 2.12 hsa miR 375 0.06 0.07 0.10 0.02 hsa miR 494 0.18 0.90 0.55 0.10 hsa miR 486 5p 0.20 0.25 0.0 8 0.01 FC=Fold Changes.

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65 CHAPTER 4 MAJOR micro RNA SIGNATURES IN HUMAN SALIVA Introduction Several reports have published on the use of isolated RNAs, including miRNAs, from saliva as diagnostic biomarkers. However there is little information on the RNA quality and yield; hence emphasizing the ambiguity of the published findings (Hanson et al., 2009; Michael et al., 2009; Palanisamy et al., 2010; Park et al., 2009) Furthermore, in some instances a preamplificati on step, a reaction capable of amplifying miRNA molecules ~1 million fold, was required for the successful detection of miRNAs in saliva (Park et al., 2009) Preamplification of samples exhibiting a low copy number can yield increased variations between the same sample and unacceptable error bars due to the effects of Poissons Law (Bustin, 2000) Therefore, there is a need to improve upon the isolation of high quality RNA, including miRNAs from saliva without the need for preamplification. This in turn will help identify discriminatory RNA biomarkers with more abundancy and perhaps characterize previously undetected miRNAs in a low cost screening assay for diseases In this way, the investigators seek to help set the foundation to potentially establish saliva as a diagnostic, minimally invasive, and easily accessible medium in order to determine between a cancerous and healthy state in the oral cavity by the introduction of novel methods. Materials and Methods Donors Saliva samples were collected from healthy volunteers at the University of Florida College of Dentistry in consensus with a protocol approved by the UF Institutional Revi ew Board. The mean age of the donors was 30 years (range 20 49). The donors

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66 were negative for a history of immune deficiencies including HIV, autoimmune disorder, hepatitis, and malignancy. Saliva Collection Saliva was obtained as previously described (Navazesh, 1993) with several modifications. First, the stringency of the collection protocol was increased. Specifically, the donors were not allowed to eat two hours prior to collection, after having underg one normal oral hygiene. Second, donors were asked to cease drinking water at least 1 hour prior to collection. Whole saliva samples from each donor were collected from 9am to 10am each day and were preserved with the Oragene RNA Self Collection Kit, according to the manufacturers instructions ( DNA Genotek Inc ., Kanata, Ontario, Canada) Immediately upon preservation, the samples were placed on ice for subsequent RNA isolation. Salivary RNA Extraction Tot al RNA was extracted using a modified protocol combining the Oragene RNA Self Collection Kit ( DNA Genotek Inc ., Kanata, Ontario, Canada) and the mirVanaTM miRNA isolation kit (Ambion/Applied Biosystems, Austin, TX ) In brief, 600 L of the whole saliva m ixture (300 L of whole saliva and 300 L of Oragene RNA) were used for salivary RNA extraction. Saliva mixtures were incubated for 1 hour at 50C then heated at 90C for 15 minutes and allowed to cool to room temperature. Afterwards, 48 L of the Orag ene RNA Neutralizer solution was added, the s amples were mixed, incubated on ice for 10 minutes and then centrifuged at 10,000 x g for 3 minutes at room temperature. The supernatant was collected without disturbing the pellet after which the mirVanaTM miRNA isolation kit (Ambion/Applied Biosystems, Austin, TX ) was used to complete the isolation of total RNA with several modifications to the

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67 manufacturers protocol. First, f or the lysis step ; the addition of the Lysis/Binding buffer and the m iRNA Homoge nate Additive was bypassed and instead an equal amount of Acid Phenol: Chloroform was added directly to the collected supernatant Second, only 50 L of 95C Elution Solution was used to elute RNA. RNA was quantitated using a NanoDrop ND1000 spectrophot ometer (Thermo Scientific, Wilmington, DE). T he Agilent 2100 Bioanalyzer ( Santa Clara, CA ) from the Interdisciplinary Center for Biotechnology Research at the University of Florida was used to d etect the size distribution of total RNA as well as determin e the quality of the RNA. Real Time PCR For 18S rRNA and GAPDH mRNA quantitation, total RNA was reversetranscribed using the High Capacity cDNA Reverse Transcription kit (Applied Biosystems, Foster City, CA). For snU6 RNA and let 7b miRNA quantitation, total RNA was reversetranscribed using TaqMan specific RT primers and the TaqMan microRNA Reverse Transcription Kit (Applied Biosystems). Afterward, quantitative real time PCR was performed in an Applied Biosystems StepOne Real Time PCR machine using p redesigned TaqMan gene/miRNA specific assays for 18S, GAPDH, snU6, and let 7b (Applied Biosystems) combined with TaqMan Fast Universal PCR Master Mix (Applied Biosystems), according to the manufacturers instructions. For 16S rRNA quantitation, RNA was diluted down to 30ng/uL and reverse transcribed by iScript cDNA synthesis kit (Bio Rad Laboratories, Hercules, CA) with the specific antisense 16S RT2 primer 5 ACC CAA CAT CTC ACG ACA CGA G 3 by following the procedures recommended by the manufacturer. Real time PCR was carried out by the described methods (Ahn et al., 2005) with the use of the following primers : 16S RT 1 5 CTT ACC AGG TCT TGA CAT CCC G 3 and 16S RT 2 5 ACC

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68 CAA CAT CTC ACG ACA CGA G 3, which generated PCR products that were approximately 100bp in length. miRNA Array Analyses All RNA samples were adjusted to a concentration of 166.67 ng/L with nucleasefree water (Ambion; Foster City, CA). Reverse transcription and qPCR reactions were compl eted at the Interdisciplinary Center for Biotechnology Research at the University of Florida. Reverse transcription for each sample were assembled with Megaplex RT Primers Human Pool A (Applied Biosystems) and Megaplex RT Primers Human Pool B (Applied B iosystems). In a 0.2 mL PCR tube, 10X Megaplex RT Primers (0.8 L), 100mM dNTPS with dTTP (0.2 L), 50 U/ L MultiScribe Reverse Transcriptase (1.5 L), 10X RT Buffer (0.8 L), 25 mM MgCl 2 (0.9 L) 20 U/ L RNase Inhibitor (0.1 L) and Nucleasefree wa ter (0.2 L) from the TaqManMicroRNA Reverse Transcription Kit (Applied Biosystems) were mixed with 500 ng of total RNA (3 L) for a total reaction volume of 7.5 L. The sample was incubated at 40 cycles of 16C for 2 minutes, 42C for 1 minute and 50C for 1 second followed by 1 cycle of 85C for 5 minutes in a DNA Engine Peltier thermal cycler (BioRad). Amplifi cation mix was assembled by combining 2X TaqMan Universal PCR Master Mix No AmpErase UNG (450 L), Megaplex RT product (6 L) and nucleasefree water (444 L) in a 1.5 mL microcentrifuge tube. The tube was inverted 6 times to mix and then centrifuged briefly. 100 L of the amplification mix was added to each of the 8 wells on a TaqMan Low Density Array Card (TLDA) Human miRNA Panel (Applied Biosystems) The TLDA was placed in the Sorvall/Heraeus custom bucket in a Sorvall Legend T centrifuge (Therm o Fisher Scientific), and centrifuged for 2 consecutive 1minute spins at 1200 RPM. The TLDA card was sealed in a microfluidic

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69 card sealer and the sample loading ports were removed. The TLDA was incubated at 50C for 2 minutes, 94.5C for 10 minutes and 40 cycles of 97C for 30 seconds, 59.7C 1 minute in a 7900HT FAST Real Time PCR System using Sequence Detection Systems Software version 2.3. Statistical Analysis Comparisons between inter operator variability were performed using linear regression analysis A value of P < 0.05 was considered statistically significant. For the miRNA array analyses, snU6 and RNU48 were used for miRNA normalization. Raw data from the miRNA array analyses were normalized by subtracting the target cycle threshold (CT) values from the mean of the snU6 and RNU48 CT values. Results RNA P urification from H uman S aliva In order for salivary RNA to be successfully utilized as a reliable biomarker, a protocol needs to be established that can produce high yields of good quality RNA for subsequent downstream applications and/or analyses. Recently, several studies have described the isolation and purification of RNA from human saliva for the purpose of identifying RNA biomarkers (Hanson et al., 20 09; Michael et al., 2009; Park et al., 2009; Zubakov et al., 2008) Interestingly however, the description of the total yield and purity of RNA obtained from these studies were never clearly reported. Testing of one of the protocols (Park et al., 2009) the RNA isolation protocol demonstrated low RNA yield s and poor RNA quality. This finding confirmed the need for a better method of RNA isolation from saliva. As a result, w e tested a protocol that would enable us to isolate consistently higher yields of good quality RNA from saliva for subsequent uses in downstream applications. Our protocol essentially consisted of combining and

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70 modifying the use of two commercially available kits, the Oragene RNA saliva collection kit and the mirVana miRNA Isolation kit. For details regarding our protocol, please see the Materials and Methods section. T he ability of the Oragene RNA solution in preserving and stabilizing RNA collected from saliva was examined from three different donors over a 48 hour span. In brief, fresh collected whole saliva were mixed with Oragene RNA solution and aliquots were used for RNA is olation immediately or after they were stored at room temperature for 48 hours The RNA yield and concentration remained fairly constant between matched samples from each donor when stored for 48 hours (Figure 41). The V ariability of T otal RNA as a R esult of the B acterial RNA C ontribution in S aliva Total yield of RNA per m L of human whole saliva was calculated and compared for samples from four donors collected each day over a three day period (Figure 42 A). This was the first example showing some consistency of total RNA yield from an individual donor There was a cons istent trend that donors produce either high (donor 1), medium (donors 2 and 4), or low (donor 3) level yields of total RNA over the course of just three days (Figure 4 2A) Having shown that the total RNA yield varied between the respective donor during this three day period, as well as between different donors we next sought to determine if the differences observed were the result of bacterial RNA contribution across the four samples (Figure 42B). The level of 16S rRNA copy number per sample (Figure 42B, log scale) appeared to closely parallel the trend of varied total RNA daily yields from each individual donor (Figure 42A, linear scale). The results thus suggested that the variation in total RNA yield (Figure 42A) might be reflecting difference i n the levels of oral bacteria in individual donors. To substantiate

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71 this, the potential variations in commonly used endogenous mRNA and miRNA normalizing controls were measured. Real time PCR analysis of three commonly used RNA controls, snU6 small RNA 18S rRNA and GAPDH mRNA in the four donors over the course of three days demonstrated that the relative levels of expression for each respective target were fairly stable (Figure 4 2C). Minor differences of only 0.881.47 PCR cycles between each donor for snU6 and GAPDH, and 1.401.80 PCR cycles between each donor for 18S rRNA were noted. According to a recent report (Michael et al., 2009) let 7b was amongst the highest expressed miRNAs Th e relative expression of let 7b miRNA with a mean CT range from the donors between 26.50 29.1 indicates up to approximate 7 fol d difference in the expression of let 7b between the donors (Figure 4 2D) High RNA Y ield in S aliva of H ealthy D onors Parallel comparison of the present protocol with that of an early report in salivary RNA purificatio n (Park et al., 2009) demonstrated that the present protocol generated substantially higher yields of better quality RNA. More specifically, from 200 L of whole saliva obtained from four different donors, our pr otocol yielded a range of 0.84 25. 1 g total RNA versus 0.1 8.9 g of total RNA using the previous protocol. Moreover, the OD 260/280 ratios ranged from 1.91 2.13 using our protocol compared to the other protocol which resulted in an OD 260/280 ratio that ranged between 1.25 2.00. Note that the study by Park et al did not report RNA yield or purity as determined by OD 260/280 ratios (Park et al., 2009) Having demonstrat ed that our protocol consistently produced higher yields of better quality RNA; it became the primary method for isolating RNA from saliva for subsequent experiments within this study.

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72 To further establish the use of the present protocol for salivary RNA isolation and purification, twenty randomly selected healthy donors were recruited and RNA samples were analyzed. The total RNA yield was examined and compared between males and females (Figure 4 3 A). No statistical difference was observed between both groups using an unpaired t test Both groups had a mean value of appr oximately 40 g of total RNA per mL of whole saliva. A relatively similar spread was observed in the different quartiles between both groups. There were two donors from each group who exhibited total RNA yields in the upper quartile of all donors and were considered high RNA producers. Similarly, there were two donors from both groups that ranged in the bottom quartile of all donors and were considered low RNA producers. In addition, it is our experience that many donors had relatively consistent levels of salivary RNA when their samples were collected multiple times over weeks or months. In other words, the general trend is that high and low RNA producers remain high and low producer s respectively over time (data not shown) but the number of donors is still relatively small and the time followed for each donor is short Therefore, f urther work is needed to validate this preliminary finding. In order to test the reproducibility of the protocol between two different users, t he variability of the RNA concentration range isolated from twenty samples was examined and plotted (Table 41) after which linear regression analysis was performed (Figure 43 B). The analysis between both operators was found to have a pvalue of less than 0.0001. The purity ratio average was 2.08. The I dentification of P reviously U ndetected S alivary miRNAs To better understand potential differences in the baseline range of miRNA expression between high versus low producers and males versus females, a m iRNA array between both sets of groups was analyzed. Our analysis demonstrated that

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73 RNU48 and snU6 were amongst the best miRNA normalizers for salivary RNA because both showed the least variance in expression acros s the 12 samples tested. There was no statistical difference observed in the expression of miRNAs between males and females, similar to the findings of the total yield of RNA between both groups. Raw data from the miRNA array analyses were normalized by subtracting marker cycle threshold (CT) values from the mean CT values of snU6 and RNU48. Therefore, since there was an approximate 3 cycle difference in CT values between RNU48 and snU6, in the high and low producers, the statistical differences in miRNA expression between the two groups can be skewed and falsely positive (Figure 44). Therefore, the concept of establishing a baseline for high and low producers between future comparisons of multiple groups becomes important. The 25 most abundantly expres sed miRNAs in whole saliva based on the miRNA array analys e s across 12 donors were illustrated and CT means were compared to the other three published salivary miRNA reports (Table 42). Eighteen of the 25 most abundantly expressed miRNAs from this report have been collectively observed from the other published reports (Hanson et al., 2009; Michael et al., 2009) However, note that since the sample collections are different in these reports, direct comparison must be taken with these considerations. Specifically, the current study used whole saliva whereas the Park et al study used both whole and supernatant saliva (Park et al., 2009) t he Hanson et al study used saliva obtained from the buccal mucosa (Hanson et al., 2009) and finally the Michael et al study collected saliva directly from the submandibular gland (Michael et al., 2009) Unlike the study of Park et al (Park et al., 2009) we did not preamplify the miRNAs prior to any downstream analysis Preamplification generally amplifies lowly expressed miRNAs up to 1 million

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74 fold according to the manufacturers information. Of note, despite the lack of preamplification for the current study the mean CT values for miR 223 was 19.91 whereas it was 20.39 in the Park et al study (Park et al., 2009) Although our study focused on the miRNA analysis between six high producers and six low producers as well as six males and six females, a more comprehensive study will be needed that will include testing of a greater number donors to more conclusively ascertain the observed findings. Discussion The Isolation of High Quality and High Yield RNA from Saliva The detection of salivary mRNA and miRNA expression has been previously reported (Hanson et al., 2009; Li et al., 2004a; Li et al., 2004b; Michael et al., 2009; Palanisamy et al., 2010; Park et al., 2006; Park et al., 2007; Park et al., 2009; Zubakov et al., 2008) however little to no information on the quality and yield of RNA collected has been reported until now. For example, the expression of mRNA and miRNA signatures from different bodily fluids, including saliva, were observed by Zu bakov et al (Zubakov et al., 2008) and Hanson et al (Hanson et al., 2009) respectively. However, the RNA purity and total yield collected from saliva samples were not reported. Moreover, in another study the total RNA yield from whole saliva and stimulated glandular saliva was reported, but there was no mention of the quality of RNA that was analyzed (Michael et al., 2009) Similarly, in yet another report demonstrating the differential expression of salivary miRNAs between normal and oral cancer patients the data pertaining to the quality and yield of salivary RNA collected was again omitted (Park et al., 2009) Meanwhile, other s report ed failure in isolat ing good quality RNA from sali va (Kumar et al., 2006; Zubakov et al., 2008) perhaps because of the

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75 experimental methodologies used. Therefore, this study explored a protocol that would be able to collect RNA from human saliva for subsequent m iRNA analyses. In the present study, relatively high yields of high quality RNA were isolated from the saliva of 20 different donors ( Table 41 ). The RNA yield from 1 mL of saliva across twenty donors ranged from 1.6929.6 g, with OD ratios between 1.922.14 To the best of our knowledge, this is the first report detailing the stabilization of RNA collected from saliva. Testing how long RNA is stable using the protocol proves important in feasibly identifying the collection to the processing time in order to employ downstream diagnostics for future basic and translational studies. We found that RNA yield and concentration remain stable at room temperature in each of the three donors over the course of two days. Alterations of miRNA expression between the saliva from healthy controls and OSCC patients has been reported (Park et al., 2009) and salivary specific miRNA signatures have been examined from forensically relevant biol ogical fluids (Hanson et al., 2009) However, until now, nothing has been reported as to the innate total RNA and miRNA variations in the same individuals over a series of days. In order for miRNAs to be considered diagnostic markers in saliva, specific miRNA species should have minimal differences in expression in any one individual over multiple days with the assumption that there is no change in status of these individuals during this period. Therefore, this study not only explored the differences in the total RNA yield from donors over a three day period, but also sought to examine the potential differences in expression between commonly used mRNA and miRNA endogenous controls. Though total RNA from each donor varied on any given day, perhaps as a result of the bacterial

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76 contribution of RNA, the abundance of th e three mammalian normalizers (snU6 small RNA, 18S rRNA, GAPDH mRNA) examined remained relatively stable. Together, the above findings demonstrated that based on the donors analyzed, the bacterial composition in saliva can vary in one donor across several days and in comparison with other donors as expected. However, the mammalian contribution of salivary RNA remained relatively unchanged. These findings suggest that mammalian RNA harvested from saliva can therefore be used in/for future studies. Recentl y, it was reported that miRNAs isolated from saliva have the clinical utility for oral cancer detection (Park et al., 2009) However this was achieved only after preamplificat ion of the miRNA species. Our analysis using the current protocol across twenty healthy donors revealed similar or more abundant levels of respective miRNAs as well as newly observed miRNAs without the need for preamplification. Unpreamplified cDNA at or above a CT value of 32 contains approximately 1020 copies of the target of interest. Therefore, preamplification of samples containing extremely low copy numbers of target cDNAs, will increase copy number, but can pose problems when aliqouting the cDNA into the preamplification reaction, known as Poissons law (Bustin, 2000) For example, if there was one copy of a particular cDNA per well, and then the sample was aliqouted into three wells, only one of the thr ee wells would get that copy. Thus, this effect increases the variations across the samples yielding error bars to an unacceptable size. For this reason, preamplification does not help if the CT values of the unamplified cDNAs were at or above 32, whic h could have been the case in the Park et al study (Park et al., 2009) Also, in the Park et al study (Park et al., 2009) small differences, of one cycle or less, in the median C T values of

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77 certain miRNAs were observed between OSCC patients and healthy controls. The data provided from our study however, suggests that more discriminatory analysis c an potentially be obtained from RNA isolated from saliva between two groups without the need for preamplification. It is important to note that the limitation of our study was the use of a still relatively small number of donors for the microRNA microarray analysis. miRNA Signatures in Human Saliva From the perspective of average CT values alone for the 25 most abundantly expressed miRNAs from this study in comparison to the other published reports, the usefulness and superiority of the current protocol for the isolation of high quality RNA from whole saliva is reaffirmed. Furthermore, the data do coincide with the expression of miRNAs from other reports validating the results, but nearly one third of the miRNAs ( Table 4 2 ) are not mentioned from the other studies suggesting an unsurpassed resolution. The ability to discern and identify miRNAs previously undetected in saliva from other reports may prove useful for understanding molecular events that may have been overlooked in the past. Recently, it was r eported that in serum miRNAs can be detected in undegraded forms within structures termed exosomes, which are membrane bound secretory granules containing certain proteins, mRNA, and miRNAs (Skog et al., 2008) Salivary miRNAs and mRNAs have also been isolated from exosomes found within the saliva (Michael et al., 2009; Palanisamy et al., 2010) Not only was the structural and mRNA transcriptome of salivary exosomes char acterized, but the results demonstrated that salivary exosomes could regulate gene expression in oral keratinocytes (Palanisamy et al., 2010) Taken together, the above findings demonstrate a possible origin of salivary RNA. However the tissue origin from where the salivary exosomes are possibly shed

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78 along with the biological function of extracellular mi RNAs still requires further elucidation. Current evidence suggests that extracellular miRNAs are released through a ceramidedependent secretory machinery (Kosaka et al., 2010) It is important to note that a limitation to our study was the inability to confirm the source of all the miRNAs collected using our protocol. However, interestingly, three of the top five miRNAs, miR223, miR 16 and miR 24, that were abundantly expressed in the twelve donors based on the miRNA array analysis are involved in hematopoietic stem cell differentiation. Similarly, other reports have found these same miRNAs to also be expressed copiously in saliva (Hanson et al., 2009; Park et al., 2009) In this study, the overabundance of miR223 topped even the expression of snU6, a small nuclear RNA used commonly as a normalizer for m iRNA studies (Table 4 2 ). Moreover, the crucial role of miR 223 during myelopoiesis has been well documented and it is reported to positively regulate granulocytic differentiation (Fazi et al., 2005; Garzon et al., 2007; Nervi et al., 2007) Similarly, miR16 and miR 24 are found to be abundantly expressed in CD34+ cells and are also implicated in controlling erythropoiesis (Bruchova et al., 2007; Wang et al., 2008) Thus we speculate that the abundant expression of these salivary miRNAs observed in our study may be linked to increasing granulocytic and erythroblastic precursors shed into the saliva for reasons yet to be answered. Clearly however, more work is needed to identify the cellular origin to the major miRNA species found within saliva. In conclusion, based on the samples analyzed, our present study demonstrated that high quality and high yields of miRNA transcriptomic information can be isolated from saliva without the need for preamplification. Moreover, previously unidentified

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79 salivary miRNA species were discovered using the current protocol. In addition, this methodology has the capacity to isolate high quality total RNA from saliva stored at room temperat ure two days after the addition of Oragene RNA solution. Furthermore, there appears to be no statistical difference between salivary miRNAs observed between males and females. This work implies that high and low producers of salivary total RNA must be ta ken into consideration prior to studying the differential expression between a healthy and disease state for comparisons. The sheer abundance of major salivary miRNAs signatures (Table 4 2 ), the resolution of miRNAs compared to previous published reports (Table 4 2 ) along with a very significant correlation of the methodology between multiple users (Figure 43B) makes the current protocol a useful resource for employing salivary RNA diagnostics. The results of the present study could potentially identify miRNA signatures exclusive to disease states that will not only lead to early detection, but also help facilitate the identification of novel therapeutic targets.

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80 A B Figure 41. Stabilization of saliva for RNA isolation. Incubation of whole saliva w ith Oragene RNA solution stabilizes total RNA for two days at room temperature. A) Bioanalyzer data demonstrating the total RNA extracted from whole saliva at Day 0 from three different donor s with the total RNA yield per 1 mL of saliva and OD260/OD280 ra tios indicated. B) Bioanalyzer data of total RNA extracted from the same whole saliva samples two days later

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81 A B D Figure 42. The variable expression of total RNA in the saliva from donors is a result of the bacterial contributi on and not fluctuations in mammalian RNA. A) Graphical representation of the total yield of RNA isolated from four different donors over the course of three days. B) The total 16S rRNA copy number calculated from each donor sample using a standard curve. C ) The mean CT values standard e rror of snU6, 18S, and GAPDH plotted for each of the four donors across three days. Data shown were obtained from three replicates. D) The mean CT values of let 7b across four donors over three days. The results represent the mean standard e rror measurements. Data shown were obtained from three replicates. C

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82 A B Figure 43. R elatively high RNA yield collected across the saliva of 20 healthy donors. A) Scatter plot analysis between total RNA collected from 1 mL of whol e saliva from males compared to females. The data shown are divided into quartiles. B) Linear regression analysis of RNA collected from donors between two operators. Data shown were obtained from the same biological starting sample. *p < 0.0001

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83 Table 41. Inter operator variability in the collection of total RNA from the saliva of 20 healthy donors. The purity ratio average was 2.08. Operator 1 Operator 2 Conce ntration (ng/ L) A 260 /A 280 Ratio Concentration (ng/ L) A 260 /A 280 Ratio Donor 1 63.68 2.10 4 8.05 2.08 Donor 2 87.10 2.14 87.84 2.14 Donor 3 111.14 2.12 78.53 2.11 Donor 4 43.43 2.02 70.40 2.10 Donor 5 50.12 2.03 32.31 1.98 Donor 6 169.47 2.16 107.11 2.15 Donor 7 15.71 1.92 10.12 1.97 Donor 8 103.92 2.05 102.13 2.07 Donor 9 74.19 2.13 62.0 7 2.19 Donor 10 32.68 2.03 31.54 2.00 Donor 11 61.73 2.09 41.08 2.09 Donor 12 53.39 2.07 31.56 2.12 Donor 13 106.52 2.10 81.32 2.11 Donor 14 42.93 2.06 35.13 2.01 Donor 15 167.91 2.08 74.37 2.09 Donor 16 71.27 2.06 62.64 2.02 Donor 17 83.19 2.09 48 .33 2.10 Donor 18 177.92 2.14 129.26 2.11 Donor 19 145.71 2.13 80.10 2.16 Donor 20 122.85 2.13 31.54 2.00

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84 Figure 44. The CT values of the normalizers, snU6 and RNU48, between high and low salivary RNA producers.

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85 Table 42 Identification of the 25 most abun dantly expressed miRNAs in saliva by miRNA array analys e s of 12 donor samples and comparison of the mean CT values with that of other published reports. miRNA Current Report Whole Saliva C T Mean (Park et al, 2009) (Hanson et al, 2009) Dried and Buccal Saliva C T Mean (Michael et al, 2010) Parotid Exosomes C T Mean Whole Saliva C T Mean Supernatant Saliva C T Mean hsa miR 223 19.91 + 20.39 + 18.23 + ~26 28 hsa miR 191 25.00 + 23.79 + 21.82 + ~35 37 hsa miR 16 25.35 + 27.23 + 25.53 + ~27 hsa miR 203 25.35 + 26.45 + 26.34 + ~38 40 hsa miR 24 25.54 + 27.12 + 25.48 hsa miR 222 26.76 + 30.22 + 27.20 hsa miR 135a* 26.14 hsa miR 200c 27.46 + 26.45 + 24.45 + ~35 37 hsa miR 484 27.78 + ~30 hsa miR 320 27.78 + 31. 52 + 30.07 hsa miR 106a 28.17 + ~35 37 hsa miR 17 28.21 + ~32.5 + hsa miR 29a 28.26 hsa miR 923 29.50 hsa miR 26a 28.47 + 29.48 + 28.36 hsa miR 19b 28.81 + 30.25 + 28.96 + ~33.5 hsa miR 30c 28.82 + 32.58 + 30.21 hsa miR 760 29.89 + hsa miR 27a 29.00 + 34.51 + 33.50 hsa miR 768 3p 29.24 hsa miR 375 29.17 + 32.45 + 30.11 hsa miR 26b 29.20 + 30.58 + 29.69 + ~29 31 hsa miR 574 3p 29.32 hsa miR 193b 29.35 hsa miR 186 2 9.48 The expression of previously undetected microRNAs and validation of previously identified miRNAs in human whole saliva. The data shown here compares the presence (+) or absence ( ) of the particular miRNAs between other published reports, wi th the mean CT values listed. Average CT mean of snU6= 23.13; Average CT mean of RNU48= 27.08

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86 CHAPTER 5 CONCLUSIONS mi RNA Machinery in OSCCs The literature agrees with the altered expression of certain miRNAs repeatedly witnessed in human cancers, however the underlying regulatory mechanisms still require further elucidation. Therefore, we first examined the components of the RNAi pathway for aberrant expression in OSCCs. Of all the key RNAi machinery components screened, Dicer was overexpressed at the protein level in HNSCCs, and in particular OSCCs. Previous to our study, there were reports of altered Dicer expression in various types of cancer not including OSCCs. Therefore, this study further characterized the expression of Dicer in oral cancer and the regulatory mechanisms governing its levels. We found that the increased Dicer protein expression was due to the aberrant expression of let 7b in HNSCCs. Moreover, introduction of let 7b and small interfering RNAs targeting Dicer into oral cancer cel ls resulted in the significant inhibition of oral cancer proliferation. This work has significantly contributed to the understanding of one of the many roles of let 7 miRNA in oral cancer. Further studies need to be performed in order to possibly deter mine if the expression of Dicer is correlated to the aggressiveness of oral cancer, similar to that seen within breast cancer (Grelier et al., 2009) Fortunately, that can easily be done thanks to the to ngue tumor library created as part of the body of work presented in this di ssertation. miRNA Signatures Specific to Oral Cancer After proving let 7b plays an important mechanistic role in oral cancer cells we next became interested in profiling the expression of all mi RNAs in tumors obtained

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87 from the tongue, the most common locat ion for oral cancers. T hrough collaboration with the Moffitt Cancer Center, we have collected 17 human tongue tumors (4 well differentiated, 1 moderately differentiated, 4 poorly differentiated, and 8 lacking a classification) as well as 3 normal tongue t issues. To the best of our knowledge to date, this will be the most comprehensive human oral cancer study performed from the same site specific origin. This becomes extremely important since the expression of miRNAs are tissue specific. RNA were isolated and submitted for miRNA array analysis. Preliminary analysis of the data showed that there are >60 miRNAs overexpressed and ~15 miRNAs underexpressed in the tumors versus controls in a highly significant manner with p value < 0.01. Currently we are val idating these data using independent methods as w ell as performing bioinformatic analysis on the relative contributions of selected miRNAs The long term goal of the current work is to determine whether these elevated miRNAs are true markers for oral cancer and whether changes in their levels can be used as biomarkers for early detection of oral cancer. Comparisons will be made between an oral cancer in vivo mouse model system, developed here, established cell lines as well as clinical oral cancer isolates from tissue biopsies. This work will provide a full spectrum of miRNA expression in human oral cancers. The relevance of this innovative research to public health is the ability to potentially generate an early and noninvasive oral cancer detection ass ay using established miRNA biomarkers with samples such as oral tissue biopsies or saliva.

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88 Elucidation of S alivary miRNA Biomarkers Through the Introduction of Novel Methods After observing the aberrant expression profiles of a number of miRNAs in oral cancer, we sought to determine if we can possibly utilize these miRNA signatures by developing a noninvasive and clinically accessible screening protocol. Identifying discriminatory human salivary RNA biomarkers reflective of disease in a low cost non inva sive screening assay is an attractive research direction. Recent reports have detected both mRNA and miRNAs in saliva. These prior findings indicate little to no information on the quality and yield of RNA collected. Our improved method is described for the high yield and quality of RNA (Chapter 4). Results demonstrate that previously unidentified miRNAs from saliva have been unveiled. Additionally, the sheer abundance of major salivary miRNA signatures, the resolution of miRNAs compared to previous published reports along with a very significant correlation of the methodology between multiple users makes the current protocol a useful resource for employing salivary RNA diagnostics. The results of the future study may potentially identify miRNA signatures exclusive to cancerous states that will not only lead to early detection, but also help facilitate the identification of novel therapeutic targets from the use of human and established animal model systems. We plan to develop salivary miRNAs as biomark ers for early oral cancer detection by monitoring miRNAs from oral cancer patients before and after effective treatment. Specifically matched saliva samples for the expression of miRNAs before and after tumor resection will be analyzed. Additionally, it is believed that the molecular investigation of head and neck cancer targets requires the utilization and optimization of established animal models in order to help characterize biomarkers in an in vivo model

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89 system. In the current study, we have developed the first human orthotopic floor of the mouth CAL 27 squamous cell carcinoma mouse model and are currently working to establish and optimize a SCC 25 model system as well. Furthermore, these model systems allow the investigators to obtain saliva and tes t whether the aberrant expression of miRNAs seen within the tumor tissues mimic the observed profiles in saliva. Thus far, attempts to collect saliva in the CAL 27 model system have been successful however we are limited in the volume of saliva collected since the mice are unstimulated for salivary secretions. Thus, in the future we plan to study the miRNA profiles of these mice after they are stimulated for salivary secretions, and compare them to the matched tumor tissues. In this way, the investigators seek to help set the foundation to potentially establish saliva as a diagnostic, minimally invasive, and easily accessible medium in order to determine between a cancerous and healthy state in the oral cavity by the introduction of novel methods. Success of the project will lead to incorporating the new screening tool into future community outreach projects. Early diagnosis of oral cancer will benefit patients as well provide large cost savings to the health care system, in the best tradition, a bench t o bedside approach. There is also the prospect that our approach can not only be applied to the early diagnosis of other cancers, but to any disease state that miRNAs play a role in thus contributing substantially to the field of molecular and cellular bi ology. Significant breakthroughs related to analyzing RNAs in saliva, will have major implications for salivary diagnostics in virtually any disease state.

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98 BIOGRAPHICAL SKETCH Rushi Shirish Patel was born in London, England. After a nomadic journey through Africa, Europe, and the Americas; he settled with his family in the small coastal town of Hernando, Florida. He graduated Summa Cum Laude from Lecanto High School in 2000. Rushi entered the highly competitive University of Missouri Kansas City six year baccalaureate/ dental degree program. At this time, Rushis interest in scientific r esearch spawned as he con ducted t able clinic research with e ndosseous i mplants. After completion of his dental s chool curricu lum, Rushi graduated with his Bachelor of Art/Doctor of Dent al Surgery d ual d egree in May of 2006. After Graduation, Rushi matched at Columbia Universitys General Practice Residency in New York, New York. A year in a dynamic clinical environment strengthened Rushis passion for balance, and his passion to recomm it to the realm of research. In August of 2007, Rushi matriculated into the Indisciplinary PhD program in b iomedical sciences at the University of Florida. Over the span of the next three years, Rushi enriched his experience by positioning himself to conquer both the clinical and academic disciplines in d entistry. He was appointed as a courtesy clinical assistant professor in the oral and maxillofacial surgery clinic. In 2008, Rushi joined the Laboratory of Dr. Edward K. L. Chan, where he studied the identification of miRNA signatures in oral cancer from a molecular oncologic and saliva diagnostic approach. In 2009, Rushis academic exploits garnered the prestigious Medical Guild Research Incentive Grant. Meanwhile, his teaching and private practice exp eriences fueled his passion for research. By discovering and correlating specific miRNAs

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99 biomarkers to oral cancers of the tongue, the promising field of molecular and cellular biology may lead to the discovery of new and innovative therapeutic targets.