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DOI: 10.1055/a-1894-6817
Genes Relating to Biological Processes of Endometriosis: Expression Changes Common to a Mouse Model and Patients
Abstract
Endometriosis is one of the most common gynecological diseases in women of reproductive age. Retrograde menstruation is considered a major reason for the development of endometriosis. The syngeneic transplantation mouse model is an endometriosis animal model that is considered to mimic retrograde menstruation. However, it remains poorly understood which genetic signatures of endometriosis are reflected in this model. Here, we employed an in vivo syngeneic mouse endometriosis model and identified differentially expressed genes (DEGs) between the ectopic and eutopic tissues using microarray analysis. Three gene expression profile datasets, GSE5108, GSE7305, and GSE11691, were downloaded from the Gene Expression Omnibus database and DEGs between ectopic and eutopic tissues from the same patients were identified. Gene ontology analysis of the DEGs revealed that biological processes including cell adhesion, the inflammatory response, the response to mechanical stimulus, cell proliferation, and extracellular matrix organization were enriched in both the model and patients. Of the 195 DEGs common to the model and patients, 154 showed the same expression pattern, and 28 of these 154 DEGs came up when PubMed was searched for each gene along with the terms “endometriosis” and “development”. This is the first comparison of the DEGs of the mouse syngeneic endometriosis model and those of patients, and we identified the biological processes common to the model and patients at the transcriptional level. This model may be useful to evaluate the efficacy of drugs which target these biological processes.
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Key words
endometriosis - mouse syngeneic model - DNA microarray - patients - differentially expressed genes - biological processIntroduction
Endometriosis is one of the most common gynecological diseases in women of reproductive age, and it is diagnosed in about 5% to 10% of women during their reproductive years, which is approximately 176 million women in the world [1]. Endometriosis is defined as the presence of endometrial-like lesions outside the uterus, primarily in the peritoneum, ovaries, bowel, uterosacral ligaments, and fallopian tubes, which has a great impact on quality of life [2]. The combined oral contraceptive pill and progestogens are widely used as therapies for endometriosis [3]. Although they are effective for some symptoms of endometriosis such as pain, they are not a complete therapy; some patients show recurrence of the disease after withdrawal of the therapy and one-third of patients are non-responders due to progesterone resistance [4]. Thus, new therapeutic options which have a mechanism of action that is different from that of hormonal drugs and which act on endometriotic lesions are desirable for the treatment of endometriosis.
To achieve this goal, the extrapolation of information from animal models to humans is essential; however, extrapolation is complicated because rodents do not develop endometriosis spontaneously [5]. Among the several rodent models available, the syngeneic mouse model is often used because it is considered to mimic retrograde menstruation [6], which is one of the main causes of the development of endometriosis [7]. However, few studies have comprehensively compared the biological processes of endometriosis in patients and in the model, and the usefulness of this animal model in the interpretation of the pathophysiology of endometriosis in humans is not yet fully understood.
In recent years, transcriptome analysis has been one of the technologies most utilized to study human diseases at the gene expression level, and it has contributed to the development of data integration approaches to discover molecular biomarkers in human pathologies and targets for new drugs [8]. Therefore, in the present study, we employed a syngeneic mouse endometriosis model and used transcriptome analysis to investigate the differentially expressed genes and the biological processes common to the model and endometriosis patients.
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Materials and Methods
Animals
Seven-week-old female BALB/cCrSlc mice (n=65) were purchased from Japan SLC Inc. (Hamamatsu, Japan). The mice were housed under conditions of controlled temperature (20–26°C), humidity (35–75%), and lighting (12-h light/dark cycle) with water and food ad libitum. The study was conducted in compliance with the Internal Regulations on Animal Experiments at Nippon Shinyaku Co., Ltd. (Kyoto, Japan), which are based on the Law for the Humane Treatment and Management of Animals (Law No. 105, October 1, 1973).
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Ovariectomy and mouse model of endometriosis
Eight-week-old mice were anesthetized with Isoflurane Inhalation Solution [Pfizer] (Mylan Inc., Canonsburg, Pennsylvania, USA). The mice were ovariectomized through bilateral paravertebral incisions, and the muscular and skin incisions were closed with 6–0 black silk suture. Butorphanol tartrate (1 mg/kg; Fujifilm Wako Pure Chemical Co., Osaka, Japan) and ampicillin sodium (100 mg/kg; Viccillin; Meiji Seika Pharma Co., Ltd., Tokyo, Japan) were administered subcutaneously. At the end of the procedure, estradiol valerate in sesame oil (2 μg/animal) was administered intramuscularly every week to all mice. The day of ovariectomy was designated as day 0. On day 7, the mice were divided into three groups by their body weight: 10 mice in the sham group, 14 mice in the donor group, and 28 mice in the recipient group. To construct the syngeneic mouse endometriosis model, uterine tissues from the donor mice were harvested and minced into small cell aggregates in Medium 199 with Hanks’ Balanced Salts (Thermo Fisher Scientific, Inc., Waltham, Massachusetts, USA) supplemented with penicillin-streptomycin mixed solution (Nacalai Tesque Inc., Kyoto, Japan), then equal volumes of uterine cell suspension were transferred into the peritoneal cavities of the recipient mice at a ratio of one donor to two recipients. For the sham group, the same volume of Medium 199 with Hanks’ Balanced Salts was injected into the peritoneal cavities of the mice. To reduce the local surgical response to trauma, we incised the upper right side of mice and transferred the uterine cell suspension into their lower left peritoneal cavities through the indwelling needle. The wounds of the mice were closed with 6–0 black silk suture and bupivacaine hydrochloride hydrate (2.5 mg/kg; Marcaine Injection; Aspen Japan Co., Ltd., Tokyo, Japan) and ampicillin sodium (100 mg/kg) were administered subcutaneously. On day 35, the recipient mice were euthanized and all ectopic cysts and uterine tissues were carefully and exclusively removed from each mouse with a small scissors and forceps, infused with RNAlater solution (Thermo Fisher Scientific, Inc.) and stored at −80°C for analysis of gene expression.
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Microarray analysis
Total RNA were isolated from the mouse ectopic cystic tissue and eutopic uterus using an RNeasy Lipid Tissue Mini Kit (Qiagen Inc., Hilden, Germany) (n=5 animals per group). The quality and concentration of the RNA was checked using an Agilent 2100 bioanalyzer. The RNA Integrity Number (RIN) was used to evaluate RNA integrity and all samples used for the microarray analysis had RIN ≥7.0. Purified RNA was labeled by using the GeneChip WT Plus Reagent Kit (Thermo Fisher Scientific, Inc.), then hybridized to a Clariom S Mouse Array (Thermo Fisher Scientific, Inc.) according to the manufacturer’s instructions. Experiments from RNA isolation to microarray analysis were conducted at Filgen, Inc. (Nagoya, Japan). Briefly, CEL files were processed using Affymetrix Expression Console software (Thermo Fisher Scientific, Inc.) and subjected to normalization using the Signal Space Transformation-Robust Multiarray Analysis (SST-RMA) method for the following analysis. The number of probes detected was 22,206 and genes whose expression changed at least two-fold with p<0.05 (Student’s t-test) in the ectopic cystic tissue compared to the eutopic tissue in the syngeneic endometriosis mouse model or in the eutopic tissue in the model compared to the sham group were considered to be differentially expressed. Gene ontology (GO) analysis was conducted on the significantly differentially expressed genes (DEGs) using the Database for Annotation, Visualization and Integrated Discovery [9] (DAVID; Laboratory of Human Retrovirology and Immunoinformatics). GO terms for biological processes with p<0.05 (Fisher’s exact test with the Benjamini-Hochberg multiple-testing correction) were considered significant. The datasets are available from the National Center for Biotechnology Information/Gene Expression Omnibus, and can be accessed with GSE190209.
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Endometriosis patient data collection
The BaseSpace Correlation Engine (Illumina, Inc., San Diego, California, USA) bioinformatics database was used to investigate the microarray gene expression profiles of the endometriosis patients, in which data were reanalyzed as determined by NextBio analysis [10]. We found three datasets (GSE5108 [11], GSE7305 [12] and GSE11691 [13]) in which the gene expression in ectopic tissue is compared to that in eutopic tissue from the same patients.
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Analysis of DEGs from patient datasets
The files from the three datasets were individually processed and normalized according to the BaseSpace Correlation Engine platform, and genes whose expression changed in ectopic tissue at least two-fold compared to eutopic tissue with p<0.05 were considered to be the DEGs of each dataset. The genes which showed the same expression pattern (up-regulated or down-regulated) in at least two datasets were defined as the DEGs of the endometriosis patients. GO analysis was conducted on the DEGs of patients using DAVID. GO terms for biological processes with p<0.05 (Fisher’s exact test with the Benjamini-Hochberg multiple-testing correction) were considered significant.
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Comparison of data between the syngeneic mouse endometriosis model and patients
The data for the GO analysis of the syngeneic mouse endometriosis model were combined with those of the patients, then GO terms common to them were identified using TIBCO Spotfire data analysis software (TIBCO Software Inc., Palo Alto, California, USA). The DEGs common to the model and patients were identified using the BaseSpace Correlation Engine. To investigate the relationship between each common DEG and endometriosis, PubMed (National Center for Biotechnology Information) was searched for each common DEG along with the terms “endometriosis” or “endometriosis” and “development”. Studies on genes which were not shown to be associated with endometriosis in patients (e. g., studies in animal models only or on endometriosis-associated ovarian carcinoma) were excluded.
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Results
DEGs in the syngeneic mouse endometriosis model
We used DNA microarray analysis to identify the changes in gene expression in the syngeneic mouse endometriosis model. Seventy-seven out of 22,206 genes were differentially expressed in the eutopic uterus of the model compared to that of sham-operated mice, comprising 54 up-regulated and 23 down-regulated genes, hereinafter referred to as the DEGs in the eutopic uterus ([Fig. 1a]). We then investigated the DEGs in the ectopic cystic tissue of the model mice compared to those in their eutopic uteri. We identified 1,154 out of 22,206 genes as DEGs, comprising 742 up-regulated and 412 down-regulated genes, and these are hereinafter referred to as the DEGs in ectopic tissue ([Fig. 1b]). These results show that the expression of some genes was different between the eutopic and ectopic tissues of the model mice.
![](https://www.thieme-connect.de/media/drugres/202209/thumbnails/10-1055-a-1894-6817-i2022-06-2537-0001.jpg)
![Zoom Image](/products/assets/desktop/css/img/icon-figure-zoom.png)
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DEGs in the endometriosis patients of three datasets from NCBI GEO
We identified DEGs in the endometriosis patients using three datasets from NCBI GEO in which the gene expression between eutopic and ectopic lesions from the endometriosis patients was compared using microarray analysis. We identified 2633 genes in GSE5108, 3787 in GSE7305, and 494 in GSE11691. Of these, 950 genes showed the same expression pattern in at least two datasets and were defined as the DEGs common to the patients. They comprised 530 up-regulated and 420 down-regulated genes ([Fig. 2]).
![](https://www.thieme-connect.de/media/drugres/202209/thumbnails/10-1055-a-1894-6817-i2022-06-2537-0002.jpg)
![Zoom Image](/products/assets/desktop/css/img/icon-figure-zoom.png)
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GO analysis of DEGs in the mouse model and endometriosis patients
To find biological processes associated with the DEGs, we used gene ontology (GO) analysis. We found that DEGs in the eutopic uterus of the model mice represented the enrichment of two biological processes, the response to lipopolysaccharide and neutrophil chemotaxis ([Table 1]). The DEGs in the ectopic tissue of the model mice represented the enrichment of 75 biological processes, including muscle contraction, cell adhesion, response to hypoxia, and the inflammatory response (Supplementary Table 1). The DEGs in the patients represented the enrichment of 28 biological processes, including extracellular matrix organization, cell adhesion, and the inflammatory response (Supplementary Table 2). We then matched GO terms which were enriched both in the ectopic tissue of the model mice and in the patients, and found that 12 biological processes were common to them ([Table 2] and [Fig. 3]), including cell adhesion, the inflammatory response, the response to mechanical stimulus, cell proliferation and extracellular matrix organization. This result suggests that these biological processes are important in both the model and patients.
![](https://www.thieme-connect.de/media/drugres/202209/thumbnails/10-1055-a-1894-6817-i2022-06-2537-0003.jpg)
![Zoom Image](/products/assets/desktop/css/img/icon-figure-zoom.png)
GO Term |
Count |
p-value |
|
---|---|---|---|
GO:0032496 |
response to lipopolysaccharide |
7 |
0.03 |
GO:0030593 |
neutrophil chemotaxis |
5 |
0.03 |
GO term |
Mouse model |
Endometriosis patients |
|||
---|---|---|---|---|---|
Gene Count |
p-value |
Gene Count |
p-value |
||
GO:0007155 |
cell adhesion |
74 |
3.2.E-11 |
60 |
7.0.E-08 |
GO:0006954 |
inflammatory response |
51 |
6.7.E-07 |
46 |
1.1.E-04 |
GO:0009612 |
response to mechanical stimulus |
17 |
9.9.E-05 |
12 |
3.0.E-02 |
GO:0008285 |
negative regulation of cell proliferation |
47 |
3.6.E-04 |
38 |
3.8.E-02 |
GO:0030198 |
extracellular matrix organization |
22 |
3.8.E-04 |
37 |
5.1.E-08 |
GO:0043627 |
response to estrogen |
17 |
7.7.E-04 |
12 |
5.0.E-02 |
GO:0001525 |
angiogenesis |
33 |
1.0.E-03 |
29 |
3.1.E-03 |
GO:0045766 |
positive regulation of angiogenesis |
21 |
1.8.E-03 |
20 |
2.2.E-03 |
GO:0007568 |
aging |
24 |
1.1.E-02 |
21 |
3.8.E-02 |
GO:0006955 |
immune response |
32 |
1.4.E-02 |
45 |
2.2.E-03 |
GO:0070098 |
chemokine-mediated signaling pathway |
12 |
1.8.E-02 |
13 |
3.5.E-02 |
GO:0048247 |
lymphocyte chemotaxis |
9 |
2.6.E-02 |
8 |
5.0.E-02 |
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DEGs common to the syngeneic mouse endometriosis model and endometriosis patients
To identify gene-expression changes common to the model and the patients, we compared the DEGs between them. We found that they shared 195 DEGs, of which 154 showed the same expression pattern (that is, 115 genes were up-regulated and 39 were down-regulated in both the model and the patients; [Table 3] and [Fig. 4]). We defined these 154 genes as the DEGs common to the model and the patients. We then explored the gene annotations of the common DEGs, and found that some of them were annotated by GO terms which were enriched in both the model and patients ([Table 4]).
![](https://www.thieme-connect.de/media/drugres/202209/thumbnails/10-1055-a-1894-6817-i2022-06-2537-0004.jpg)
![Zoom Image](/products/assets/desktop/css/img/icon-figure-zoom.png)
Gene |
Description |
Fold changein model |
Fold change in patients (average of 3 datasets) |
---|---|---|---|
up-regulated genes |
|||
Hp |
Haptoglobin |
468.70 |
9.18 |
Cfd |
complement factor D (adipsin) |
405.34 |
6.15 |
Fabp4 |
fatty acid binding protein 4, adipocyte |
280.26 |
30.81 |
Hspb6 |
heat shock protein, alpha-crystallin-related, B6 |
144.70 |
2.31 |
Serpina3n |
serine (or cysteine) peptidase inhibitor, clade A, member 3 N |
58.06 |
5.74 |
Cryab |
crystallin, alpha B |
42.87 |
3.25 |
Hsd11b1 |
hydroxysteroid 11-beta dehydrogenase 1 |
41.74 |
20.65 |
Gpnmb |
glycoprotein (transmembrane) nmb |
39.08 |
3.53 |
Ldb3 |
LIM domain binding 3 |
33.46 |
3.86 |
Cpxm2 |
carboxypeptidase X 2 (M14 family) |
31.13 |
16.65 |
Rgs16 |
regulator of G-protein signaling 16 |
30.34 |
2.62 |
Serpine2 |
serine (or cysteine) peptidase inhibitor, clade E, member 2 |
22.80 |
18.67 |
Thbs2 |
thrombospondin 2 |
20.39 |
4.11 |
Lrrc2 |
leucine rich repeat containing 2 |
17.94 |
4.20 |
Filip1l |
filamin A interacting protein 1-like |
17.62 |
3.99 |
Col12a1 |
collagen, type XII, alpha 1 |
16.63 |
5.26 |
Fmod |
Fibromodulin |
15.29 |
2.75 |
Thbs4 |
thrombospondin 4 |
12.82 |
3.89 |
Mgp |
matrix Gla protein |
12.49 |
4.65 |
Timp1 |
tissue inhibitor of metalloproteinase 1 |
12.08 |
5.25 |
Thbs1 |
thrombospondin 1 |
11.39 |
6.86 |
C1qtnf7 |
C1q and tumor necrosis factor related protein 7 |
10.29 |
2.27 |
Itm2a |
integral membrane protein 2 A |
9.54 |
7.11 |
Sfrp2 |
secreted frizzled-related protein 2 |
8.96 |
21.75 |
Il7r |
interleukin 7 receptor |
8.48 |
5.82 |
Slit3 |
slit homolog 3 (Drosophila) |
8.08 |
2.86 |
Itgbl1 |
integrin, beta-like 1 |
7.92 |
4.05 |
Angptl1 |
angiopoietin-like 1 |
7.46 |
13.75 |
Sulf1 |
sulfatase 1 |
7.43 |
3.22 |
Bgn |
Biglycan |
6.91 |
3.43 |
Ghr |
growth hormone receptor |
6.84 |
2.79 |
Inhba |
inhibin beta-A |
6.45 |
8.09 |
Cd163 |
CD163 antigen |
6.37 |
5.35 |
Chl1 |
cell adhesion molecule with homology to L1CAM |
5.96 |
36.95 |
Pdgfrl |
platelet-derived growth factor receptor-like |
5.72 |
3.80 |
Fhl5 |
four and a half LIM domains 5 |
5.64 |
2.58 |
Olfml1 |
olfactomedin-like 1 |
5.54 |
2.55 |
Nupr1 |
nuclear protein 1 |
5.43 |
2.37 |
Rcan2 |
regulator of calcineurin 2 |
5.20 |
8.91 |
Frzb |
frizzled-related protein |
5.04 |
5.21 |
Scn7a |
sodium channel, voltage-gated, type VII, alpha |
4.81 |
37.20 |
Lyz2 |
lysozyme 2 |
4.75 |
4.23 |
Vgll3 |
vestigial like 3 (Drosophila) |
4.62 |
3.04 |
Lhfp |
lipoma HMGIC fusion partner |
4.53 |
3.59 |
Lbh |
limb-bud and heart |
4.52 |
2.50 |
Wisp2 |
WNT1 inducible signaling pathway protein 2 |
4.52 |
13.38 |
Gfpt2 |
glutamine fructose-6-phosphate transaminase 2 |
4.37 |
2.24 |
Msr1 |
macrophage scavenger receptor 1 |
4.36 |
3.90 |
Ctss |
cathepsin S |
4.01 |
2.59 |
C4a |
complement component 4 A (Rodgers blood group) |
3.97 |
7.01 |
Rgs5 |
regulator of G-protein signaling 5 |
3.85 |
3.40 |
Dpysl3 |
dihydropyrimidinase-like 3 |
3.84 |
8.99 |
Prelp |
proline arginine-rich end leucine-rich repeat |
3.80 |
7.90 |
Itgb2 |
integrin beta 2 |
3.65 |
2.42 |
Aspn |
aspirin |
3.60 |
4.09 |
Meox2 |
mesenchyme homeobox 2 |
3.55 |
3.09 |
Cbs |
cystathionine beta-synthase |
3.53 |
2.58 |
Nrp2 |
neuropilin 2 |
3.47 |
8.76 |
Ccdc80 |
coiled-coil domain containing 80 |
3.43 |
8.69 |
S100a6 |
S100 calcium binding protein A6 (calcyclin) |
3.42 |
2.22 |
Folr2 |
folate receptor 2 (fetal) |
3.42 |
2.20 |
Kcnma1 |
potassium large conductance calcium-activated channel, subfamily M, alpha member 1 |
3.42 |
2.55 |
Pdlim5 |
PDZ and LIM domain 5 |
3.36 |
2.71 |
Podn |
Podocan |
3.34 |
4.29 |
Plxdc2 |
plexin domain containing 2 |
3.32 |
2.78 |
Steap4 |
STEAP family member 4 |
3.32 |
4.67 |
Ltbp2 |
latent transforming growth factor beta binding protein 2 |
3.08 |
6.01 |
Spsb1 |
splA/ryanodine receptor domain and SOCS box containing 1 |
3.06 |
2.45 |
Eltd1 |
EGF, latrophilin seven transmembrane domain containing 1 |
2.99 |
2.30 |
Sytl2 |
synaptotagmin-like 2 |
2.96 |
5.78 |
Gpx3 |
glutathione peroxidase 3 |
2.91 |
10.59 |
Hmox1 |
heme oxygenase (decycling) 1 |
2.90 |
4.67 |
Chrdl1 |
chordin-like 1 |
2.88 |
5.43 |
Ncf4 |
neutrophil cytosolic factor 4 |
2.87 |
3.66 |
Loxl1 |
lysyl oxidase-like 1 |
2.85 |
2.76 |
Rarres1 |
retinoic acid receptor responder (tazarotene induced) 1 |
2.78 |
7.20 |
Rerg |
RAS-like, estrogen-regulated, growth-inhibitor |
2.75 |
5.45 |
Sep4 |
septin 4 |
2.75 |
3.94 |
Pdgfd |
platelet-derived growth factor, D polypeptide |
2.71 |
5.77 |
Col14a1 |
collagen, type XIV, alpha 1 |
2.69 |
3.54 |
Nfasc |
Neurofascin |
2.68 |
14.96 |
Tspan7 |
tetraspanin 7 |
2.67 |
2.67 |
Colec12 |
collectin sub-family member 12 |
2.66 |
3.25 |
Igsf6 |
immunoglobulin superfamily, member 6 |
2.65 |
2.96 |
Cdh5 |
cadherin 5 |
2.64 |
2.47 |
Plvap |
plasmalemma vesicle associated protein |
2.57 |
2.96 |
Clu |
Clusterin |
2.55 |
8.12 |
Fry |
furry homolog (Drosophila) |
2.55 |
3.56 |
Chi3l1 |
chitinase 3-like 1 |
2.55 |
9.68 |
Fcgr3 |
Fc receptor, IgG, low affinity III |
2.54 |
5.88 |
Itga7 |
integrin alpha 7 |
2.53 |
3.01 |
Man1c1 |
mannosidase, alpha, class 1 C, member 1 |
2.52 |
3.40 |
Dkk3 |
dickkopf homolog 3 (Xenopus laevis) |
2.51 |
3.51 |
Tril |
TLR4 interactor with leucine-rich repeats |
2.50 |
3.49 |
Pros1 |
protein S (alpha) |
2.48 |
6.98 |
Fcgr2b |
Fc receptor, IgG, low affinity IIb |
2.44 |
3.29 |
Jam2 |
junction adhesion molecule 2 |
2.44 |
2.92 |
Ccr1 |
chemokine (C-C motif) receptor 1 |
2.42 |
2.48 |
Grk5 |
G protein-coupled receptor kinase 5 |
2.26 |
2.93 |
Pde1a |
phosphodiesterase 1 A, calmodulin-dependent |
2.26 |
3.38 |
Npl |
N-acetylneuraminate pyruvate lyase |
2.25 |
4.02 |
Ptprb |
protein tyrosine phosphatase, receptor type, B |
2.25 |
2.54 |
Serping1 |
serine (or cysteine) peptidase inhibitor, clade G, member 1 |
2.20 |
5.47 |
Gpr116 |
G protein-coupled receptor 116 |
2.14 |
3.21 |
Nr4a1 |
nuclear receptor subfamily 4, group A, member 1 |
2.13 |
2.31 |
Fst |
Follistatin |
2.11 |
6.28 |
Cpa3 |
carboxypeptidase A3, mast cell |
2.08 |
2.87 |
Aox1 |
aldehyde oxidase 1 |
2.08 |
17.10 |
Gnb4 |
guanine nucleotide binding protein (G protein), beta 4 |
2.08 |
2.36 |
Cd22 |
CD22 antigen |
2.07 |
3.19 |
Nuak1 |
NUAK family, SNF1-like kinase, 1 |
2.05 |
3.74 |
Gpc6 |
glypican 6 |
2.03 |
3.29 |
9430020K01Rik |
RIKEN cDNA 9430020K01 gene |
2.02 |
3.09 |
C7 |
complement component 7 |
2.02 |
73.71 |
Laptm5 |
lysosomal-associated protein transmembrane 5 |
2.01 |
2.94 |
down-regulated genes |
|||
Hsd11b2 |
hydroxysteroid 11-beta dehydrogenase 2 |
−8.06 |
−5.61 |
Mogat1 |
monoacylglycerol O-acyltransferase 1 |
−7.94 |
−4.09 |
Kcnip4 |
Kv channel interacting protein 4 |
−6.37 |
−4.81 |
Gcnt3 |
glucosaminyl (N-acetyl) transferase 3, mucin type |
−5.21 |
−2.26 |
Car12 |
carbonic anyhydrase 12 |
−5.21 |
−6.39 |
Slc15a2 |
solute carrier family 15 (H+/peptide transporter), member 2 |
−4.27 |
−4.48 |
Pgbd5 |
piggyBac transposable element derived 5 |
−3.38 |
−4.45 |
Crabp2 |
cellular retinoic acid binding protein II |
−3.28 |
−6.77 |
Mme |
membrane metallo endopeptidase |
−3.28 |
−4.71 |
Ckb |
creatine kinase, brain |
−3.27 |
−3.01 |
Krt8 |
keratin 8 |
−3.23 |
−3.75 |
Krt19 |
keratin 19 |
−3.19 |
−3.69 |
Tfcp2l1 |
transcription factor CP2-like 1 |
−3.13 |
−3.44 |
Tspan13 |
tetraspanin 13 |
−3.01 |
−3.43 |
Galnt4 |
UDP-N-acetyl-alpha-D-galactosamine:polypeptide N-acetylgalactosaminyltransferase 4 |
−2.93 |
−11.19 |
Agr2 |
anterior gradient 2 (Xenopus laevis) |
−2.85 |
−11.16 |
Fam174b |
family with sequence similarity 174, member B |
−2.71 |
−2.27 |
Galnt3 |
UDP-N-acetyl-alpha-D-galactosamine:polypeptide N-acetylgalactosaminyltransferase 3 |
−2.71 |
−2.45 |
Rorb |
RAR-related orphan receptor beta |
−2.66 |
−7.46 |
Tspan1 |
tetraspanin 1 |
−2.53 |
−4.88 |
Gpsm2 |
G-protein signalling modulator 2 (AGS3-like, C. elegans) |
−2.51 |
−2.90 |
Aldh1a2 |
aldehyde dehydrogenase family 1, subfamily A2 |
−2.49 |
−9.64 |
Prr15 |
proline rich 15 |
−2.44 |
−7.56 |
Rasef |
RAS and EF hand domain containing |
−2.36 |
−2.79 |
Esr1 |
estrogen receptor 1 (alpha) |
−2.34 |
−7.53 |
Rev3l |
REV3-like, catalytic subunit of DNA polymerase zeta RAD54 like (S. cerevisiae) |
−2.34 |
−3.11 |
Ptn |
Pleiotrophin |
−2.31 |
−3.03 |
Tmem30b |
transmembrane protein 30B |
−2.29 |
−4.84 |
Cd24a |
CD24a antigen |
−2.26 |
−22.91 |
Qpct |
glutaminyl-peptide cyclotransferase (glutaminyl cyclase) |
−2.25 |
−4.16 |
Cndp2 |
CNDP dipeptidase 2 (metallopeptidase M20 family) |
−2.20 |
−3.14 |
Wfdc2 |
WAP four-disulfide core domain 2 |
−2.20 |
−10.44 |
Stxbp6 |
syntaxin binding protein 6 (amisyn) |
−2.16 |
−9.21 |
Rab25 |
RAB25, member RAS oncogene family |
−2.15 |
−5.87 |
Llgl2 |
lethal giant larvae homolog 2 (Drosophila) |
−2.14 |
−2.27 |
Npr2 |
natriuretic peptide receptor 2 |
−2.14 |
−2.80 |
Ppap2c |
phosphatidic acid phosphatase type 2 C |
−2.08 |
−4.03 |
Irf6 |
interferon regulatory factor 6 |
−2.04 |
−5.03 |
Gjb6 |
gap junction protein, beta 6 |
−2.00 |
−5.75 |
GO term |
genes |
|
---|---|---|
cell adhesion |
17 |
Gpnmb, Thbs2, Col12a1, Thbs4, Thbs1, Sulf1, Chl1, Wisp2, Itgb2, Col14a1, Nfasc, Cdh5, Itga7, Cd22, Nuak1, 9430020K01Rik, Cd24a |
inflammatory response |
6 |
Thbs1, Cd163, C4a, Chi3l1, Tril, Ccr1 |
response to mechanical stimulus |
2 |
Thbs1, Chi3l1 |
negative regulation of cell proliferation |
13 |
Serpine2, Sfrp2, Slit3, Inhba, Frzb, Wisp2, Podn, Hmox1, Rerg, Cdh5, Aldh1a2, Irf6, Gjb6 |
extracellular matrix organization |
1 |
Ccdc80 |
response to estrogen |
5 |
Kcnma1, Hmox1, Krt19, Esr1, Cd24a |
Angiogenesis |
5 |
Meox2, Nrp2, Ccdc80, Hmox1, Ptprb |
positive regulation of angiogenesis |
5 |
Thbs1, Sfrp2, Itgb2, Hmox1, Chi3l1 |
Aging |
5 |
Cryab, Timp1, Itgb2, Serping1, Gjb6 |
immune response |
7 |
Thbs1, Ctss, Colec12, Fcgr2b, Ccr1, C7, Cd24a |
chemokine-mediated signaling pathway |
1 |
Ccr1 |
#
The roles of DEGs common to the syngeneic mouse endometriosis model and endometriosis patients in endometriosis
To investigate possible roles played by the DEGs common to the model and the patients, we searched for a relationship between the common DEGs and endometriosis by using PubMed. When we searched for each gene along with the term “endometriosis”, 52 of 154 genes came up (Supplementary Table 3 and [Fig. 5]). When we searched for each gene along with the terms “endometriosis” and “development”, 23 genes came up that had some association with endometriosis in patients ([Table 5]).
![](https://www.thieme-connect.de/media/drugres/202209/thumbnails/10-1055-a-1894-6817-i2022-06-2537-0005.jpg)
![Zoom Image](/products/assets/desktop/css/img/icon-figure-zoom.png)
Gene |
Number of publications |
Reference lists |
---|---|---|
up-regulated genes |
||
Hp |
2 |
Piva M et al., Glycoconj J. 2002 Jan;19(1):33–41. Sharpe-Timms KL et al., Hum Reprod. 2000 Oct;15(10):2180–5. |
Hsd11b1 |
1 |
Zhen Lin et al., J Food Biochem. 2021 May;45(5):e13717. |
Timp1 |
6 |
Luddi A et al., Int J Mol Sci. 2020 Apr 18;21(8):2840.Szymanowski K et al.,Ann Agric Environ Med. 2016 Dec 23;23(4):649–653. Stilley JA et al., Biol Reprod. 2010 Aug 1;83(2):185–94. Collette T et al.,Hum Reprod. 2006 Dec;21(12):3059–67. Li Y et al., Zhonghua Fu Chan Ke Za Zhi. 2006 Jan;41(1):30–3. Collette T et al., Hum Reprod. 2004 Jun;19(6):1257–64. |
Thbs1 |
3 |
Liu Y et al., Am J Reprod Immunol. 2020 Jun;83(6):e13236. Gilabert-Estellés J et al., Hum Reprod. 2007 Aug;22(8):2120–7. Tan XJ et al., Fertil Steril. 2002 Jul;78(1):148–53. |
Slit3 |
1 |
Greaves E et al., Endocrinology. 2014 Oct;155(10):4015–26. |
Inhba |
1 |
Lin J et al., Mol Hum Reprod. 2011 Oct;17(10):605–11. |
Cd163 |
3 |
Kusunoki M et al., Med Mol Morphol. 2021 Jun;54(2):122–132. Krasnyi AM et al., Biomed Khim. 2019 Aug;65(5):432–436. Itoh F et al., Fertil Steril. 2013 May;99(6):1705–13. |
Chl1 |
2 |
Jiang L et al., Int J Immunopathol Pharmacol. 2020 Jan-Dec;34:2058738420976309. Zhang C et al., Eur J Obstet Gynecol Reprod Biol. 2019 May;236:177–182. |
Prelp |
1 |
Araujo FM et al., Braz J Med Biol Res. 2017 Jul 3;50(7):e5782. |
Itgb2 |
1 |
Sundqvist J et al., Hum Reprod. 2012 Sep;27(9):2737–46. |
S100a6 |
1 |
Peng Y et al., Gynecol Endocrinol. 2018 Sep;34(9):815–820. |
Gpx3 |
1 |
Mirza Z et al., Diagnostics (Basel) . 2020 Jun 19;10(6):416. |
Hmox1 |
2 |
Van LA et al., Fertil Steril. 2002 Mar;77(3):561–70. Imanaka S et al., Arch Med Res. 2021 Aug;52(6):641–647. |
Fcgr3 |
1 |
Mei J et al., Autophagy. 2018;14(8):1376–1397. |
Ccr1 |
3 |
Li T et al., Biomed Pharmacother. 2020 Sep;129:110476. Trummer D et al., Acta Obstet Gynecol Scand. 2017 Jun;96(6):694–701. Kyama CM et al., Curr Med Chem. 2008;15(10):1006–17. |
Nr4a1 |
1 |
Qingdong Z et al., Cell Physiol Biochem. 2018;45(3):1172–1190. |
Fst |
2 |
Kimber-Trojnar Ż et al., J Clin Med. 2021 Jun 23;10(13):2762. Luisi S et al., Womens Health (Lond). 2015 Aug;11(5):603–10. |
down-regulated genes |
||
Crabp2 |
1 |
Sokalska A et al., J Clin Endocrinol Metab. 2013 Mar;98(3):E463–71. |
Krt19 |
1 |
Konrad L et al., Reprod Sci. 2019 Jan;26(1):49–59. |
Aldh1a2 |
1 |
Jiang Y et al., J Endocrinol. 2018 Mar;236(3):R169-R188. |
Esr1 |
18 |
Wang J etal., Clin Lab. 2020 Aug 1;66(8). Huang ZX et al., J Cell Mol Med. 2020 Sep;24(18):10693–10704. Gibson DA et al., J Endocrinol. 2020 Sep;246(3):R75-R93. Chantalat E et al., Int J Mol Sci. 2020 Apr 17;21(8):2815. Tang ZR et al., Cells. 2019 Sep 21;8(10):1123. Yilmaz BD et al., Hum Reprod Update. 2019 Jul 1;25(4):473–485. Osiński M et al., Ginekol Pol. 2018;89(3):125–134. Sapkota Y et al., Nat Commun. 2017 May 24;8:15539. Hamilton KJ et al., Curr Top Dev Biol. 2017;125:109–146. Xiong W et al., Reproduction. 2015 Dec;150(6):507–16 Zhang Q et al., Gynecol Obstet Invest. 2015;80(3):187–92. Huang PC et al., Environ Sci Pollut Res Int. 2014 Dec;21(24):13964–73. Wang W et al., Reprod Biomed Online. 2013 Jan;26(1):93–8 Li Y et al., Gene. 2012 Oct 15;508(1):41–8. Veillat V et al., Am J Pathol. 2012 Sep;181(3):917–27. Matsuzaka Y et al., Environ Health Prev Med. 2012 Sep;17(5):423–8. Athanasios F et al., Arch Gynecol Obstet. 2012 Apr;285(4):1001–7. Smuc T et al., Mol Cell Endocrinol. 2009 Mar 25;301(1–2):59–64. |
Cd24a |
1 |
Sundqvist J et al., Hum Reprod. 2012 Sep;27(9):2737–46. |
Wfdc2 |
1 |
Chen T et al., J Clin Lab Anal. 2021 Sep;35(9):e23947. |
#
#
Discussion
In the present study, we found that biological processes including cell adhesion, the inflammatory response, the response to mechanical stimulus, cell proliferation, extracellular matrix organization (ECM), and the estrogen response were enriched in both the model and patients. We found that thrombospondin 1 (Thbs1), tissue inhibitor of metalloproteinase 1 (Timp1), and cell adhesion molecule with homology to L1CAM (Chl1) were up-regulated in both the model and patients. These genes are known to play a role in cell adhesion and/or ECM organization, biological processes important for the attachment and invasion of ectopic cells in tissues [14] [15] [16]. Thus, these genes might be critical for the development of endometriosis via cell attachment and invasion in both model and patients. The inflammatory and immune responses are also critical to the development of endometriosis. Single-cell analysis has shown that T cells in endometriosis are less activated, cytotoxic T cell populations and the proportion of natural killer cells in endometriosis lesions are decreased, and the ratio of monocytes to macrophages is increased in endometriosis cysts whose main population highly expresses CD206 and CD163, which have been described as M2 macrophage markers [17]. In the present study, the gene expression of haptoglobin and CD163 was upregulated in both the model and patients. Haptoglobin is an acidic glycoprotein and ligand of CD163, which is a surface hemoglobin-haptoglobin scavenger receptor, and is related to the development of endometriosis [18]. These results suggest that M2 macrophages might be critical for the development of endometriosis in both model and patients. Furthermore, endometriosis is considered to be an estrogen-dependent disease. Previous studies have shown that the aberrant expression of hormone receptors in endometriosis lesions, including high estrogen receptor 2 (Esr2) to Esr1 ratios, is related progesterone resistance [19]. In our study, the gene expression of Esr1 was decreased in both the model and patients, suggesting that the estrogen response is also important in the pathogenesis of this model, despite the fact that the rodent model does not exhibit menstruation. Thus, this model partly reflects the pathophysiology of endometriosis that occurs in humans as mentioned above, and it might be useful for evaluating the efficacy of new therapeutic agents targeting biological processes that include cell adhesion and ECM remodeling, inflammatory and immune responses, cell proliferation, angiogenesis, and the estrogen response.
We found for the first time that gene expression in the eutopic uterus was changed in the model, and the biological processes associated with the genes whose expression was changed were response to lipopolysaccharide and neutrophil chemotaxis. Previous work has shown that the expression of lipopolysaccharide in the endometrium of endometriosis patients is increased compared to that in healthy controls [20]. These findings suggest that the model reflects the environment not only in ectopic lesions but also in the eutopic endometrium of endometriosis patients.
In addition to this model, immunocompromised models, in which human endometrial tissue is injected into mice, are useful for examining the multiple cellular pathways associated with the development of human endometriosis. However, immunocompromised models may not mimic the inflammatory or immune response of endometriosis patients because of the lack of a fully competent immune system in such mice [21]. The surgical immunocompetent model reflects the inflammation response, cell proliferation and the estrogen response of patients, yet it may not mimic early events in the development of endometriosis such as retrograde menstruation due to the surgical induction of ectopic growth [21]. There is reported to be no change in the levels of cytokeratin or E-cadherin in the epithelial cells of ectopic endometrium, or in the excessive collagen deposition or alpha-SMA positive myofibroblasts in the ectopic endometrium of the surgical mouse endometriosis model [22]. In the present study, the expression of genes related to the inflammatory or immune response and ECM remodeling was changed in the syngeneic mouse endometriosis model, indicating that this model may be distinct from other models.
A limitation of our study is that we did microarray analysis of whole tissues at a specific time point. The model was found not to reflect some biological process in humans, such as endopeptidase activity and platelet degranulation, at least under the present experimental conditions. However, since the level of gene expression would be expected to change with time after construction of the model, or according to the estrous cycle or the component cells, spatiotemporal single-cell RNA sequencing should be more effective for future study. To obtain data on gene expression in endometriosis patients, we used the gene expression data of endometriosis patients from three datasets in which the gene expression in ectopic tissue is compared to that in eutopic tissue, and reanalyzed them in order to unify the analysis method between the patient datasets. However, similar data would have been reported consecutively, so we should also analyze those new data to increase the sample size. Furthermore, in the future we should confirm the relationship between disease severity and the gene expression of key molecules which seem to be important for the development of the disease. Additionally, it is not clear whether the DEGs common to the model and patients are the cause or the result of the pathogenesis of endometriosis. To resolve this issue, experiments using a suppressor or initiator for each gene are necessary. On the basis of the DEGs identified in this study, further work would be expected to clarify molecular mechanisms underlying the pathogenesis of endometriosis, which may lead to the identification of new biomarkers and/or treatment targets for this disease.
#
#
Conflict of interest
The authors declare no conflict of interest.
Acknowledgements
The study was supported in part by Nippon Shinyaku Co., Ltd. We thank Dr. Gerald E. Smyth for English-language editing of the manuscript.
-
References
- 1 Giudice LC. Endometriosis. Clinical Practice. N Engl J Med 2010; 362: 2389-2398
- 2 Nnoaham KE, Hummelshoj L, Webster P. et al. World Endometriosis Research Foundation Global Study of Women's Health Consortium. Impact of endometriosis on quality of life and work productivity: a multicenter study across ten countries. Fertil Steril 2011; 96: 366-373
- 3 Kalaitzopoulos DR, Samartzis N, Kolovos GN. et al. Treatment of endometriosis: a review with comparison of 8 guidelines. BMC Women’s Health 2021; 21: 397
- 4 Donnez J, Dolmans MM. Endometriosis and medical therapy: from progestogens to progesterone resistance to GnRH antagonists: a review. J Clin Med 2021; 10: 1085
- 5 Laganà AS, Garzon S, Franchi M. et al. Translational animal models for endometriosis research: a long and windy road. Ann Transl Med 2018; Nov 6: 431
- 6 Burns KA, Rodriguez KF, Hewitt SC. et al. Role of estrogen receptor signaling required for endometriosis-like lesion establishment in a mouse model. Endocrinology. 2012; 153: 3960-39671
- 7 Sampson JA. Peritoneal endometriosis due to the menstrual dissemination of endometrial tissue into the peritoneal cavity. Am J Obstet Gynecol 1927; 14: 422-469
- 8 Goulielmos GN, Matalliotakis M, Matalliotaki C. et al. Endometriosis research in the -omics era. Gene. 2020; 741: 144545
- 9 Huang DW, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID Bioinformatics Resources. Nat Protoc 2009; 4: 44-57
- 10 Kupershmidt I, Su QJ, Grewal A. et al. Ontology-based meta-analysis of global collections of high-throughput public data. PLoS One 2010; 5: e13066
- 11 Eyster KM, Klinkova O, Kennedy V. et al. Whole genome deoxyribonucleic acid microarray analysis of gene expression in ectopic versus eutopic endometrium. Fertil Steril 2007; 88: 1505-1533
- 12 Hever A, Roth RB, Hevezi P. et al. Human endometriosis is associated with plasma cells and overexpression of B lymphocyte stimulator. Proc Nat Acad Sci U S A. 2007; 104: 12451-12456
- 13 Hull ML, Escareno CR, Godsland JM. et al. Endometrial-peritoneal interactions during endometriotic lesion establishment. Am J Pathol 2008; 173: 700-715
- 14 Ramón LA, Braza-Boïls A, Gilabert-Estellés J. et al. microRNAs expression in endometriosis and their relation to angiogenic factors. Hum Reprod 2011; 26: 1082-1090
- 15 Luddi A, Marrocco C, Governini L. et al. Expression of matrix metalloproteinases and their inhibitors in endometrium: high levels in endometriotic lesions. Int J Mol Sci 2020; 21: 2840
- 16 Liu T, Liu M, Zheng C. et al. Exosomal lncRNA CHL1-AS1 derived from peritoneal macrophages promotes the progression of endometriosis via the miR-610/MDM2 axis. Int J Nanomedicine 2021; 16: 5451-5464
- 17 Ma J, Zhang L, Zhan H. et al. Single-cell transcriptomic analysis of endometriosis provides insights into fibroblast fates and immune cell heterogeneity. Cell Biosci 2021; 11: 125
- 18 Zhong Q, Yang F, Chen X. et al. Patterns of Immune Infiltration in Endometriosis and Their Relationship to r-AFS Stages. Front Genet 2021; 12: 631715
- 19 Shao R, Cao S, Wang X. et al. The elusive and controversial roles of estrogen and progesterone receptors in human endometriosis. Am J Transl Res 2014; 6: 104-113
- 20 Khan KN, Kitajima M, Hiraki K. et al. Escherichia coli contamination of menstrual blood and effect of bacterial endotoxin on endometriosis. Fertil Steril 2010; 94: 2860-2863
- 21 Greaves E, Critchley HOD, Horne AW. et al. Relevant human tissue resources and laboratory models for use in endometriosis research. Acta Obstet Gynecol Scand 2017; 96: 644-658
- 22 Mishra A, Galvankar M, Vaidya S. et al. Mouse model for endometriosis is characterized by proliferation and inflammation but not epithelial-to-mesenchymal transition and fibrosis. J Biosci 2020; 45: 105
Corresponding
Publikationsverlauf
Eingereicht: 06. Januar 2022
Angenommen: 05. Juli 2022
Artikel online veröffentlicht:
02. September 2022
© 2022. Thieme. All rights reserved.
Georg Thieme Verlag
Rüdigerstraße 14, 70469 Stuttgart,
Germany
-
References
- 1 Giudice LC. Endometriosis. Clinical Practice. N Engl J Med 2010; 362: 2389-2398
- 2 Nnoaham KE, Hummelshoj L, Webster P. et al. World Endometriosis Research Foundation Global Study of Women's Health Consortium. Impact of endometriosis on quality of life and work productivity: a multicenter study across ten countries. Fertil Steril 2011; 96: 366-373
- 3 Kalaitzopoulos DR, Samartzis N, Kolovos GN. et al. Treatment of endometriosis: a review with comparison of 8 guidelines. BMC Women’s Health 2021; 21: 397
- 4 Donnez J, Dolmans MM. Endometriosis and medical therapy: from progestogens to progesterone resistance to GnRH antagonists: a review. J Clin Med 2021; 10: 1085
- 5 Laganà AS, Garzon S, Franchi M. et al. Translational animal models for endometriosis research: a long and windy road. Ann Transl Med 2018; Nov 6: 431
- 6 Burns KA, Rodriguez KF, Hewitt SC. et al. Role of estrogen receptor signaling required for endometriosis-like lesion establishment in a mouse model. Endocrinology. 2012; 153: 3960-39671
- 7 Sampson JA. Peritoneal endometriosis due to the menstrual dissemination of endometrial tissue into the peritoneal cavity. Am J Obstet Gynecol 1927; 14: 422-469
- 8 Goulielmos GN, Matalliotakis M, Matalliotaki C. et al. Endometriosis research in the -omics era. Gene. 2020; 741: 144545
- 9 Huang DW, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID Bioinformatics Resources. Nat Protoc 2009; 4: 44-57
- 10 Kupershmidt I, Su QJ, Grewal A. et al. Ontology-based meta-analysis of global collections of high-throughput public data. PLoS One 2010; 5: e13066
- 11 Eyster KM, Klinkova O, Kennedy V. et al. Whole genome deoxyribonucleic acid microarray analysis of gene expression in ectopic versus eutopic endometrium. Fertil Steril 2007; 88: 1505-1533
- 12 Hever A, Roth RB, Hevezi P. et al. Human endometriosis is associated with plasma cells and overexpression of B lymphocyte stimulator. Proc Nat Acad Sci U S A. 2007; 104: 12451-12456
- 13 Hull ML, Escareno CR, Godsland JM. et al. Endometrial-peritoneal interactions during endometriotic lesion establishment. Am J Pathol 2008; 173: 700-715
- 14 Ramón LA, Braza-Boïls A, Gilabert-Estellés J. et al. microRNAs expression in endometriosis and their relation to angiogenic factors. Hum Reprod 2011; 26: 1082-1090
- 15 Luddi A, Marrocco C, Governini L. et al. Expression of matrix metalloproteinases and their inhibitors in endometrium: high levels in endometriotic lesions. Int J Mol Sci 2020; 21: 2840
- 16 Liu T, Liu M, Zheng C. et al. Exosomal lncRNA CHL1-AS1 derived from peritoneal macrophages promotes the progression of endometriosis via the miR-610/MDM2 axis. Int J Nanomedicine 2021; 16: 5451-5464
- 17 Ma J, Zhang L, Zhan H. et al. Single-cell transcriptomic analysis of endometriosis provides insights into fibroblast fates and immune cell heterogeneity. Cell Biosci 2021; 11: 125
- 18 Zhong Q, Yang F, Chen X. et al. Patterns of Immune Infiltration in Endometriosis and Their Relationship to r-AFS Stages. Front Genet 2021; 12: 631715
- 19 Shao R, Cao S, Wang X. et al. The elusive and controversial roles of estrogen and progesterone receptors in human endometriosis. Am J Transl Res 2014; 6: 104-113
- 20 Khan KN, Kitajima M, Hiraki K. et al. Escherichia coli contamination of menstrual blood and effect of bacterial endotoxin on endometriosis. Fertil Steril 2010; 94: 2860-2863
- 21 Greaves E, Critchley HOD, Horne AW. et al. Relevant human tissue resources and laboratory models for use in endometriosis research. Acta Obstet Gynecol Scand 2017; 96: 644-658
- 22 Mishra A, Galvankar M, Vaidya S. et al. Mouse model for endometriosis is characterized by proliferation and inflammation but not epithelial-to-mesenchymal transition and fibrosis. J Biosci 2020; 45: 105
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