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Identification of hub genes in key hallmarks of ovarian cancer via bioinformatics analysis.

Authors :
Su R
Jin C
Jin C
Kuang M
Xiang J
Source :
Translational cancer research [Transl Cancer Res] 2021 Feb; Vol. 10 (2), pp. 827-841.
Publication Year :
2021

Abstract

Background: Ovarian cancer is one of the most lethal malignant gynecologic tumors worldwide. We aimed to identify critical hallmarks of the surface epithelium between normal ovaries and serous ovarian carcinomas and then obtain the hub genes associated with these critical hallmarks.<br />Methods: We chose GSE36668, GSE54388 and GSE69428 as data sources and then determined the common gene sets through gene set enrichment analysis (GSEA) to explore essential hallmarks and hub genes driving normal ovarian cells to evolve progressively into a neoplastic state. The differentially expressed genes (DEGs) extracted separately in each gene set were analyzed again through the Metascape website. Kaplan-Meier plotter was used to obtain significant prognostic information. The hub genes were obtained through protein-protein interaction (PPI) network by Cytoscape. Hub genes were confirmed to have higher mRNA and protein expression levels in ovarian cancer tissues than in normal tissues through public databases [Gene Expression Profiling Interactive Analysis (GEPIA) and The Human Protein Atlas]. Correlation analysis of six hub genes showed a strong correlation via R.<br />Results: We obtained 11 common hallmarks from GSEA of the three mentioned datasets and 22 hub genes that showed a significant association with poor survival.<br />Conclusions: Not only the gene sets enriched by GSEA but also the hub genes extracted by the PPI network indicate that the most fundamental trait of ovarian cancer cells involves their ability to sustain chronic proliferation.<br />Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at http://dx.doi.org/10.21037/tcr-20-2604). The authors have no conflicts of interest to declare.<br /> (2021 Translational Cancer Research. All rights reserved.)

Details

Language :
English
ISSN :
2219-6803
Volume :
10
Issue :
2
Database :
MEDLINE
Journal :
Translational cancer research
Publication Type :
Academic Journal
Accession number :
35116413
Full Text :
https://doi.org/10.21037/tcr-20-2604