Identification of key genes associated with endometriosis and endometrial cancer by bioinformatics analysis - Ma, Ruyue; Zheng, Yu; Wang, Jianing; Xu, Hong; Zhang, Ruirui; Xie, Zhijia; Zhang, Lei; Zhao, Ruiheng (Frontiers in Oncology, 2024)
BackgroundEndometriosis (EMS) is acknowledged as a risk factor for the development of endometrial cancer (EC), although the precise molecular mechanisms that underpin this association have yet to be fully elucidated. The primary objective of this investigation is to harness bioinformatics methodologies to identify pivotal genes and pathways that may be implicated in both EMS and EC, potentially offering novel therapeutic biomarkers for the management of endometriosis.MethodsWe acquired four datasets pertaining to EMS and one dataset concerning EC from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) in EMS and EC cohorts, in comparison to controls, were ascertained utilizing the limma package. Subsequently, we conducted a series of bioinformatic analyses, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and protein-protein interaction (PPI) analysis, to delineate pathways associated with the identified DEGs.ResultsOur bioinformatics analyses disclosed 141 shared DEGs between EMS and EC groups relative to the control cohort. GO analysis demonstrated that these genes are predominantly involved in the regulation of growth and development, as well as signal transduction pathways. KEGG analysis underscored the significance of these genes in relation to the JAK-STAT signaling pathway and leukocyte transendothelial migration. Furthermore, PPI analysis pinpointed ten central genes (APOE, FGF9, TIMP1, BGN, C1QB, MX1, SIGLEC1, BST2, ICAM1, MME) exhibiting high interconnectivity. Notably, the expression levels of APOE, BGN, C1QB, and BST2 were found to correlate with cancer genomic atlas data, and were implicated in tumor immune infiltration. Strikingly, only APOE and BGN demonstrated a significant correlation with patient prognosis.ConclusionThis comprehensive bioinformatics analysis has successfully identified key genes that may serve as potential biomarkers for EC. These findings significantly enhance our comprehension of the molecular underpinnings of EC pathogenesis and prognosis, and hold promise for the identification of novel drug targets.