Bioinformatics Analysis Identifies Molecular Markers Regulating Development and Progression of Endometriosis and Potential Therapeutic Drugs - Peng, Ying; Peng, Cheng; Fang, Zheng; Chen, Gang (Frontiers in Genetics, 2021)
Endometriosis is a common disease in women. It presented as polymorphism, invasiveness, and extensiveness, and its main clinical manifestations include dysmenorrhea, infertility, and menstrual abnormalities, seriously affecting the quality of life of women. However, its etiological mechanism and underlying driver genes remain unclear. This study is aimed to clarify the molecular markers and mechanisms underlying the development and sustained presence of this disease. We downloaded four microarray expression data sets (GSE11691, GSE23339, GSE25628 and GSE105764) from the Gene Expression Omnibus (GEO). These data sets contain endometriosis and normal tissues. We further conducted in-depth bioinformatics analysis to identify differentially expressed genes (DEGs) between normal and endometriosis tissues, then gene ontology (GO) and KEGG pathway enrichment analysis were further performed on these DEGs to investigate their function and pathways. The protein–protein interaction (PPI) network analysis was applied to explore the key genes and module. In addition, we also using the machine learning methods (SVM-RFE) and LASSO algorithm to identify the key genes. The CIBERSORTX algorithm was used to estimated the immune cell infiltration level and the connective map (CMAP) database was used to identify potential therapeutic drugs in endometriosis. As a result, a total of 423 differential genes were identified, including 233 upregulated genes and 190 downregulated genes through differentially expression analysis, and a total of 1733 PPIs were obtained form PPI net work analysis. The most abundant functions of the DEGs were mainly related to immune mechanisms. Three key genes including apelin receptor (APLNR), C-C motif chemokine ligand 21 (CCL21), and Fc fragment of IgG receptor IIa (FCGR2A) were identified using the machine learning methods and LASSO algorithm. In addition, 16 small molecular compounds that may contributed to the treatment of EM were identified and the mechanism of action of each drug was also analyzed. This study provides new insights into the occurrence and development of endometriosis and its molecular mechanism, and it identifies specific therapeutic drugs and molecular markers with clinical significance for the early diagnosis of endometriosis.