Open Access

Genomic and proteomic profiling II: Comparative assessment of gene expression profiles in leiomyomas, keloids, and surgically-induced scars

Reproductive Biology and Endocrinology20075:35

https://doi.org/10.1186/1477-7827-5-35

Received: 15 May 2007

Accepted: 24 August 2007

Published: 24 August 2007

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 E2F1, RUNX3, EGR3, TBPIP, ECM-2, ESM1, THBS1, GAS1, ADAM17, CST6, FBLN5, and COL18A 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 myometrial smooth muscle cells and/or connective tissue fibroblasts during the reproductive years. Leiomyomas can develop in multiple numbers that are individually encapsulated by a connective tissue core separating them from the surrounding normal myometrium and are ovarian 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 Americans 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 [36]. Unlike surgically-induced and hypertrophic scars that are confined to the area of original tissue injury, keloids can expand beyond the boundaries of their original sites following removal and during healing. Keloids are rather similar to hypertrophic scars at early stages of development, however they become collagenous and poorly vascularized 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 adhesions also display a considerable histological similarity with dermal scars; however there is no data to suggest a higher risk of adhesion formation with ethnicity. Comparatively, uterine tissue injury i.e., following myomectomy 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 vascuraized 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 identified excess production and deposition of extracellular matrix, namely collagens in leiomyomas, keloids, hypertrophic and surgical scars and peritoneal adhesions [2, 710]. Evidence also exists implicating altered production of several proinflammatory and profibrotic cytokines, proteases and adhesion molecules in pathogenesis and characteristic of these and other fibrotic disorders [1114]. Large-scale gene expression studies have provided additional evidence for the expression of a number of differentially expressed genes in leiomyomas [11, 1517], 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-β system, proteases, adhesion molecules and extracellular matrix etc. (for review see [717]). Despite these advancements, the biological significance of many of these genes in pathophysiology of leiomyomas 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 comparatively analyzed the molecular profile that distinguishes leiomyomas from other fibrotic tissues, specifically keloids.

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 individually 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 Americans. In addition, we utilized the gene expression data obtained from our previous study [11] involving leiomyomas (N = 3) and peritoneal adhesions (N = 3) using human U95A GeneChips. These tissues were from Caucasians patients with the exception of one peritoneal adhesion 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 menstrual 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 corresponding normal tissues i.e. myometrium, skin and parietal 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 leiomyomas 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 learning 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 annotation 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 regulated genes may be different from probe sets with smallest 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 combine p-values using inverse chi-square method and permutation 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 discovery 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[17].

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 assessment and P ranking of P < 0.005, followed by 2-fold cutoff change selection, resulted in identification of 1124 transcripts (1103 genes) of which 732 genes were over-expressed and 371 were under-expressed in leiomyomas as compared to keloids/incisional scars (N = 4). Hierarchical clustering separated these genes into distinctive groups with each cohort clustering into the corresponding subgroup (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.
Table 1

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

BC010958

CCND2

5.62

0.0041

cell cycle

U47413

CCNG1

3.16

0.0007

cell cycle

AF048731

CCNT2

2.83

0.0004

cell cycle

NM_001927

DBS

61.51

0.0022

cytoskeleton/motility

AK124338

ACTG2

30.16

0.00001

cytoskeleton/motility

BC022015

CNN1

27.26

0.00001

cytoskeleton/motility

NM_006449

CDC42EP3

25.29

0.0051

cytoskeleton/motility

AB023209

KIAA0992

17.61

0.0004

cytoskeleton/motility

AF474156

TPM1

14.84

0.0029

cytoskeleton/motility

BC011776

TPM2

12.04

0.00001

cytoskeleton/motility

M11315

COL4A1

11.87

0.0029

cytoskeleton/motility

AK126474

LMOD1

9.49

0.00001

cytoskeleton/motility

AB062484

CALD1

9.22

0.0042

cytoskeleton/motility

NM_003186

TAGLN

6.68

0.00001

cytoskeleton/motility

BC017554

ACTA2

5.18

0.00001

cytoskeleton/motility

AK074048

FLNA

5.08

0.00001

cytoskeleton/motility

NM_016274

CKIP-1

4.44

0.002

cytoskeleton/motility

BC003576

ACTN1

4.23

0.0024

cytoskeleton/motility

AF089841

FLNC

3.43

0.0005

cytoskeleton/motility

X05610

COL4A2

7.86

0.0017

extracellular matrix

BC005159

COL6A1

3.70

0.002

extracellular matrix

A98730

CAPN6

13.7

0.0023

protease activity

U41766

ADAM9

4.76

0.0021

protease

NM_001110

ADAM10

3.2

0.00001

protease

AF031385

CYR61 (CCN1)

9.13

0.0035

growth factor

M32977

VEGF

7.13

0.002

growth factor

AF035287

SDFR1

4.70

0.0001

chemokine receptor

X04434

IGF1R

3.64

0.0017

growth factor receptor

AB029156

HDGFRP3

2.89

0.0006

GF receptor activity

AF056979

IFNGR1

2.72

0.0001

signal transduction

AB020673

MYH11

53.80

0.0006

signal transduction

D26070

ITPR1

26.18

0.0034

signal transduction

AB037717

SORBS1

15.25

0.0005

signal transduction

AF110225

ITGB1BP2

14.18

0.0009

signal transduction

AB004903

SOCS2

11.39

0.0002

signal transduction

B011147

GREB1

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

AC006581

M6PR

3.85

0.0012

signal transduction

AF275719

HSPCB

3.74

0.001

signal transduction

AJ242780

ITPKB

3.68

0.00001

signal transduction

AK095866

GPR125

3.62

0.0001

signal transduction

AF016050

NRP1

3.44

0.0011

signal transduction

AB015706

IL6ST

3.42

0.0002

signal transduction

AK057120

HMGB1

3.16

0.0001

signal transduction

NM_006644

HSPH1

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

AJ404847

ILK

4.74

0.0002

protein kinase activity

AF119911

CSNK1A1

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

NR2F1

12.57

0.0039

transcription factor

BC062602

PNN

9.93

0.0001

transcription factor

AK098174

MEIS1

9.61

0.00001

transcription factor

NM_000125

ESR1

9.36

0.0004

transcription factor

AF249273

BCLAF1

8.62

0.0001

transcription factor

AF017418

MEIS2

7.46

0.0009

transcription factor

AF045447

MADH4

6.39

0.00001

transcription factor

AF162704

AR

5.54

0.0018

transcription factor

NM_001527

HDAC2

4.76

0.00001

transcription factor

NM_004268

CRSP6

4.76

0.0001

transcription factor

BC020868

STAT5B

4.57

0.0003

transcription factor

BC002646

JUN

3.84

0.0042

transcription factor

AY347527

CREB1

3.77

0.0031

transcription factor

AL833643

MAX

3.66

0.0014

transcription factor

NM_021809

TGIF2

3.58

0.0014

transcription factor

AB007836

TGFB1I1

3.55

0.0007

transcription coactivator

NM_005760

CEBPZ

3.53

0.00001

transcription factor

AL833268

MEF2C

3.49

0.0019

transcription factor

NM_005903

MADH5

3.10

0.0037

transcription factor

NM_022739

SMURF2

2.58

0.0013

transcription factor

NM_003472

DEK

2.55

0.0001

transcription factor

NM_001358

DHX15

2.49

0.0029

transcription factor

BC029619

ATF1

2.41

0.0026

transcription factor

AB082525

TSC22

2.26

0.0002

transcription factor

AL831995

MEF2A

2.25

0.0024

transcription factor

AA765457

DDX17

10.41

0.0035

translation factor

NM_018951

HOXA10

8.69

0.00001

translation factor

BC000751

EIF5A

4.07

0.001

translation factor

AF015812

DDX5

2.48

0.0004

translation factor

AL079283

EIF1A

2.35

0.0005

translation factor

NM_003760

EIF4G3

2.35

0.0028

translation factor

NM_012218

ILF3

2.29

0.0003

translation factor

AB018284

EIF5B

2.26

0.002

translation factor

AF155908

HSPB7

9.52

0.0002

protein binding

AF209712

MCP

6.54

0.00001

complement activation

AL833430

SPARCL1

5.12

0.00001

calcium ion binding

AF297048

PTGIS

4.26

0.0004

catalytic activity

AF288537

FSTL1

4.11

0.001

calcium ion binding

AB034951

HSPA8

3.13

0.001

protein binding

NM_001155

ANXA6

2.85

0.0014

calcium ion binding

NM_003642

HAT1

2.81

0.00001

catalytic activity

NM_002267

KPNA3

2.55

0.0031

protein transporter

AK124769

XPO1

2.46

0.0002

protein transporter

AJ238248

CENTB2

2.37

0.0045

GTPase activator activity

AF072928

MTMR6

2.17

0.002

phosphatase activity

Partial list of differentially expressed genes identified in leiomyomas (African Americans and Caucasians) as compared to keloid/incisional scars as shown in Fig. 1. 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 1 displays the over-expressed genes in leiomyomas as compared to keloid/incisional scars.

Table 2

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

Gene Bank

Symbol

Fold Change

Probability

Function

AF004709

MAPK13

0.06

0.0002

apoptosis

AF010316

PTGES

0.09

0.0003

apoptosis

NM_014430

CIDEB

0.21

0.0014

apoptosis

AJ307882

TRADD

0.26

0.0007

apoptosis

BC041689

CASP1

0.31

0.0009

apoptosis

NM_014922

NALP1

0.31

0.0025

apoptosis

AF159615

FRAG1

0.33

0.0044

apoptosis

BC019307

BCL2L1

0.42

0.0027

apoptosis

NM_016426

GTSE1

0.43

0.0033

apoptosis

AK027080

LTBR

0.50

0.0047

apoptosis

M92287

CCND3

0.48

0.0028

cell cycle

AJ242501

MAP7

0.2

0.0001

structural molecule

AF381029

LMNA

0.3

0.00001

structural molecule

X83929

DSC3

0.009

0.0035

cell adhesion

AB025105

CDH1

0.01

0.0009

cell adhesion

AJ246000

SELL

0.21

0.002

cell adhesion

NM_003568

ANXA9

0.22

0.0031

cell adhesion

AF281287

PECAM1

0.36

0.0017

cell adhesion

J00124

KRT14

0.0001

0.0003

cytoskeleton/motility

BC034535

KRT6B

0.005

0.0043

cytoskeleton/motility

M19156

KRT10

0.018

0.001

cytoskeleton/motility

AJ551176

SDC1

0.039

0.0038

cytoskeleton/motility

NM_006478

GAS2L1

0.22

0.0016

cytoskeleton/motility

M34225

KRT8

0.26

0.0029

cytoskeleton/motility

NM_005886

KATNB1

0.27

0.0011

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

COL18A1

0.49

0.0011

extracellular matrix

M58051

FGFR3

0.007

0.0039

growth factor receptor

NM_004887

CXCL14

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

CRLF1

0.26

0.0003

cytokine binding

NM_002514

NOV (CCN3)

0.28

0.0009

growth factor

NM_000685

AGTR1

0.30

0.005

growth factor receptor

D16431

HDGF

0.42

0.0046

creatine kinase

L36719

MAP2K3

0.22

0.0048

protein kinase activity

AJ290975

ITPKC

0.28

0.0036

protein kinase activity

NM_001569

IRAK1

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

MC1R

0.17

0.0046

signal transduction

AF035442

VAV3

0.17

0.004

signal transduction

NM_014030

GIT1

0.21

0.0025

signal transduction

AB011152

CENTD1

0.21

0.0003

signal transduction

AK095244

CYB561

0.23

0.0001

signal transduction

AF106858

GPR56

0.23

0.0002

signal transduction

AF231024

CELSR1

0.23

0.0006

signal transduction

AF234887

CELSR2

0.24

0.0003

signal transduction

NM_007197

FZD10

0.25

0.0009

signal transduction

NM_014349

APOL3

0.25

0.002

signal transduction

NM_004039

ANXA2

0.27

0.0044

signal transduction

AI285986

THBD

0.29

0.0004

signal transduction

M57730

EFNA1

0.31

0.0032

signal transduction

NM_002118

HLA-DMB

0.33

0.0008

signal transduction

AF427491

TUBB4

0.36

0.001

signal transduction

NM_005279

GPR1

0.40

0.0033

signal transduction

X60592

TNFRSF5

0.40

0.0032

signal transduction

BC052968

EPHB3

0.42

0.0001

signal transduction

M64749

CMKOR1

0.46

0.0014

signal transduction

M21188

IDE

0.46

0.0031

signal transduction

AB018325

CENTD2

0.47

0.0004

signal transduction

AK054968

ITGB5

0.49

0.0005

signal transduction

NM_001730

KLF5

0.04

0.0021

transcription factor

NM_004350

RUNX3

0.08

0.0001

transcription factor

U34070

CEBPA

0.11

0.0005

transcription factor

AF062649

PTTG1

0.15

0.0039

transcription factor

NM_004235

KLF4

0.20

0.0005

transcription factor

X52773

RXRA

0.20

0.0011

transcription factor

AF202118

HOXD1

0.21

0.0006

transcription factor

NM_000376

VDR

0.21

0.0001

transcription factor

NM_006548

IMP-2

0.26

0.0031

transcription factor

NM_007315

STAT1

0.32

0.00001

transcription factor

NM_004430

EGR3

0.34

0.002

transcription factor

NM_003644

GAS7

0.36

0.0033

transcription factor

NM_005900

MADH1

0.48

0.0028

transcription factor

X14454

IRF1

0.49

0.0013

transcription factor

AF067572

STAT6

0.49

0.0001

transcription factor

NM_005596

NFIB

0.49

0.0041

transcription factor

AB002282

EDF1

0.40

0.0002

transcription coactivator

AK075393

CTSB

0.50

0.0016

protease activity

AB021227

MMP24

0.29

0.0001

protease activity

AB007774

CSTA

0.02

0.0018

cysteine protease inhibitor

AF143883

ALOX12

0.06

0.0016

catalytic activity

AF440204

PTGS1

0.08

0.00001

catalytic activity

NM_000777

CYP3A5

0.14

0.0041

catalytic activity

NM_016593

CYP39A1

0.21

0.0027

catalytic activity

BC001491

HMOX1

0.23

0.0028

catalytic activity

BC020734

PGDS

0.26

0.00001

catalytic activity

AL133324

GSS

0.39

0.002

catalytic activity

AF055027

CARM1

0.41

0.00001

catalytic activity

NM_001630

ANXA8

0.01

0.0006

calcium ion binding

AB011542

EGFL5

0.43

0.0001

calcium ion binding

NM_005979

S100A13

0.31

0.001

calcium ion binding

NM_020672

S100A14

0.02

0.0005

calcium ion binding

NM_005978

S100A2

0.003

0.005

calcium ion binding

BC012610

HF1

0.22

0.00001

complement activation

AF052692

GJB3

0.03

0.0001

connexon channel activity

M12529

APOE

0.21

0.0001

metabolism

NM_004925

AQP3

0.01

0.0003

transporter activity

Partial list of differentially expressed genes identified in leiomyomas (African Americans and Caucasians) as compared to keloid/incisional scars as shown in Fig. 1. 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.

Figure 1

Cluster analysis of 1124 differentially expressed transcripts in leiomyomas (N = 6) form African Americans (AAL1, AAL2 and AAL3), Caucasians (CL1, CL2, and CL3) and in keloids (S3 and S4) and incisional scars (S1 and S2) identified following supervised and unsupervised analysis and p ranking of P < 0.005 followed by 2-fold cutoff change selection (Affymetrix U133A). Genes represented by rows were clustered according 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 (S1) and keloids 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.

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 differentially expressed genes as compared to keloids (N = 2)/incisional 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. American 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 leiomyomas of AA and Caucasians, compared to keloids/incisional scars (Table 3).
Table 3

Differentially expressed genes in leiomyomas compared to keloids/incesional scars

Gene Bank

Symbol

F. Change

LAA:Scar

F. Change

LC:Scar

P value

Function

NM_006198

PCP4

68.14

6.66

0.0017

system development

S67238

MYOSIN

62.78

36.69

0.0034

cytoskeleton/motility

NM_004342

Cald1

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

SORBS1

17.44

9.26

0.0003

cytoskeleton/motility

NM_012278

ITGB1BP2

17.42

9.9

0.0018

signal transduction

NM_006101

KNTC2

17.33

5.23

0.0022

transcription factor

NM_001845

COL4A1

16.08

5.94

0.0029

cytoskeleton/motility

AF104857

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

AI375002

ZNF447

14.55

8.04

0.0061

transcription factor

NM_014890

DOC1

14.35

5.19

0.0002

proteolysis

NM_001784

CD97

13.16

6.35

0.00004

signal transduction

BF111821

WSB1

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

0.0011

extracellular matrix

NM_007362

NCBP2

11.38

8.04

0.0034

RNA processing

AK023406

Macf1

8.8

4.77

0.0041

ECM signaling

AF095192

BAG2

8.01

4.34

0.0018

apoptosis

NM_004196

CDKL1

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

AI149535

STAT5B

5.62

3.94

0.0043

transcription factor

NM_016277

RAB23

5.61

2.68

0.0055

signal transduction

AI582238

TRA1

5.13

3.46

0.0042

calcium ion binding

NM_005722

ACTR2

4.04

2.49

0.0001

cytoskeleton/motility

AF016005

RERE

4.02

2.87

0.008

transcription factor

AL046979

TNS1

3.65

2.14

0.0047

signal transduction

NM_005757

MBNL2

3.57

0.84

0.0049

muscle development

AJ133768

LDB3

3.3

1.53

0.0056

cytoskeleton/motility

AI650819

CUL4B

3.04

1.59

0.0045

metabolism

AL031602

MT1K

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

AI889941

COL4A6

10.4

30.21

0.007

extracellular matrix

AW451711

PBX1

14.44

18.14

0.0001

transcription factor

NM_014668

GREB1

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

BC002811

SUMO2

0.47

0.83

0.0035

protein binding

AV700891

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

AI984221

COL5A3

0.08

0.17

0.0011

extracellular matrix

NM_006823

PKIA

0.08

0.17

0.0034

Kinase regulator

AU144284

IRF6

0.04

0.15

0.0026

transcription factor

NM_000962

PTGS1

0.06

0.11

0.0046

catalytic activity

NM_022898

BCL11B

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

We also utilized the gene expression values obtained in our previous microarray studies in leiomyomas[11] and peritoneal adhesions (unpublished results) for comparative analysis. Because these results were generated using Affymetrix U95A GeneChips, due to cross-platform comparability with U133A the combined data from both platforms 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 adhesions (considered as one cohort during analysis). Of these, 85 genes were differentially expressed in leiomyomas as compared to peritoneal adhesions (Fig. 2), however 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-platform 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 leiomyomas from Caucasians (CL1, CL2, and CL3) and peritoneal adhesions (A1, 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 cutoff change selection. The genes represented by rows were clustered according to their similarities in expression patterns 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 regulator 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 expression in keloids, incisional scars and peritoneal adhesions. The level of expression of these 12 genes displayed significant variations among these tissues with some overlapping 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 keloids/incisional scars (scar) and adhesions, the results indicated that the expression of E2F1, TBPIP and ESM1 was elevated in leiomyoma as compared to keloids/incisional scars and adhesions (Fig. 3, P < 0.05). In contrast, the expression of EGR3, ECM2, THBS1, GAS1 and FBLN5 in scars and RUNX3 and COL18 expression in peritoneal adhesions was higher as compared to leiomyomas (Fig. 3).
Figure 3

The bar graphs show the relative mean expression levels of 12 genes (E2F1, RUNX3, EGR3, TBPIP, ECM-2 ESM1, THBS1, GAS1, ADAM17, CST6, FBLN5, and COL18A1) in leiomyomas (LYM), keloids/incisional scars (Scar) and peritoneal 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 1 independently for each gene prior to normalization against their expression levels in myometrium form a Caucasian serving as control. The asterisks * indicate statistical difference between the expression of these genes with arrows pointing the difference between each group. A probability level of P < 0.05 was considered significant.

Discussion

Using a large-scale gene expression profiling approach we compared leiomyomas with keloids, incisional cars and peritoneal adhesions and found that their molecular environments 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 elevated level of expression of a number of muscle cell-specific genes in leiomyomas and fibroblast-specific genes in keloids reflected the specific cellular make up of these tissues. 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 govern 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 leiomyoma, keloids, incisional scars, and possibly other fibrotic disorders, despite differences in the nature of their development 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 adhesions were first formed, reflecting the stage of wound healing, 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 ethnicity, because of more frequent development of leiomyomas and keloids in African Americans. However, it is worth mentioning that comparing leiomyomas with keloids from this ethnic group showed a limited difference in their gene expression profile, or when compared with leiomyomas from Caucasians, suggesting the existence of a comparable environment in leiomyomas and keloids. 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 keloids/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 understanding of molecular conditions that lead to tissue fibrosis at these and other sites [1821].

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 transduction and transcription and metabolism. Consistent with the importance of ECM, cytoskeleton, adhesion molecules 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 expression 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 myofibroblast conversion and is regulated by profibrotic cytokine such as TGF-β [22, 23]. SM22, which is overexpressed in keloids[24], promotes ECM accumulation through inhibition of MMP-9 expression [25]. The expression of many components of ECM including collagens, decorin, versican, 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 [2732]).

We validated the expression of ECM-2, ESM1, THBS1, FBLN5 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 leiomyomas[17]. Although the biological significance of these gene products and changes in their expression in leiomyomas, 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 [2732]. A number of MMPs were equally expressed in leiomyomas, 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 biological importance of lower expression of these MMPs in leiomyoma is unknown; however unlike most MMPs that are secreted as inactive proenzymes and require activation, 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 hypertrophic 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-β, 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 adhesions imply an additional role for these angiogenic factors in pathogenesis of leiomyomas. While the expression of TGF-β was equally elevated in leiomyomas, keloids, incisional scars and peritoneal adhesion as compared to their normal tissues reinforcing the importance of TGF-β as principle mediator of tissue fibrosis [30]. Although profibrotic action of TGF-β 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 significance 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 leiomyomas 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, Smad1, 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. Preferential phosphorylation of many of these transcription factors such as Jun, Stats, Smads, Runx and EGRs leads to regulation of target genes involved in cell growth and apoptosis, inflammation, angiogenesis and tissue turnover with central roles in tissue fibrosis [11, 17, 3942]

In conclusion, the gene expression profiling involving leiomyomas and their comparison with keloids, incisional scars and peritoneal adhesion indicated that a combination of tissue-specific and common genes differentiate their molecular environments. The tissue-specific differences 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' development and growth is vastly different from these fibrotic tissues, we speculate that the outcome of their tissue characteristics is influenced by the products of genes regulating cell growth and apoptosis, inflammation, angiogenesis and tissue turnover, and may also be under different tissue-specific regulatory control.

Declarations

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.

Authors’ Affiliations

(1)
Department of Obstetrics and Gynecology, University of Florida, College of Medicine
(2)
Interdisciplinary Center for Biotechnology Research, University of Florida, College of Medicine

References

  1. Wise LA, Palmer JR, Stewart EA, Rosenberg L: Age-specific incidence rates for self-reported uterine leiomyomata in the Black Women's Health Study. Obstet Gynecol. 2005, 105: 563-568.PubMed CentralView ArticlePubMedGoogle Scholar
  2. Catherino WH, Leppert PC, Stenmark MH, Payson M, Potlog-Nahari C, Nieman LK, Segars JH: Reduced dermatopontin expression is a molecular link between uterine leiomyomas and keloids. Genes Chromosomes Cancer. 2004, 40: 204-217. 10.1002/gcc.20035.PubMed CentralView ArticlePubMedGoogle Scholar
  3. Al Attar A, Mess S, Thomassen JM, Kauffman CL, Davison SP: Keloid pathogenesis and treatment. Plast Reconstr Surg. 2006, 117: 286-300. 10.1097/01.prs.0000195073.73580.46.View ArticlePubMedGoogle Scholar
  4. Burd A, Huang L: Hypertrophic response and keloid diathesis: two very different forms of scar. Plast Reconstr Surg. 2005, 116: 150e-157e. 10.1097/01.prs.0000191977.51206.43.View ArticlePubMedGoogle Scholar
  5. Louw L: The keloid phenomenon: progress toward a solution. Clin Anat. 2007, 20: 3-14. 10.1002/ca.20374.View ArticlePubMedGoogle Scholar
  6. Slemp AE, Kirschner RE: Keloids and scars: a review of keloids and scars, their pathogenesis, risk factors, and management. Curr Opin Pediatr. 2006, 18: 396-402. 10.1097/01.mop.0000236389.41462.ef.View ArticlePubMedGoogle Scholar
  7. Chegini N: Peritoneal molecular environment, adhesion formation and clinical implication. Front Biosci. 2002, 7: e91-115. 10.2741/chegini.View ArticlePubMedGoogle Scholar
  8. Berto AG, Sampaio LO, Franco CR, Cesar RM, Michelacci YM: A comparative analysis of structure and spatial distribution of decorin in human leiomyoma and normal myometrium. Biochim Biophys Acta. 2003, 1619: 98-112.View ArticlePubMedGoogle Scholar
  9. Lindblad WJ: Perspective article: collagen expression by novel cell populations in the dermal wound environment. Wound Repair Regen. 1998, 6: 186-193. 10.1046/j.1524-475X.1998.60304.x.View ArticlePubMedGoogle Scholar
  10. Midwood KS, Williams LV, Schwarzbauer JE: Tissue repair and the dynamics of the extracellular matrix. Int J Biochem Cell Biol. 2004, 36: 1031-1037. 10.1016/j.biocel.2003.12.003.View ArticlePubMedGoogle Scholar
  11. Luo X, Ding L, Xu J, Williams RS, Chegini N: Leiomyoma and myometrial gene expression profiles and their responses to gonadotropin-releasing hormone analog therapy. Endocrinology. 2005, 146: 1074-1096. 10.1210/en.2004-1384.View ArticlePubMedGoogle Scholar
  12. Martin P, Leibovich SJ: Inflammatory cells during wound repair: the good, the bad and the ugly. Trends Cell Biol. 2005, 15: 599-607. 10.1016/j.tcb.2005.09.002.View ArticlePubMedGoogle Scholar
  13. Romagnani P, Lasagni L, Annunziato F, Serio M, Romagnani S: CXC chemokines: the regulatory link between inflammation and angiogenesis. Trends Immunol. 2004, 25: 201-209. 10.1016/j.it.2004.02.006.View ArticlePubMedGoogle Scholar
  14. Szpaderska AM, DiPietro LA: Inflammation in surgical wound healing: friend or foe?. Surgery. 2005, 137: 571-573. 10.1016/j.surg.2005.01.006.View ArticlePubMedGoogle Scholar
  15. Chen W, Fu X, Sun X, Sun T, Zhao Z, Sheng Z: Analysis of differentially expressed genes in keloids and normal skin with cDNA microarray. J Surg Res. 2003, 113: 208-216. 10.1016/S0022-4804(03)00188-4.View ArticlePubMedGoogle Scholar
  16. Na GY, Seo SK, Lee SJ, Kim DW, Kim MK, Kim JC: Upregulation of the NNP-1 (novel nuclear protein-1, D21S2056E) gene in keloid tissue determined by cDNA microarray and in situ hybridization. Br J Dermatol. 2004, 151: 1143-1149. 10.1111/j.1365-2133.2004.06284.x.View ArticlePubMedGoogle Scholar
  17. Q Pan, X Luo, L Liu, N Chegini: Genomic and proteomic profiling I: Leiomyomas in African Americans and Caucasians. Reprod Biol Endocrinol. 2007, 5 (1): 34-10.1186/1477-7827-5-34.View ArticleGoogle Scholar
  18. Hinz B, Phan SH, Thannickal VJ, Galli A, Bochaton-Piallat ML, Gabbiani G: The Myofibroblast. One Function, Multiple Origins. Am J Pathol. 2007, 170 (6): 1807-1816. 10.2353/ajpath.2007.070112.PubMed CentralView ArticlePubMedGoogle Scholar
  19. Stramer BM, Mori R, Martin P: The inflammation-fibrosis link? A Jekyll and Hyde role for blood cells during wound repair. J Invest Dermatol. 2007, 127: 1009-1017. 10.1038/sj.jid.5700811.View ArticlePubMedGoogle Scholar
  20. Wallace WA, Fitch PM, Simpson AJ, Howie SE: Inflammation-associated remodelling and fibrosis in the lung - a process and an end point. Int J Exp Pathol. 2007, 88: 103-110. 10.1111/j.1365-2613.2006.00515.x.PubMed CentralView ArticlePubMedGoogle Scholar
  21. Wynn TA: Common and unique mechanisms regulate fibrosis in various fibroproliferative diseases. J Clin Invest. 2007, 117: 524-529. 10.1172/JCI31487.PubMed CentralView ArticlePubMedGoogle Scholar
  22. Goicoechea S, Arneman D, Disanza A, Garcia-Mata R, Scita G, Otey CA: Palladin binds to Eps8 and enhances the formation of dorsal ruffles and podosomes in vascular smooth muscle cells. J Cell Sci. 2006, 119: 3316-3324. 10.1242/jcs.03076.View ArticlePubMedGoogle Scholar
  23. Ronty MJ, Leivonen SK, Hinz B, Rachlin A, Otey CA, Kahari VM, Carpen OM: Isoform-specific regulation of the actin-organizing protein palladin during TGF-beta1-induced myofibroblast differentiation. J Invest Dermatol. 2006, 126: 2387-2396. 10.1038/sj.jid.5700427.View ArticlePubMedGoogle Scholar
  24. Satish L, Lyons-Weiler J, Hebda PA, Wells A: Gene expression patterns in isolated keloid fibroblasts. Wound Repair Regen. 2006, 14: 463-470. 10.1111/j.1743-6109.2006.00135.x.View ArticlePubMedGoogle Scholar
  25. Nair RR, Solway J, Boyd DD: Expression cloning identifies transgelin (SM22) as a novel repressor of 92-kDa type IV collagenase (MMP-9) expression. J Biol Chem. 2006, 281: 26424-26436. 10.1074/jbc.M602703200.View ArticlePubMedGoogle Scholar
  26. Luo X, Ding L, Chegini N: CCNs, fibulin-1C and S100A4 expression in leiomyoma and myometrium: inverse association with TGF-beta and regulation by TGF-beta in leiomyoma and myometrial smooth muscle cells. Mol Hum Reprod. 2006, 12: 245-256. 10.1093/molehr/gal015.View ArticlePubMedGoogle Scholar
  27. Chirco R, Liu XW, Jung KK, Kim HR: Novel functions of TIMPs in cell signaling. Cancer Metastasis Rev. 2006, 25: 99-113. 10.1007/s10555-006-7893-x.View ArticlePubMedGoogle Scholar
  28. Davis GE, Saunders WB: Molecular balance of capillary tube formation versus regression in wound repair: role of matrix metalloproteinases and their inhibitors. J Investig Dermatol Symp Proc. 2006, 11: 44-56. 10.1038/sj.jidsymp.5650008.View ArticlePubMedGoogle Scholar
  29. Genis L, Galvez BG, Gonzalo P, Arroyo AG: MT1-MMP: universal or particular player in angiogenesis?. Cancer Metastasis Rev. 2006, 25: 77-86. 10.1007/s10555-006-7891-z.View ArticlePubMedGoogle Scholar
  30. Mauviel A: Transforming growth factor-beta: a key mediator of fibrosis. Methods Mol Med. 2005, 117: 69-80.PubMedGoogle Scholar
  31. Nagase H, Visse R, Murphy G: Structure and function of matrix metalloproteinases and TIMPs. Cardiovasc Res. 2006, 69: 562-573. 10.1016/j.cardiores.2005.12.002.View ArticlePubMedGoogle Scholar
  32. VanSaun MN, Matrisian LM: Matrix metalloproteinases and cellular motility in development and disease. Birth Defects Res C Embryo Today. 2006, 78: 69-79. 10.1002/bdrc.20061.View ArticlePubMedGoogle Scholar
  33. Saarialho-Kere U, Kerkela E, Jahkola T, Suomela S, Keski-Oja J, Lohi J: Epilysin (MMP-28) expression is associated with cell proliferation during epithelial repair. J Invest Dermatol. 2002, 119: 14-21. 10.1046/j.1523-1747.2002.01790.x.View ArticlePubMedGoogle Scholar
  34. Ahonen M, Poukkula M, Baker AH, Kashiwagi M, Nagase H, Eriksson JE, Kahari VM: Tissue inhibitor of metalloproteinases-3 induces apoptosis in melanoma cells by stabilization of death receptors. Oncogene. 2003, 22: 2121-2134. 10.1038/sj.onc.1206292.View ArticlePubMedGoogle Scholar
  35. Lambert E, Dasse E, Haye B, Petitfrere E: TIMPs as multifacial proteins. Crit Rev Oncol Hematol. 2004, 49: 187-198. 10.1016/j.critrevonc.2003.09.008.View ArticlePubMedGoogle Scholar
  36. Leask A, Abraham DJ: TGF-beta signaling and the fibrotic response. FASEB J. 2004, 18: 816-827. 10.1096/fj.03-1273rev.View ArticlePubMedGoogle Scholar
  37. Chegini N, Luo X, Ding L, Ripley D: The expression of Smads and transforming growth factor beta receptors in leiomyoma and myometrium and the effect of gonadotropin releasing hormone analogue therapy. Mol Cell Endocrinol. 2003, 209: 9-16. 10.1016/j.mce.2003.08.007.View ArticlePubMedGoogle Scholar
  38. Sampath D, Zhu Y, Winneker RC, Zhang Z: Aberrant expression of Cyr61, a member of the CCN (CTGF/Cyr61/Cef10/NOVH) family, and dysregulation by 17 beta-estradiol and basic fibroblast growth factor in human uterine leiomyomas. J Clin Endocrinol Metab. 2001, 86: 1707-1715. 10.1210/jc.86.4.1707.PubMedGoogle Scholar
  39. Lee CG, Cho SJ, Kang MJ, Chapoval SP, Lee PJ, Noble PW, Yehualaeshet T, Lu B, Flavell RA, Milbrandt J, Homer RJ, Elias JA: Early growth response gene 1-mediated apoptosis is essential for transforming growth factor beta1-induced pulmonary fibrosis. J Exp Med. 2004, 200: 377-389. 10.1084/jem.20040104.PubMed CentralView ArticlePubMedGoogle Scholar
  40. Miyazono K, Maeda S, Imamura T: Coordinate regulation of cell growth and differentiation by TGF-beta superfamily and Runx proteins. Oncogene. 2004, 23: 4232-4237. 10.1038/sj.onc.1207131.View ArticlePubMedGoogle Scholar
  41. Thiel G, Cibelli G: Regulation of life and death by the zinc finger transcription factor Egr-1. J Cell Physiol. 2002, 193: 287-292. 10.1002/jcp.10178.View ArticlePubMedGoogle Scholar
  42. Tien ES, Davis JW, Vanden Heuvel JP: Identification of the CREB-binding protein/p300-interacting protein CITED2 as a peroxisome proliferator-activated receptor alpha coregulator. J Biol Chem. 2004, 279: 24053-24063. 10.1074/jbc.M401489200.View ArticlePubMedGoogle Scholar

Copyright

© Luo et al; licensee BioMed Central Ltd. 2007

This article is published under license to 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.

Advertisement