Identification of key molecular markers in epithelial ovarian cancer by integrated bioinformatics analysis

Abstract

Objective: The current research was aimed to identify candidate genes associated with development and progression of epithelial ovarian carcinoma using bioinformatics analysis. Materials and methods: We screened and validated candidate genes associated with carcinogenesis and development of epithelial ovarian carcinoma via bioinformatic analysis in three microarray datasets (GSE14407, GSE29450, and GSE54388) downloaded from the Gene Expression Omnibus (GEO) database. Results: Our bioinformatic analysis identified 514 differentially expressed genes (DEGs) and nine candidate hub genes (CCNB1, CDK1, BUB1, CDC20, CCNA2, BUB1B, AURKA, RRM2, and TTK). Survival analysis using the Kaplan–Meier plotter showed that high expression levels of seven candidate genes (CCNB1, RRM2, BUB1, CCNA2, AURKA, CDK1, and BUB1B) were associated with poor overall survival (OS). Gene Expression Profiling Interactive Analysis (GEPIA) revealed a higher expression level of these seven candidate genes in ovarian carcinoma samples than in normal ovarian samples. Immunostaining results from the Human Protein Atlas (HPA) database suggested that the protein expression levels of CCNB1, CCNA2, AURKA, and CDK1 were increased in ovarian cancer tissues. No difference was observed in RRM2 protein expression level between normal ovarian and ovarian cancer samples. Oncomine analysis revealed an association between the expression patterns of BUB1B, CCNA2, AURKA, CCNB1, CDK1, and BUB1 and patient clinicopathological information. Finally, six genes, namely CCNB1, CCNA2, AURKA, BUB1, BUB1B, and CDK1, were identified as hub genes and a transcription factor (TF)-gene regulatory network was constructed to identify TFs, including POLR2A, ZBTB11, KLF9, and ELF1, that were implicated in regulating these hub genes. Conclusion: Six significant hub DEGs associated with a poor prognosis in epithelial ovarian cancer were identified. These could be potential biomarkers for ovarian cancer patients.

Keywords

• Ovarian cancer
• Differentially expressed genes
• Bioinformatics analysis
• Kaplan–Meier curve
• Transcriptional factors