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

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. Endometriosis is a frequently occurring gynaecological disease characterised by chronic pelvic pain, dysmenorrhea and infertility 1. Its prevalence is estimated to be 10-15% of reproductive age females 2 and around to 20-48% in infertile women 3. Despite a number of theories being suggested to describe the molecular mechanisms underlying the development of endometriosis such as: Sampson's theory of retrograde menstruation 4 , ectopic implantation, epigenetic factors 5 , immune and inflammatory factors 6,7 , eutopic endometrial determinism 8 , and stem cell factors 9 ; disease pathogenesis is still not fully understood. At present, there have been several studies on the gene expression profiles of endometriosis 10-13 , which have identified various differentially expressed genes (DEGs) involved in the development of endometriosis. However, due to heterogeneity between each independent experiment as a result of variations in tissue or specimens and/ or different data processing methods, the identification of these DEGs is inconsistent. In this study, we integrated different studies using a non-biased approach, which may resolve these problems and enable the discovery of effective and reliable molecular markers. We downloaded 3 microarray datasets GSE11691 11 , GSE7305 12 , GSE12768 13 , from Gene Expression Omnibus database (GEO), which contain gene expression data from endometriosis tissues and normal endometrial tissues. We then performed deep bioinformatic analysis, including ident...

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