Group Title: Reproductive Biology and Endocrinology 2007, 5:35
Title: Genomic and proteomic profiling II: Comparative assessment of gene expression profiles in leiomyomas, keloids, and surgically-induced scars
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Title: Genomic and proteomic profiling II: Comparative assessment of gene expression profiles in leiomyomas, keloids, and surgically-induced scars
Series Title: Reproductive Biology and Endocrinology 2007, 5:35
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Pan Q
Liu L
Chegini N
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Reproductive Biology and

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Research

Genomic and proteomic profiling II: Comparative assessment of
gene expression profiles in leiomyomas, keloids, and
surgically-induced scars
Xiaoping Luo1, Qun Pan', Li Liu2 and Nasser Chegini*l


Address: 'Department of Obstetrics and Gynecology, University of Florida, College of Medicine, Gainesville, Florida 32610, USA and
2Interdisciplinary Center for Biotechnology Research, University of Florida, College of Medicine, Gainesville, Florida 32610, USA
Email: Xiaoping Luo xiaoping@obgyn.ufl.edu; Qun Pan panq@obgyn.ufl.edu; Li Liu liliu@biotech.ufl.edu;
Nasser Chegini* cheginin@obgyn.ufl.edu
* Corresponding author


id Central


Published: 24 August 2007
Reproductive Biology and Endocrinology 2007, 5:35 doi:10.1 186/1477-7827-5-35


Received: 15 May 2007
Accepted: 24 August 2007


This article is available from: http://www.rbej.com/content/5/l/35
2007 Luo et al; licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0),
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.



Abstract
Background: Leiomyoma have often been compared to keloids because of their fibrotic
characteristic and higher rate of occurrence among African Americans as compared to other ethnic
groups. To evaluate such a correlation at molecular level this study comparatively analyzed
leiomyomas with keloids, surgical scars and peritoneal adhesions to identify genes that are either
commonly and/or individually distinguish these fibrotic disorders despite differences in the nature
of their development and growth.
Methods: Microarray gene expression profiling and realtime PCR.
Results: The analysis identified 3 to 12% of the genes on the arrays as differentially expressed
among these tissues based on P ranking at greater than or equal to 0.005 followed by 2-fold cutoff
change selection. Of these genes about 400 genes were identified as differentially expressed in
leiomyomas as compared to keloids/incisional scars, and 85 genes as compared to peritoneal
adhesions (greater than or equal to 0.01). Functional analysis indicated that the majority of these
genes serve as regulators of cell growth (cell cycle/apoptosis), tissue turnover, transcription factors
and signal transduction. Of these genes the expression of E2FI, RUNX3, EGR3, TBPIP, ECM-2,
ESM I, THBS I, GAS I, ADAM 17, CST6, FBLN5, and COLI 8A was confirmed in these tissues using
quantitative realtime PCR based on low-density arrays.
Conclusion: the results indicated that the molecular feature of leiomyomas is comparable but may
be under different tissue-specific regulatory control to those of keloids and differ at the levels
rather than tissue-specific expression of selected number of genes functionally regulating cell
growth and apoptosis, inflammation, angiogenesis and tissue turnover.


Background
Leiomyomas are benign uterine tumors with unknown
etiology that originate from transformation of myome-
trial smooth muscle cells and/or connective tissue fibrob-


lasts during the reproductive years. Leiomyomas can
develop in multiple numbers that are individually encap-
sulated by a connective tissue core separating them from
the surrounding normal myometrium and are ovarian


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Reproductive Biology and Endocrinology 2007, 5:35



steroid-dependent for their growth. Although they occur
independent of ethnicity, clinical and epidemiological
studies have indicated that African Americans are at a
higher risk of developing leiomyomas compared to other
ethnic groups [1].

Leiomyomas have also often been compared to keloids
because of a higher rate of occurrence in African Ameri-
cans and their fibrotic characteristics despite differences in
the nature of their development and growth [2]. Keloids
are benign skin lesions that develop spontaneously, or
form from proliferation of dermal cells following tissue
injury resulting in a collagenous and poorly vascularized
structure at later stage of their development [3-6]. Unlike
surgically-induced and hypertrophic scars that are con-
fined to the area of original tissue injury, keloids can
expand beyond the boundaries of their original sites fol-
lowing removal and during healing. Keloids are rather
similar to hypertrophic scars at early stages of develop-
ment, however they become collagenous and poorly vas-
cularized at later stages and tend to occur more frequently
in darker skinned individuals [3,4]. Surgically-induced
injury and/or inflammation also result in peritoneal scar
or adhesions and similar to other incisional scars they are
confined to the area of tissue injury[7]. Peritoneal adhe-
sions also display a considerable histological similarity
with dermal scars; however there is no data to suggest a
higher risk of adhesion formation with ethnicity. Com-
paratively, uterine tissue injury i.e., following myomec-
tomy or cesarean sections, does not cause leiomyomas
formation, but rather results in incisional scar formation
at the site of injury. Furthermore, leiomyomas consist
mainly of smooth muscle cells forming a relatively vas-
curaized tissue, while keloids derive from proliferation of
connective tissue fibroblasts, adopting a myofibroblastic
phenotype at a later stage of wound healing[3,4].

As part of these characteristics previous studies have iden-
tified excess production and deposition of extracellular
matrix, namely collagens in leiomyomas, keloids, hyper-
trophic and surgical scars and peritoneal adhesions [2,7-
10]. Evidence also exists implicating altered production of
several proinflammatory and profibrotic cytokines, pro-
teases and adhesion molecules in pathogenesis and char-
acteristic of these and other fibrotic disorders [11-14].
Large-scale gene expression studies have provided addi-
tional evidence for the expression of a number of differen-
tially expressed genes in leiomyomas [11,15-17], keloids
and hypertrophic scars [15,16] as compared to their
respective normal tissues. Several conventional studies
have demonstrated that the products of some of these
genes regulate various cellular activities implicated in the
outcome of tissue fibrosis at various sites throughout the
body Among these genes, include several growth factors
and cytokines such as TGF-P system, proteases, adhesion


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molecules and extracellular matrix etc. (for review see [7-
17]). Despite these advancements, the biological signifi-
cance of many of these genes in pathophysiology of leio-
myomas and keloids and their relationship to the
outcome of other tissue fibrosis remains to be established.
In addition, there has not been any study that compara-
tively analyzed the molecular profile that distinguishes
leiomyomas from other fibrotic tissues, specifically kel-
oids.

Considering these characteristics we used large-scale gene
expression profiling to evaluate such a correlation at
molecular level by comparatively analyzing leiomyomas
with keloids, surgical scars and peritoneal adhesions to
identify genes that are either commonly and/or individu-
ally distinguish these fibrotic disorders despite differences
in nature of their development and growth. We evaluated
the expression of 12 genes in these tissues representing
several functional categories important to tissue fibrosis
using quantitative realtime PCR based on low-density
arrays.

Methods
All the materials and methods utilized in this study are
identical to our previous studies and those reported in the
accompanying manuscript [11,17]. Prior approval was
obtained from the University of Florida Institutional
Review Board for the experimental protocol of this study,
with patients with scars giving informed consent, while
the study with leiomyomas was expedited and did require
obtaining written informed consent.

Total cellular RNA was isolated from keloid/incisional
scars (N = 4) and subjected to microarray analysis using
human U133A Affymetrix GeneChips as described in the
accompanying manuscript [17]. One patient who had
developed keloid at the site of previous surgical incision
also developed leiomyoma. All the patients with keloids
and one patient with incisional scar were African Ameri-
cans. In addition, we utilized the gene expression data
obtained from our previous study [11] involving leiomy-
omas (N = 3) and peritoneal adhesions (N = 3) using
human U95A GeneChips. These tissues were from Cauca-
sians patients with the exception of one peritoneal adhe-
sion collected from an African American patient. The age
of patients with leiomyomas ranged from 29 to 38 years.
These women were not taking any medication, including
hormonal therapy, for pervious 3 months prior to surgery
and based on their last menstrual period and endometrial
histology was from early-mid secretary phase of the men-
strual cycle. The age of patients with adhesions ranged
from 25 to 46 years and those with keloids and surgical
scars were 26, 32 and 39 years, respectively. All the tissues
with the exception of one keloid matched by their corre-
sponding normal tissues i.e. myometrium, skin and pari-


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etal peritoneum for microarry analysis. All the procedures
for total RNA isolation, amplification, cDNA synthesis,
RNA labeling and hybridization into the GeneChips were
carried out as previously described in detail [11].

Microarray data analysis
The gene expression values obtained from the leiomyo-
mas and matched myometrium (N = 6) using U133A
GeneChips in the accompanying manuscript was utilized
here only for the purpose of comparative analysis. The
gene expression values obtained from all U133A and
U95A GeneChips were independently subjected to global
normalization and transformation, and their coefficient
of variation was calculated for each probe set across the
chips as previously described [11]. The selected gene
expression values were than subjected to supervised learn-
ing including statistical analysis in R programming and
ANOVA with Turkey test and gene ranking at P < 0.005
followed by 2-fold change cutoff] 11]. Functional annota-
tion and molecular pathway analysis was carried out as
described [17].

For combining the data from the U95A and U133A chips
the probes that were absent across all chips were removed
and subjected to t-test to identify differentially expressed
genes. The data set was annotated using Entrez Gene and
full annotation files NetAffy software and probe sets were
consolidated based on Entrez Gene ID and subjected to
microarray.dog.MetaAnalysisTester. The analysis keeps
one probe for each gene with the smallest p-value for up
or down t-test. The probe with smallest p-value for up reg-
ulated genes may be different from probe sets with small-
est p-value for down-regulated genes. When the data from
U95A and U133A was combined if a gene was represented
on one platform, but not on both the missing data was
replaced with NA. The data was subjected to Fisher com-
bine p-values using inverse chi-square method and per-
mutation test to determine new p-value, named
randomized inverse chi-square p-value and to calculate
the traditional inverse chi-square p-value. The false dis-
covery rate was calculated using the inverse chi-square p-
value and the min t-test p-value for each gene.

Quantitative realtime PCR
The same total RNA isolated from these tissues and used
for microarray studies was also subjected to quantitative
realtime PCR using custom-made TaqMan Low Density
Arrays (LDAs) assessing the expression of 12 genes and
the house-keeping gene, GAPDH. Detailed descriptions of
LDA and realtime PCR, including data analysis has been
provided in the accompanied manuscript[ 171.


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Results
Gene expression profiles of leiomyomas, keloids and scars
Utilizing Affymetrix U133A platform we first assessed the
gene expression profile of keloids and incisional scars.
Following supervised and unsupervised assessments of
the gene expression values in each cohort the combined
data set with the gene expression values of leiomyomas
reported in the accompanying manuscript using U133A
arrays [17] only for the purpose of comparative analysis.
The analysis based on supervised and unsupervised assess-
ment and P ranking of P < 0.005, followed by 2-fold cutoff
change selection, resulted in identification of 1124 tran-
scripts (1103 genes) of which 732 genes were over-
expressed and 371 were under-expressed in leiomyomas
as compared to keloids/incisional scars (N = 4). Hierarchi-
cal clustering separated these genes into distinctive groups
with each cohort clustering into the corresponding sub-
group (Fig. 1). A partial list of these differentially
expressed genes with their biological functions is shown
in Tables 1 and 2. The combined gene list presented in
Tables 1 and 2 is different from the list reported in the
accompanying manuscript for leiomyomas[ 17], although
many commonly expressed genes displaying different
expression values could be find in between the tables.

The analysis based on inclusion of leiomyomas as two
independent cohorts (3 A. American and 3 Caucasians)
resulted in identification of a limited number of differen-
tially expressed genes as compared to keloids (N = 2)/inci-
sional scars (N = 2). Because both keloids were from A.
American patients we excluded one of the incisional scar
from a Caucasian patient from the analysis and lowered
the statistical stringency to P < 0.01 which resulted in
identified 424 differentially expressed genes in A. Ameri-
can leiomyomas as compared to keloids/scars. Similar
analysis resulted in identified 393 differentially expressed
genes in Caucasian leiomyomas as compared to keloids/
scars (all from A. Americans). Of these genes 64 and 32
genes, respectively differed by at least 2 fold in leiomyo-
mas of AA and Caucasians, compared to keloids/inci-
sional scars (Table 3).

We also utilized the gene expression values obtained in
our previous microarray studies in leiomyomas[ 11] and
peritoneal adhesions (unpublished results) for compara-
tive analysis. Because these results were generated using
Affymetrix U95A GeneChips, due to cross-platform com-
parability with U133A the combined data from both plat-
forms were subjected to additional analysis as described
in the materials and methods. The analysis based on p <
0.005 and 2-fold change cutoff identified 1801 genes as
over-expressed and 45 under-expressed in leiomyomas as
compared to keloids/incisional scars and peritoneal adhe-
sions (considered as one cohort during analysis). Of
these, 85 genes were differentially expressed in leiomyo-


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Reproductive Biology and Endocrinology 2007, 5:35


Figure I
Cluster analysis of 1124 differentially expressed transcripts in
leiomyomas (N = 6) form African Americans (AALI, AAL2
and AAL3), Caucasians (CLI, CL2, and CL3) and in keloids
(S3 and S4) and incisional scars (SI and S2) identified follow-
ing supervised and unsupervised analysis and p ranking of P <
0.005 followed by 2-fold cutoff change selection (Affymetrix
U I33A). Genes represented by rows were clustered accord-
ing to their similarities in expression patterns for each tissue
identified as A and B. The dendrogram displaying similarity of
gene expression among the cohorts is shown on top of the
image, and relatedness of the arrays is denoted by distance to
the node linking the arrays. The incisional scar (S I) and kel-
oids were from African American patients. The shade of red
and green indicates up- or down-regulation of a given gene
according to the color scheme shown below.


mas as compared to peritoneal adhesions (Fig. 2), how-
ever exclusion of U133A data from the analysis resulted in
identification of a higher number differentially expressed
genes. The gene expression profiles in these tissues were
comparatively analyzed with their corresponding normal
tissues, myometrium, skin and peritoneum, and as
expected they displayed distinct patterns (data not
shown). The analysis confirmed the effect of cross-plat-
form on gene expression profiling when comparing
results of different studies (See Nature Bio-technology,
Sept 2006 for several reviews).


Figure 2
Cluster analysis of 206 differentially expressed genes in leio-
myomas from Caucasians (CLI, CL2, and CL3) and perito-
neal adhesions (AI, A2, A3) using Affymetrix U95 array. The
genes were selected based on supervised and unsupervised
assessment and p ranking at P < 0.01 followed by 2-fold cut-
off change selection. The genes represented by rows were
clustered according to their similarities in expression pat-
terns for each tissue and identified as A and B.


Realtime PCR of gene expression
Gene ontology assessment and division into functional
categories indicated that a majority of the differentially
expressed genes identified in these cohorts serve as regula-
tor of transcription, cell cycle and apoptosis, extracellular
matrix turnover, adhesion molecules, signal transduction
and transcription factors (Tables 1, 2 and 3). Since the
expression of E2F1, RUNX3, EGR3, TBPIP, ECM-2, ESM1,
THBS1, GAS1, ADAM17, CST6, FBLN5, and COL18A1
was evaluated in leiomyomas using LDA-based realtime
PCR as described in the accompanying manuscript [17]
we used the same approach and compared their expres-
sion in keloids, incisional scars and peritoneal adhesions.
The level of expression of these 12 genes displayed signif-
icant variations among these tissues with some overlap-
ping patterns with the microarray results. By setting the
mean expression value of each gene independently as 1 in
leiomyomas compared with their mean expression in kel-
oids/incisional scars (scar) and adhesions, the results


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F_ 7=T---i F_


-----[


ZJ1,
nN~---r
c~cllc~C~N~



























E2F1 RUNX3 EGR3 TBPIP
6-


0 J- -- ---- -- -- -
ECM2 ESM1 THBS1 GAS1

0 LYM
s2.5 Scar



Z 1.5





ADAM17 CST6 FBLN5 COL18A1


Figure 3
The bar graphs show the relative mean expression levels of
12 genes (E2F I, RUNX3, EGR3, TBPIP, ECM-2 ESM I,
THBS I, GASI, ADAM 17, CST6, FBLN5, and COLI8AI) in
leiomyomas (LYM), keloids/incisional scars (Scar) and perito-
neal adhesions (P. Adhesion) using realtime PCR and LDA as
described in materials and methods section. Values on the y-
axis represent an arbitrary unit derived from the mean
expression level of these genes in each tissue with their mean
expression values in leiomyomas set at I independently for
each gene prior to normalization against their expression lev-
els in myometrium form a Caucasian serving as control. The
asterisks indicate statistical difference between the expres-
sion of these genes with arrows pointing the difference
between each group. A probability level of P < 0.05 was con-
sidered significant.


Reproductive Biology and Endocrinology 2007, 5:35



indicated that the expression of E2F1, TBPIP and ESM1
was elevated in leiomyoma as compared to keloids/inci-
sional scars and adhesions (Fig. 3, P < 0.05). In contrast,
the expression of EGR3, ECM2, THBS1, GAS 1 and FBLN5
in scars and RUNX3 and COL18 expression in peritoneal
adhesions was higher as compared to leiomyomas (Fig.
3).

Discussion
Using a large-scale gene expression profiling approach we
compared leiomyomas with keloids, incisional cars and
peritoneal adhesions and found that their molecular envi-
ronments consist of a combination of both tissue-specific
and commonly expressed genes. The tissue-specific gene
expression between leiomyomas and keloids was not
reflected based on the presence/absence of unique genes,
but rather occurred at the level of expression of a selective
number of differentially expressed genes. As such an ele-
vated level of expression of a number of muscle cell-spe-
cific genes in leiomyomas and fibroblast-specific genes in
keloids reflected the specific cellular make up of these tis-
sues. In addition, specific expression of estrogen receptor
(ER) in leiomyomas with limited expression in keloids
and incesional scar tissues re-enforced the importance of
ovarian steroids in leiomyomas growth. Collectively the
results suggest that the molecular environments that gov-
ern the characteristic of these fibrotic tissues, at least at
genomic levels, are relatively similar and involved specific
set of genes represented by 3 to 12% of the genes on the
array. This observation also suggests that differential
expression of a limited number of these genes with
unique biological functions may regulate the processes
that results in establishment and progression of leiomy-
oma, keloids, incisional scars, and possibly other fibrotic
disorders, despite differences in the nature of their devel-
opment and growth.

We recognize that the stage of the menstrual cycle and to
a limited extend the size of leiomyomas, as well as the
period since keloids, incisional scars and peritoneal adhe-
sions were first formed, reflecting the stage of wound heal-
ing, influences the outcome of their gene expression.
Although leiomyomas used in our study were similar in
size and from the same phase of the menstrual cycle, the
stage of keloids and scars tissues was unknown. As such
the study results represent their gene expression at the
time of collection. We also recognize that small sample
size limited our ability to analyze the data based on eth-
nicity, because of more frequent development of leiomy-
omas and keloids in African Americans. However, it is
worth mentioning that comparing leiomyomas with kel-
oids from this ethnic group showed a limited difference in
their gene expression profile, or when compared with lei-
omyomas from Caucasians, suggesting the existence of a
comparable environment in leiomyomas and keloids.


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Reproductive Biology and Endocrinology 2007, 5:35


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Table 2: List of under-expressed in leiomyomas as compared to scar tissues (keloids/incesional scars)

Gene Bank Symbol Fold Change Probability Function


AF004709 MAPK 13 0.06 0.0002 apoptosis
AF010316 PTGES 0.09 0.0003 apoptosis
NM 014430 CIDEB 0.21 0.0014 apoptosis
A1307882 TRADD 0.26 0.0007 apoptosis
BC041689 CASPI 0.31 0.0009 apoptosis
NM 014922 NALPI 0.31 0.0025 apoptosis
AF159615 FRAG I 0.33 0.0044 apoptosis
BC019307 BCL2LI 0.42 0.0027 apoptosis
NM 016426 GTSEI 0.43 0.0033 apoptosis
AK027080 LTBR 0.50 0.0047 apoptosis
M92287 CCND3 0.48 0.0028 cell cycle
A1242501 MAP7 0.2 0.0001 structural molecule
AF381029 LMNA 0.3 0.00001 structural molecule
X83929 DSC3 0.009 0.0035 cell adhesion
AB025105 CDH I 0.01 0.0009 cell adhesion
A1246000 SELL 0.21 0.002 cell adhesion
NM 003568 ANXA9 0.22 0.0031 cell adhesion
AF281287 PECAMI 0.36 0.0017 cell adhesion
100124 KRT14 0.0001 0.0003 cytoskeleton/motility
BC034535 KRT6B 0.005 0.0043 cytoskeleton/motility
M19156 KRTIO 0.018 0.001 cytoskeleton/motility
A1551 176 SDC I 0.039 0.0038 cytoskeleton/motility
NM 006478 GAS2LI 0.22 0.0016 cytoskeleton/motility
M34225 KRT8 0.26 0.0029 cytoskeleton/motility
NM 005886 KATNBI 0.27 0.0011 I cytoskeleton/motility
AK024835 CNN2 0.47 0.003 cytoskeleton/motility
NM 006350 FST 0.11 0.00001 extracellular matrix
AF177941 COLSA3 0.14 0.00001 extracellular matrix
L22548 COL18AI 0.49 0.0011 I extracellular matrix
M58051 FGFR3 0.007 0.0039 growth factor receptor
NM 004887 CXCLI4 0.009 0.0014 chemokine
AF289090 BMP7 0.13 0.002 cytokine
K03222 TGFA 0.2 0.0048 growth factor
M31682 INHBB 0.20 0.00001 cytokine
NM 004750 CRLFI 0.26 0.0003 cytokine binding
NM 002514 NOV (CCN3) 0.28 0.0009 growth factor
NM 000685 AGTRI 0.30 0.005 growth factor receptor
D 16431 HDGF 0.42 0.0046 creatine kinase
L36719 MAP2K3 0.22 0.0048 protein kinase activity
A1290975 ITPKC 0.28 0.0036 protein kinase activity
NM 001569 IRAKI 0.33 0.0001 protein kinase activity
AB025285 ERBB2 0.45 0.0003 protein kinase
AF029082 SFN 0.001 0.0028 signal transduction
AB065865 HM74 0.04 0.0047 signal transduction
AA021034 LTB4R 0.06 0.0006 signal transduction
NM 004445 EPHB6 0.12 0.0038 signal transduction
AF025304 EPHB2 0.17 0.0021 signal transduction
AB026663 MCIR 0.17 0.0046 signal transduction
AF035442 VAV3 0.17 0.004 signal transduction
NM 014030 GITI 0.21 0.0025 signal transduction
ABO I 152 CENTDI 0.21 0.0003 signal transduction
AK095244 CYB561 0.23 0.0001 signal transduction
AF106858 GPR56 0.23 0.0002 signal transduction
AF231024 CELSRI 0.23 0.0006 signal transduction
AF234887 CELSR2 0.24 0.0003 signal transduction
NM 007197 FZDIO 0.25 0.0009 signal transduction
NM 014349 APOL3 0.25 0.002 signal transduction
NM 004039 ANXA2 0.27 0.0044 signal transduction
A1285986 THBD 0.29 0.0004 signal transduction
M57730 EFNAI 0.31 0.0032 signal transduction
NM 002118 HLA-DMB 0.33 0.0008 signal transduction
AF42749 TUBB4 0.36 0.001 signal transduction
NM 005279 GPRI 0.40 0.0033 signal transduction
X60592 TNFRSF5 0.40 0.0032 signal transduction
BC052968 EPHB3 0.42 0.0001 signal transduction
M64749 CMKORI 0.46 0.0014 signal transduction
M21188 IDE 0.46 0.0031 signal transduction
ABO 18325 CENTD2 0.47 0.0004 signal transduction



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Table 2: List of under-expressed in leiomyomas as compared to scar tissues (keloids/incesional scars) (Continued)


AK054968
NM 001730
NM 004350
U34070
AF062649
NM 004235
X52773
AF202118
NM 000376
NM 006548
NM 007315
NM 004430
NM 003644
NM 005900
X14454
AF067572
NM 005596
AB002282
AK075393
AB021227
AB007774
AF143883
AF440204
NM 000777
NM 016593
BC001491
BC020734
AL133324
AF055027
NM 001630
AB011542
NM 005979
NM 020672
NM 005978
BCO 12610
AF052692
M 12529
NM 004925


Partial list of differentially expressed genes identified in leiomyomas (African Americans and Caucasians) as compared to keloid/incisional scars as shown in Fig. I. The genes
were selected based on p ranking of p < 0.005 and 2-fold cutoff change selection (F. Change) as described in materials and methods. Table 2 displays the under-expressed
genes in leiomyomas as compared to keloid/incisional scars.


Further comparison of leiomyomas' gene expression with
peritoneal adhesions (Affymetrix U95A subjected to cross-
platform comparability analysis) also identified a low
number of differentially expressed genes (85 genes) in
these tissues, although analysis based only on U95A
arrays identified higher numbers. The results indicate that
the molecular environment of leiomyomas may be more
comparable to peritoneal adhesions as compared to kel-
oids/incisional scars at least at late stage of their wound
healing development. Possibly the size of leiomyomas
(larger size often undergoing degeneration at the center),
and the stage of keloids, incesional scars and adhesions
formation following tissue injury influencing their gene
expression profiles would produce different results from
our study and their evaluation would enhance our under-
standing of molecular conditions that lead to tissue fibro-
sis at these and other sites [18-21].


A majority of the genes identified in leiomyomas, keloid,
incisional scars and adhesions function as regulators of
cell survival (cell cycle and apoptosis), cell and tissue


structure (ECM, adhesion molecules and cytoskeleton),
tissue turnover, inflammatory mediators, signal transduc-
tion and transcription and metabolism. Consistent with
the importance of ECM, cytoskeleton, adhesion mole-
cules and proteases in tissue fibrosis we identified the
expression of many of genes in these categories some with
5 to 60 fold increase in their expression. Elevated expres-
sion of DES, MYH11, MYL9 and SMTN in leiomyomas
and several KRTs in keloids and scars reflects the cellular
composition of these tissues. Additionally, PALLD has
been considered to serve as a novel marker of myofibrob-
last conversion and is regulated by profibrotic cytokine
such as TGF-P [22,23]. SM22, which is overexpressed in
keloids[24], promotes ECM accumulation through inhi-
bition of MMP-9 expression [25]. The expression of many
components of ECM including collagens, decorin, versi-
can, fibromodulin, intergrins, extracellular matrix protein
1 (ECM-1), syndecan and ESM-1 has been identified in
leiomyomas [11,17,26] as well as dermal wounds during
healing, scars and keloids (for review see [27-32]).We val-
idated the expression of ECM-2, ESM1, THBS1, FBLN5



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IIGB5
KLF5
RUNX3
CEBPA
PTTG I
KLF4
RXRA
HOXDI
VDR
IMP-2
STAT I
EGR3
GAS7
MADH I
IRFI
STAT6
NFIB
EDFI
CTSB
MMP24
CSTA
ALOX12
PTGS I
CYP3A5
CYP39AI
HMOXI
PGDS
GSS
CARMI
ANXA8
EGFL5
SIOOA13
SIOOA14
S I00A2
HFI
GJB3
APOE
AQP3


0.0005
0.0021
0.0001
0.0005
0.0039
0.0005
0.001 1
0.0006
0.0001
0.0031
0.00001
0.002
0.0033
0.0028
0.0013
0.0001
0.0041
0.0002
0.0016
0.0001
0.0018
0.0016
0.00001
0.0041
0.0027
0.0028
0.00001
0.002
0.00001
0.0006
0.0001
0.001
0.0005
0.005
0.00001
0.0001
0.0001
0.0003


signal transduction
transcription factor
transcription factor
transcription factor
transcription factor
transcription factor
transcription factor
transcription factor
transcription factor
transcription factor
transcription factor
transcription factor
transcription factor
transcription factor
transcription factor
transcription factor
transcription factor
transcription coactivator
protease activity
protease activity
cysteine protease inhibitor
catalytic activity
catalytic activity
catalytic activity
catalytic activity
catalytic activity
catalytic activity
catalytic activity
catalytic activity
calcium ion binding
calcium ion binding
calcium ion binding
calcium ion binding
calcium ion binding
complement activation
connexon channel activity
metabolism
transporter activity









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Table I: List of over-expressed in leiomyomas as compared to scar tissues (keloids/incesional scars)

Gene Bank Symbol Fold Change Probability Function


NM 003478 CUL5 5.06 0.0001 apoptosis
AB037736 CASP8AP2 4.07 0.0021 apoptosis
NM 018947 CYCS 2.08 0.0013 apoptosis
AB014517 CUL3 2.07 0.00001 apoptosis
BCO10958 CCND2 5.62 0.0041 cell cycle
U47413 CCNG I 3.16 0.0007 cell cycle
AF04873 CCNT2 2.83 0.0004 cell cycle
NM 001927 DBS 61.51 0.0022 cytoskeleton/motility
AK 124338 ACTG2 30.16 0.00001 cytoskeleton/motility
BC022015 CNN I 27.26 0.00001 cytoskeleton/motility
NM 006449 CDC42EP3 25.29 0.0051 cytoskeleton/motility
AB023209 KIAA0992 17.61 0.0004 cytoskeleton/motility
AF474156 TPM I 14.84 0.0029 cytoskeleton/motility
BCO I 1776 TPM2 12.04 0.00001 cytoskeleton/motility
M11315 COL4A I 11.87 0.0029 cytoskeleton/motility
AK126474 LMOD I 9.49 0.00001 cytoskeleton/motility
AB062484 CALD I 9.22 0.0042 cytoskeleton/motility
NM 003186 TAGLN 6.68 0.00001 cytoskeleton/motility
BCO 17554 ACTA2 5.18 0.00001 cytoskeleton/motility
AK074048 FLNA 5.08 0.00001 cytoskeleton/motility
NM 016274 CKIP- I 4.44 0.002 cytoskeleton/motility
BC003576 ACTN I 4.23 0.0024 cytoskeleton/motility
AF08984 I FLNC 3.43 0.0005 cytoskeleton/motility
X05610 COL4A2 7.86 0.0017 extracellular matrix
BC005159 COL6A I 3.70 0.002 extracellular matrix
A98730 CAPN6 13.7 0.0023 protease activity
U41766 ADAM9 4.76 0.0021 protease
NM 001110 ADAM 10 3.2 0.00001 protease
AF031385 CYR61 (CCNI) 9.13 0.0035 growth factor
M32977 VEGF 7.13 0.002 growth factor
AF035287 SDFRI 4.70 0.0001 chemokine receptor
X04434 IGFIR 3.64 0.0017 growth factor receptor
AB029156 HDGFRP3 2.89 0.0006 GF receptor activity
AF056979 IFNGRI 2.72 0.0001 signal transduction
AB020673 MYH I I 53.80 0.0006 signal transduction
D26070 ITPRI 26.18 0.0034 signal transduction
AB037717 SORBS I 15.25 0.0005 signal transduction
AFI 10225 ITGB IBP2 14.18 0.0009 signal transduction
AB004903 SOCS2 11.39 0.0002 signal transduction
B01 I 147 GREBI 11.37 0.0025 signal transduction
AB000509 TRAF5 7.83 0.0032 signal transduction
NM 005261 GEM 7.48 0.0003 signal transduction
AF028832 HSPCA 4.27 0.00001 signal transduction
AC00658 M6PR 3.85 0.0012 signal transduction
AF275719 HSPCB 3.74 0.001 signal transduction
A1242780 ITPKB 3.68 0.00001 signal transduction
AK095866 GPR125 3.62 0.0001 signal transduction
AFO 16050 NRPI 3.44 0.0011 signal transduction
ABO 15706 IL6ST 3.42 0.0002 signal transduction
AK057120 HMGBI 3.16 0.0001 signal transduction
NM 006644 HSPH I 3.14 0.002 signal transduction
AB072923 BSG 2.90 0.0024 signal transduction
AB010881 FZD7 2.62 0.0024 signal transduction
AF273055 INPP5A 2.58 0.002 signal transduction
AC078943 TANK 2.32 0.0005 signal transduction
AF051344 LTBP4 2.20 0.0002 signal transduction
A1404847 ILK 4.74 0.0002 protein kinase activity
AFI 1991 I CSNKIAI 3.40 0.0015 protein kinase activity
NM 002037 FYN 3.30 0.0028 protein kinase activity
AB058694 CDC2L5 2.37 0.0001 protein kinase activity
AF415177 CAMK2G 2.18 0.0008 protein kinase activity
NM 005654 NR2FI 12.57 0.0039 transcription factor
BC062602 PNN 9.93 0.0001 transcription factor
AK098174 MEIS I 9.61 0.00001 transcription factor
NM 000125 ESRI 9.36 0.0004 transcription factor
AF249273 BCLAFI 8.62 0.0001 transcription factor
AF017418 MEIS2 7.46 0.0009 transcription factor
AF045447 MADH4 6.39 0.00001 transcription factor



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Table I: List of over-expressed in leiomyomas as compared to scar tissues (keloids/incesional scars) (Continued)


Al 62/04
NM 001527
NM 004268
BC020868
BC002646
AY347527
AL833643
NM 021809
AB007836
NM 005760
AL833268
NM 005903
NM 022739
NM 003472
NM 001358
BC029619
AB082525





AF015812
AL079283
NM 003760
NM 012218
AB018284
AFI55908
AF209712
AL833430
AF297048
AF288537
AB034951
NM 001155
NM 003642
NM 002267
AK124769
A1238248
AF072928


Partial list of differentially expressed genes identified in leiomyomas (African Americans and Caucasians) as compared to keloid/incisional scars as shown in Fig. I. The genes
were selected based on p ranking of p < 0.005 and 2-fold cutoff change selection (F. Change) as described in materials and methods. Table I displays the over-expressed genes
in leiomyomas as compared to keloid/incisional scars.


and COL18A1 in keloids, incisional scars and adhesions
and the analysis indicated an elevated expression of
ECM2, THBS1 and FBLN5 in keloid/incisional scars and
COL18 in peritoneal adhesions as compared to leiomyo-
mas[17]. Although the biological significance of these
gene products and changes in their expression in leiomy-
omas, keloids and adhesions remains to be established,
the product of a specific number of these genes such as
ECMs, THBS1, FBLNs, MMPs and ADAMs play a critical
role in various aspect of wound healing and tissue fibrosis
[27-32]. A number of MMPs were equally expressed in lei-
omyomas, keloids and peritoneal adhesions with the
exception of lower MMP-14, MMP-24 and MMP-28
expression in leiomyomas, suggesting that these tissues
are potential target of their proteolytic actions. The bio-
logical importance of lower expression of these MMPs in
leiomyoma is unknown; however unlike most MMPs that
are secreted as inactive proenzymes and require activa-
tion, MMP-11 and MMP-28 are secreted in active forms.
In keratinocytes, MMP-28 is expressed in response to
injury and detected in the conditioned media of hyper-
trophic scars, but not normotrophic scars [33]. A lower


expression of MMP-28 and elevated expression of TIMP-3
in leiomyomas compared to keloids imply a lower matrix
turnover with an increase angiogenic and pro-apoptotic
activities that has been associated with TIMP-3 [34,35].


We identified an overexpression of a higher number of
apoptotic-related genes in keloids and incisional scars as
compared to leiomyomas, suggesting an increased rate of
cellular turnover. Because apoptotic and non-apoptotic
cell death is considered to increase local inflammatory
reaction and a key step in tissue fibrosis, a number of
genes functionally categorized as proinflammatory and
pro-fibrotic mediators were identified in these tissues.
Noticeable among these genes were TGF-P, IL-1, IL-6, IL-
11, IL-13, IL-17, IL-22 and IL-27 and chemokines CCL-2
to 5, CX3-CL1, CXCL-1, CXCL-12 and CXCL-14 and their
receptors. Elevated expression of PDGF-C, VEGF and
FGF2 in leiomyomas as compared to keloids and adhe-
sions imply an additional role for these angiogenic factors
in pathogenesis of leiomyomas. While the expression of
TGF-P was equally elevated in leiomyomas, keloids, inci-
sional scars and peritoneal adhesion as compared to their


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AR
HDAC2
CRSP6
STAT5B
JUN
CREBI
MAX
TGIF2
TGFB III
CEBPZ
MEF2C
MADH5
SMURF2
DEK
DHX15
ATFI
TSC22
MEF2A
DDXI7
HOXAIO
EIF5A
DDX5
EIFIA
EIF4G3
ILF3
EIF5B
HSPB7
MCP
SPARCLI
PTGIS
FSTLI
HSPA8
ANXA6
HAT I
KPNA3
XPO I
CENTB2
MTMR6


0.0018
0.00001
0.0001
0.0003
0.0042
0.0031
0.0014
0.0014
0.0007
0.00001
0.0019
0.0037
0.0013
0.0001
0.0029
0.0026
0.0002
0.0024
0.0035
0.00001
0.001
0.0004
0.0005
0.0028
0.0003
0.002
0.0002
0.00001
0.00001
0.0004
0.001
0.001
0.0014
0.00001
0.0031
0.0002
0.0045
0.002


transcription factor
transcription factor
transcription factor
transcription factor
transcription factor
transcription factor
transcription factor
transcription factor
transcription coactivator
transcription factor
transcription factor
transcription factor
transcription factor
transcription factor
transcription factor
transcription factor
transcription factor
transcription factor
translation factor
translation factor
translation factor
translation factor
translation factor
translation factor
translation factor
translation factor
protein binding
complement activation
calcium ion binding
catalytic activity
calcium ion binding
protein binding
calcium ion binding
catalytic activity
protein transporter
protein transporter
GTPase activator activity
phosphatase activity









Reproductive Biology and Endocrinology 2007, 5:35


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Table 3: Differentially expressed genes in leiomyomas compared to keloids/incesional scars

Gene Bank Symbol F. Change F. Change P value Function
LAA:Scar LC:Scar

NM 006198 PCP4 68.14 6.66 0.0017 system development
S67238 MYOSIN 62.78 36.69 0.0034 cytoskeleton/motility
NM 004342 Caldl 21.43 9.32 0.0047 cytoskeleton/motility
NM 013437 LRP12 20.6 6.82 0.0053 cellular process
AC004010 AMIGO2 19.07 10.61 0.0021 cell adhesion
AF040254 OCX 18.71 5.39 0.0099 signal transduction
NM 015385 SORBSI 17.44 9.26 0.0003 cytoskeleton/motility
NM 012278 ITGBIBP2 17.42 9.9 0.0018 signal transduction
NM 006101 KNTC2 17.33 5.23 0.0022 transcription factor
NM 001845 COL4AI 16.08 5.94 0.0029 cytoskeleton/motility
AF 104857 CDC42EP3 16.08 3.78 0.0002 cytoskeleton/motility
AW188131 DDX17 15.65 9.11 0.0005 translation factor
NM 001057 TACR2 15.6 4.51 0.0062 signal transduction
A1375002 ZNF447 14.55 8.04 0.0061 transcription factor
NM 014890 DOC I 14.35 5.19 0.0002 proteolysis
NM 001784 CD97 13.16 6.35 0.00004 signal transduction
BFI 11821 WSBI 12.34 7.36 0.0024 signal transduction
AW152664 PNN 12.19 8.26 0.003 transcription factor
NM 002380 MATN2 11.86 5.62 0.001 I extracellular matrix
NM 007362 NCBP2 11.38 8.04 0.0034 RNA processing
AK023406 Macfl 8.8 4.77 0.0041 ECM signaling
AF095192 BAG2 8.01 4.34 0.0018 apoptosis
NM 004196 CDKLI 7.91 2.83 0.0017 cell cycle
BF512200 MBNL2 7.58 3.01 0.0014 muscle differentiaon
AW043713 Sulfl 6.9 0.78 0.0039 hydrolase activity
NM 004781 VAMP3 6.76 3.02 0.0016 trafficking
Al 149535 STAT5B 5.62 3.94 0.0043 transcription factor
NM 016277 RAB23 5.61 2.68 0.0055 signal transduction
A1582238 TRAI 5.13 3.46 0.0042 calcium ion binding
NM 005722 ACTR2 4.04 2.49 0.0001 cytoskeleton/motility
AFO 16005 RERE 4.02 2.87 0.008 transcription factor
AL046979 TNSI 3.65 2.14 0.0047 signal transduction
NM 005757 MBNL2 3.57 0.84 0.0049 muscle development
Al 133768 LDB3 3.3 1.53 0.0056 cytoskeleton/motility
A1650819 CUL4B 3.04 1.59 0.0045 metabolism
AL031602 MTIK 0.61 0.33 0.0086 cadmium ion binding
U85658 TFAP2C 0.27 0.14 0.0083 transcription factor
NM 003790 TNFRSF25 0.19 0.11 0.007 apoptosis
BC002495 BAIAP2 0.18 0.11 0.0003 signal transduction
AV691491 TMEM30B 0.13 0.09 0.0093 cell cycle control
A1889941 COL4A6 10.4 30.21 0.007 extracellular matrix
AW45171 I PBXI 14.44 18.14 0.0001 transcription factor
NM 014668 GREBI 7.18 15.94 0.0089
NM 004619 TRAF5 6.47 11.46 0.0091 signal transduction
NM 005418 ST5 5.83 8.1 0.0044 signal transduction
BC00281 I SUMO2 0.47 0.83 0.0035 protein binding
AV70089 ETS2 0.28 0.54 0.0082 transcription factor
AB042557 PDE4DIP 0.2 0.39 0.0019 signaling
NM 014485 PGDS 0.17 0.31 0.0027 catalytic activity
A1984221 COL5A3 0.08 0.17 0.001 I extracellular matrix
NM 006823 PKIA 0.08 0.17 0.0034 Kinase regulator
AU 144284 IRF6 0.04 0.15 0.0026 transcription factor
NM 000962 PTGSI 0.06 0.11 0.0046 catalytic activity
NM 022898 BCLIIB 0.05 0.09 0.0099 transcription factor
NM 001982 ERBB3 0.02 0.06 0.0066 signal transduction
NM 002705 PPL 0.005 0.031 0.0073 hydrolase activity
NM 001630 ANXA8 0.006 0.02 0.0079 calcium ion binding
N74607 AQP3 0.006 0.02 0.0098 transporter activity
NM 000142 FGFR3 0.007 0.009 0.01 Growth factor
Receptor

Partial list of differentially expressed genes from several functional categories in leiomyomas from African Americans and Caucasians as compared to keloids/
incesional scars as shown in Fig. 2. The genes were selected based on p ranking of p < 0.01 and following 2-fold cutoff change



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Reproductive Biology and Endocrinology 2007, 5:35



normal tissues reinforcing the importance of TGF-P as
principle mediator of tissue fibrosis [30]. Although profi-
brotic action of TGF-P is reported to involve the induction
of CTGF, a member of PDGF family with mitogen action
for myofibroblasts [36], it is expressed at lower levels in
leiomyomas as compared to myometrium [26,37,38].
However, leiomyomas of African Americans expressed a
3.3 fold higher levels of CTGF as compared to Caucasians,
and 12.6 and 4.3 fold higher as compared to keloids and
incisional scars, respectively. Although the biological sig-
nificance of these differences needs further investigation,
altered expression of many of these genes as compared to
their normal tissues counterpart also imply their potential
role in various cellular processes that results in tissue
fibrosis.

The genes encoding signal transduction and transcription
factors represented the largest functional category in leio-
myomas and scar tissues. They included several genes
such as NR2F1, PNN, Smad4, Smad5, STAT5B, JUN,
TGIF2, and ATF1 that were over-expressed while RUNX3,
STAT1, STAT6, EGR3, GAS7, Smadl, and EDF1 were
underexpressed in leiomyomas as compared to keloid/
incisional scars. We validated the expression of E2F1,
RUNX3, EGR3 and TBPIP in leiomyomas [17], keloids,
incisional scars and peritoneal adhesions showing a good
correlation with microarray data Since activation of these
signal transduction pathways and transcription factors
regulate the expression of large number of genes with
diverse functional activities their altered expression in
these tissues could have a considerably more important
role in tissue fibrosis than previously considered. Prefer-
ential phosphorylation of many of these transcription fac-
tors such as Jun, Stats, Smads, Runx and EGRs leads to
regulation of target genes involved in cell growth and
apoptosis, inflammation, angiogenesis and tissue turno-
ver with central roles in tissue fibrosis [11,17,39-42]

In conclusion, the gene expression profiling involving lei-
omyomas and their comparison with keloids, incisional
scars and peritoneal adhesion indicated that a combina-
tion of tissue-specific and common genes differentiate
their molecular environments. The tissue-specific differ-
ences were not based on the presence/absence of unique
genes, but rather the level of expression of selective
number of genes accounting for 3 to 12% of the genes on
the array. Although the nature of leiomyomas' develop-
ment and growth is vastly different from these fibrotic tis-
sues, we speculate that the outcome of their tissue
characteristics is influenced by the products of genes regu-
lating cell growth and apoptosis, inflammation, angio-
genesis and tissue turnover, and may also be under
different tissue-specific regulatory control.


http://www.rbej. com/content/5/1/35


Competing interests
The authors) declare that they have no competing inter-
ests.

Authors' contributions
XL, QP and NC participated in all aspect of the experimen-
tal design and writing of the work presented here. The
final microarray gene chips were performed at Interdisci-
plinary Center for Biotechnology Research at the Univer-
sity of Florida. The analysis of microarray gene expression
profiles between the gene chips U95 and 133a was carried
out by LL and gene expression analysis and realtime PCR
was performed by XL and QP. All the authors read and
approved the final manuscript.

Acknowledgements
We thank Dr. Mick Popp at Interdisciplinary Center for Biotechnology
Research at the University of Florida for assistance with microarray chip
analysis. The work presented here is supported by a grant HD37432 from
the National Institute of Health. The work was presented in part at the 53
rd Annual Meeting of the Society for Gynecological Investigation, Reno NA,
and March 2007.

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