Identification of biomarkers correlated with diagnosis and prognosis of endometrial cancer using bioinformatics analysis.

Abstract

Endometrial cancer (EC) is one of the most common malignancies in the female genital system, characterized by high mortality and recurrence rates. This study attempted to screen key genes and potential prognostic biomarkers for EC using bioinformatics analysis. Twenty-seven normal endometrial tissues and 135 EC samples were collected from four Gene Expression Omnibus (GEO) databases, then we identified the differentially expressed genes (DEGs) and conducted downstream analyses. Moreover, we screened hub genes by constructing a protein-protein interaction (PPI) network. Finally, we assessed the prognostic values and molecular mechanism of the potential prognostic genes using the Kaplan-Meier curve and Gene Set Enrichment Analysis (GSEA). As a result, 28 upregulated and 94 downregulated genes were determined after gene integration of these four GEO data sets. Gene Ontology analysis indicated that DEGs were mainly involved in transcriptional regulation and cell proliferation. The Kyoto Encyclopedia of Gene and Genome pathway analysis primarily related to transcriptional misregulation and apoptosis. Moreover, the PPI analysis revealed 10 hub genes (JUN, UBE2I, GATA2, WT1, PIAS1, FOXL2, RUNXI, EZR, TCF4, and NR2F2) with a high degree of connectivity, among them, the expression tendency of nine genes except UBE2I were consistent with messenger RNA level from The Cancer Genome Atlas data. Furthermore, only FOXL2, TCF4, and NR2F2 were significantly correlated with prognosis of EC patients, and their low expression associated biological pathways were enriched in the cell cycle and fatty acid metabolism. In conclusion, this study identified three key genes as biomarkers and potential therapeutic targets of EC on the basis of integrated bioinformatics analysis. The findings will improve our comprehension of the molecular mechanisms underlying the pathogenesis and prognosis of EC.