A comprehensive bioinformatics analysis to identify a candidate prognostic biomarker for ovarian cancer

作者：Huijun Zhu, Haiying Yue, Yiting Xie, Qinghua Du, Binglin Chen, Yanhua Zhou, Wenqi Liu · 发表于：Translational Cancer Research · 年份：2021 · DOI：10.21037/tcr-21-380 · 被引用次数：7 · 研究领域：Ferroptosis and cancer prognosis、Bioinformatics and Genomic Networks、Clusterin in disease pathology

Background: This study aimed to investigate prognostic genes in ovarian cancer (OC) and to explore their potential underlying biological mechanisms through a comprehensive bioinformatics analysis. Methods: Common differentially expressed genes (DEGs) in 3 OC datasets from the Gene Expression Omnibus (GEO) (GSE26712, GSE18520, and GSE14407) were screened out. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed by Metascape. The protein-protein interaction (PPI) network of the DEGs was constructed using the STRING database. The prognostic value of DEGs were determined using the Kaplan-Meier plotter. The ONCOMINE and Human Protein Atlas databases were used to verify the expression levels of prognostic genes in OC. Genomic analysis of prognostic genes were also investigated by cBio Cancer Genomics Portal (cBioPortal) database, UCSC Xena browser and UALCAN. Gene set enrichment analysis (GSEA) was used to predict the possible pathways and biological processes of the prognostic genes. Results: Integration of the 3 datasets have found 879 common DEGs. A high expression of structural maintenance of chromosomes protein 4 ( SMC4 ) was revealed in the Kaplan-Meier plotter analysis to be meaningful for the prognosis of OC and was verified at both the mRNA and protein levels. The results from cBioPortal showed that SMC4 alterations accounted for 7 to 18% of genetic alterations in OC, and the majority alterations were copy number amplificati...