Bioinformatic analysis reveals the importance of epithelial-mesenchymal transition in the development of endometriosis

Meihong Chen, Yilu Zhou, Hong Xu, Charlotte Hill, Rob M Ewing, Deming He, Xiaoling Zhang, Yihua Wang
Scientific reports · 2020 · vol. 10(1) , pp. 8442 · doi:10.1038/s41598-020-65606-9 · PMID:32439908 · PMC7242372 · W3027386899
article OA: gold CC0 ⤵ 59 in-corpus citations
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⚙ AI-generated summary by claude@2026-06, 2026-06-10 ⓘ

This bioinformatic analysis of gene expression data identified epithelial-mesenchymal transition as a key pathway in endometriosis development, supported by differential expression of related genes like CDH1 and CXCL12.

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⚙ AI-generated deep summary by claude@2026-06, 2026-06-10 ⓘ

This study integrated three publicly available gene expression microarray datasets (GSE11691, GSE7305, and GSE12768) comparing endometriosis tissues with normal endometrium to identify differentially expressed genes and enriched pathways, followed by protein-protein interaction and module analyses. Across datasets, the authors found 186 DEGs, and gene ontology enrichment highlighted cell adhesion, inflammatory response, and extracellular exosome, while epithelial-mesenchymal transition (EMT) ranked first in Hallmark pathway enrichment; they proposed EMT could be induced by inflammatory cytokines such as CXCL12. Immunohistochemistry in endometrial tissues from endometriosis patients (n=6, stage IV ovarian endometriosis) and controls (n=6) confirmed down-regulation of E-cadherin (CDH1) and up-regulation of CXCL12. A key limitation is the small size of the experimental validation cohort and the reliance on bioinformatic processing of heterogeneous microarray datasets. This paper is centrally about endometriosis — it uses integrated transcriptomic bioinformatics and patient-tissue validation to argue for EMT-related molecular changes in the development of endometriosis.

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Abstract

BACKGROUND: Endometriosis is a frequently occurring disease in women, which seriously affects their quality of life. However, its etiology and pathogenesis are still unclear. METHODS: To identify key genes/pathways involved in the pathogenesis of endometriosis, we recruited 3 raw microarray datasets (GSE11691, GSE7305, and GSE12768) from Gene Expression Omnibus database (GEO), which contain endometriosis tissues and normal endometrial tissues. We then performed in-depth bioinformatic analysis to determine differentially expressed genes (DEGs), followed by gene ontology (GO), Hallmark pathway enrichment and protein-protein interaction (PPI) network analysis. The findings were further validated by immunohistochemistry (IHC) staining in endometrial tissues from endometriosis or control patients. RESULTS: We identified 186 DEGs, of which 118 were up-regulated and 68 were down-regulated. The most enriched DEGs in GO functional analysis were mainly associated with cell adhesion, inflammatory response, and extracellular exosome. We found that epithelial-mesenchymal transition (EMT) ranked first in the Hallmark pathway enrichment. EMT may potentially be induced by inflammatory cytokines such as CXCL12. IHC confirmed the down-regulation of E-cadherin (CDH1) and up-regulation of CXCL12 in endometriosis tissues. CONCLUSIONS: Utilizing bioinformatics and patient samples, we provide evidence of EMT in endometriosis. Elucidating the role of EMT will improve the understanding of the molecular mechanisms involved in the development of endometriosis.

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Condition tags

endometriosis

MeSH descriptors

Biomarkers Computational Biology Endometriosis Endometrium Epithelial-Mesenchymal Transition Gene Expression Regulation Adult Biomarkers Case-Control Studies Computational Biology Endometriosis Endometriosis Endometriosis Endometrium Endometrium Female Follow-Up Studies Gene Expression Profiling Humans Prognosis

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