BACKGROUND: The aim of this study was to investigate the differentially expressed genes (DEGs) relevant to prognosis of ovarian cancer by use of integrated bioinformatics analysis.

MATERIAL AND METHODS: The DEGs between normal ovariy tissue and ovarian cancer tissue were screened in GSE54388, GSE14407, and GSE18520 datasets and the overlapping DEGs were then indentified. GO and KEEG enrichment were performed to analyze the biological functions and pathways of the DEGs. A protein–protein interaction (PPI) network of the identified DEGs was constructed using the STRING database. Differences in prognosis between low and high expression of the hub DEGs were also evaluated using the Kaplan-Meier Plotter database. Protein expression of 2 hub genes – BUB1B and KIF201A – was assessed by immunohistochemistry assay and evaluated with the patient’s clinical pathology characteristics.

RESULTS: We identified 361 DEGs, mainly involving oncogene-induced cell senescence, cyclin B1-CDK1 complex, protein kinase A catalytic subunit binding, cell cycle, and p53 signaling pathway. Ten hub genes were identified from among the 361 DEGs. The overall survival (OS) and progression-free survival (PFS) of these 10 hub genes were evaluated in the Kaplan-Meier-plotter database. Three (BUB1B, KIF11, and KIF20A) of the 10 hub genes were found to be correlated with ovarian cancer OS and PFS. BUB1B expression level was correlated with ovarian FIGO stage (p<0.05) and tumor differentiation (p<0.05). For KIF20A, the expression level was correlated with FIGO stage (p<0.05) and intraperitoneal metastasis (p<0.05).

CONCLUSIONS: DEGs can participate in ovarian cancer development and can be used as biomarkers for prognosis. Patients with upregulated BUB1B, KIF11, and KIF20A tend to have worse overall survival and disease-free survival compared with patients who have low expression.

Keywords: Biological Markers, Ovarian Neoplasms, Biomarkers, Tumor, Cell Cycle Proteins, Computational Biology, Databases, Genetic, Gene Expression Profiling, Gene Regulatory Networks, Immunohistochemistry, Kinesins, Protein Interaction Maps, Protein Serine-Threonine Kinases