Abstract

Endometriosis, a common disease that presents as polymorphism, invasiveness, and extensiveness, with clinical manifestations including dysmenorrhea, infertility, and menstrual abnormalities, seriously affects quality of life in women. To date, its underlying etiological mechanism of action and the associated regulatory genes remain unclear. This study aimed to identify molecular markers and elucidate mechanisms underlying the development and progression of endometriosis. Specifically, we downloaded five microarray expression datasets, namely, GSE11691, GSE23339, GSE25628, GSE7305, and GSE105764, from the Gene Expression Omnibus (GEO) database. These datasets, obtained from endometriosis tissues, alongside normal controls, were subjected to in-depth bioinformatics analysis for identification of differentially expressed genes (DEGs), followed by analysis of their function and pathways via gene ontology (GO) and KEGG pathway enrichment analyses. Moreover, we constructed a protein–protein interaction (PPI) network to explore the hub genes and modules, and then applied machine learning algorithms support vector machine-recursive feature elimination and least absolute shrinkage and selection operator (LASSO) analysis to identify key genes. Furthermore, we adopted the CIBERSORTx algorithm to estimate levels of immune cell infiltration while the connective map (CMAP) database was used to identify potential therapeutic drugs in endometriosis. As a result, a total of 423 DEGs, namely, 233 and 190 upregulated and downregulated, were identified. On the other hand, a total of 1,733 PPIs were obtained from the PPI network. The DEGs were mainly enriched in immune-related mechanisms. Furthermore, machine learning and LASSO algorithms identified three key genes, namely, apelin receptor (APLNR), C–C motif chemokine ligand 21 (CCL21), and Fc fragment of IgG receptor IIa (FCGR2A). Furthermore, 16 small molecular compounds associated with endometriosis treatment were identified, and their mechanism of action was also revealed. Taken together, the findings of this study provide new insights into the molecular factors regulating occurrence and progression of endometriosis and its underlying mechanism of action. The identified therapeutic drugs and molecular markers may have clinical significance in early diagnosis of endometriosis.

Highlights

  • Endometriosis (EM), in which active endometrium is implanted in any site outside the uterine cavity, is a common gynecological disease characterized by chronic pelvic pain, dysmenorrhea, and infertility (Aghajanova and Giudice, 2010)

  • Based on the screening criterion, we discovered that the genes are mainly involved in T cell activation, regulation T cell activation, and cell chemotaxis in gene ontology (GO) results (Figure 3A), while phagosome, cell adhesion molecules (CAMs), and the intestinal immune network for IgA production pathway were enriched in KEGG results (Figure 3B)

  • We identified differentially expressed genes (DEGs) across four datasets obtained from EM and normal tissues

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Summary

Introduction

Endometriosis (EM), in which active endometrium is implanted in any site outside the uterine cavity, is a common gynecological disease characterized by chronic pelvic pain, dysmenorrhea, and infertility (Aghajanova and Giudice, 2010). Previous studies have demonstrated that overexpression and downregulation of antiapoptotic and proapoptotic factors, respectively, may interfere with peritoneal homeostasis (Erekat et al, 2018), while the mitogen-activated protein kinase (MAPK) signaling pathway has been shown to play a key role in disease occurrence (Lotfaliansaremi et al, 2020). Apoptosis pathways of Fas-FasL and tumor necrosis factor (TNF)-a seem to play a key role in immune monitoring of the peritoneal microenvironment (Yamada et al, 2017). Overall, these changes may prevent clearance of endometrial cells that reach the peritoneal cavity, thereby allowing their implantation and development. The current consensus is that changes in immune-related cell function play a crucial role in occurrence and progression of EM (Ahn et al, 2015)

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