Abstract
OBJECTIVES: In this research, we aim to explore the bioinformatic mechanism of infertile endometriosis in order to identify new treatment targets and molecular mechanism. METHODS: The Gene Expression Omnibus (GEO) database was used to download MRNA sequencing data from infertile endometriosis patients. The "limma" package in R software was used to find differentially expressed genes (DEGs). Weighted gene co-expression network analysis (WGCNA) was used to classify genes into modules, further obtained the correlation coefficient between the modules and infertility endometriosis. The intersection genes of the most disease-related modular genes and DEGs are called gene set 1. To clarify the molecular mechanisms and potential therapeutic targets for infertile endometriosis, we used Gene Ontology (GO), Kyoto Gene and Genome Encyclopedia (KEGG) enrichment, Protein-Protein Interaction (PPI) networks, and Gene Set Enrichment Analysis (GSEA) on these intersecting genes. We identified lncRNAs and miRNAs linked with infertility and created competing endogenous RNAs (ceRNA) regulation networks using the Human MicroRNA Disease Database (HMDD), mirTarBase database, and LncRNA Disease database. RESULTS: Firstly, WGCNA enrichment analysis was used to examine the infertile endometriosis dataset GSE120103, and we discovered that the Meorangered1 module was the most significantly related with infertile endometriosis. The intersection genes were mostly enriched in the metabolism of different amino acids, the cGMP-PKG signaling pathway, and the cAMP signaling pathway according to KEGG enrichment analysis. The Meorangered1 module genes and DEGs were then subjected to bioinformatic analysis. The hub genes in the PPI network were performed KEGG enrichment analysis, and the results were consistent with the intersection gene analysis. Finally, we used the database to identify 13 miRNAs and two lncRNAs linked to infertility in order to create the ceRNA regulatory network linked to infertile endometriosis. CONCLUSION: In this study, we used a bioinformatics approach for the first time to identify amino acid metabolism as a possible major cause of infertility in patients with endometriosis and to provide potential targets for the diagnosis and treatment of these patients.