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.

Identification of candidate biomarkers correlated with poor prognosis of breast cancer based on bioinformatics analysis

“…The GEO ( http://www.ncbi.nlm.nih.gov/geo/ ) database is a free public database of microarrays and is used for gene expression datasets and platform records [ 9 ]. The gene expression profiles of GSE29044, GSE42568, GSE50428 were chosen from the GEO database.…”

Identification of a three-gene prognostic signature for radioresistant esophageal squamous cell carcinoma

“…CAFs can foster radioresistance in ESCC tumor cells through the long noncoding RNA DNM3OS by modulating the PDGFβ/PDGFRβ/FOXO1 signaling pathway, suggesting that CAFs-promoted DNM3OS could be a crucial target to reverse radioresistance in ESCC tumor cells. A study by Zhao et al [ 24 ] in 2020, showed that three genes ( FOXL2, TCF4 , and NR2F2 ) exhibited a significant correlation with the prognosis of endometrial carcinoma; biological pathways associated with the low expression of these three genes were significantly enriched in cell cycle and fatty acid metabolism of cancer cells. However, there is limited evidence to validate the gene signatures involved in conferring radioresistance in ESCC patients to delineate accurate and efficient disease prognosis[ 25 ].…”

Melatonin suppresses tumor proliferation and metastasis by targeting GATA2 in endometrial cancer

“…18,19,40,41 Bioinformatic analysis has shown that GATA2 is expressed at low levels in both ovarian cancer and EC. 42,43 High GATA2 expression is associated with a good prognosis in ovarian cancer. 20 This finding is consistent with the results of the present study.…”

“…GATA2 is expressed at low levels in renal clear cell carcinoma, hepatocellular carcinoma, and neuroblastoma and inhibits distal metastasis and tumor recurrence 18,19,40,41 . Bioinformatic analysis has shown that GATA2 is expressed at low levels in both ovarian cancer and EC 42,43 . High GATA2 expression is associated with a good prognosis in ovarian cancer 20 .…”