Identification of Molecular Markers Associated With Ovarian Cancer Prognosis Using Bioinformatics Analysis

Abstract Background Ovarian cancer is associated with a high mortality rate worldwide. However, the pathogenesis, clinicopathological features, and genetic mechanisms of ovarian cancer are still unclear, and it is essential to identify prognostic markers for its clinical diagnosis and treatment. Here, we utilized bioinformatic analysis to identify potential genes involved in mediating BRCA1 expression to elucidate the potential mechanisms underlying ovarian cancer. Methods Gene expression profiling (GSE14407) was performed to identify differentially expressed genes (DEGs) and analyze the weighted gene co-expression network. We selected the key module that was significantly associated with BRCA1 expression and performed gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses for genes in the hub module. We then screened the hub genes utilizing the Search Tool for the Retrieval of Interacting Genes Database (STRING) and Molecular Complex Detection (MCODE) plug-in in Cytoscape. We validated gene expression levels through The Cancer Genome Atlas and GTEx databases for hub genes and screened genes that were related to overall survival in patients with ovarian cancer using the Kaplan-Meier plotter database. Results In total, 3124 DEGs were detected, including 433 upregulated genes and 2691 downregulated genes. We selected the brown module, which was the most significantly associated with BRCA1 expression. GO analysis showed that the hub module genes were significantly enriched in biological processes, including the mitotic cell cycle process, chromosome segregation, and cell division. KEGG analysis showed that the hub module genes were particularly enriched in the cell cycle, p53 signaling pathway, and small cell lung cancer. We selected 30 hub genes from the protein-protein interaction network, which had 88 nodes and 721 edges. Further analyses identified PBK as a prognosis-associated hub gene. Notably, PBK expression was significantly increased in ovarian cancer tissues, as demonstrated by immunohistochemistry analysis using samples from the Human Protein Atlas database.Conclusion PBK was found to be associated with overall survival in patients with ovarian cancer. Our results provide insights into our understanding of the molecular mechanisms and molecular diagnosis of ovarian cancer.