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Objectives: Ovaries are important and essential organs of animals in producing and releasing eggs. Ovarian cancer (OvCa) is one of the most prevalent lethal gynecological malignancies with a lack of distinct biomarkers. Advances in high-throughput genomic data and the continued refinement of bioinformatics tools enable the identification of potential biomarkers. Leveraging these insights, we can employ systems biology approaches to enhance the accuracy of diagnosis and prognosis. Material and Methods: A comparative analysis was conducted between normal and tumor samples, employing bioinformatics software and tools. Differential expression analysis utilized fold-change statistics, while DAVID 6.8 software was used to perform gene ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses. The protein-protein interaction (PPI) network was constructed differentially expressed genes (DEGs) using Search Tool for the Retrieval of Interacting Genes database, and Cytoscape 3.9.1, along with its Molecular Complex Detection and CytoHubba plugins, facilitated network visualization, analysis, and module detection. Hub gene expression and overall survival were explored through the Kaplan–Meier plotter, while Gene Expression Profiling Interactive Analysis 2 analyzed the tumor stage of OvCa patients. Hub genes protein expression was analyzed using the human protein atlas database through immunostaining results. The NetworkAnalyst program and Cytoscape were employed to analyze and visualize the transcription factor-hub gene associations. Subsequently, single-nucleotide variation, methylation, and pathway activity of hub genes were examined. Validation of hub genes messenger RNA expression was done using quantitative real-time polymerase chain reaction analysis. Results: 607 DEGs, including 248 upregulated and 359 downregulated genes, were identified. The top 20 candidate genes were screened out through PPI network analysis. We discovered that the genes BUB1 Mitotic Checkpoint Serine/Threonine Kinase B (BUB1B), Cyclin A2 (CCNA2), Mitotic Arrest Deficient 2 Like 1 (MAD2L1), Protein Regulator of Cytokinesis 1 (PRC1), Thyroid Hormone Receptor Interactor 13 (TRIP13), and ZW10 Interacting Kinetochore Protein (ZWINT) exhibited significant importance in OvCa prognosis. Conclusion: Six genes, BUB1B, CCNA2, MAD2L1, PRC1, TRIP13, and ZWINT (identified as functional hub genes), are probably playing tumor-promotive roles, except TRIP13. All genes product is functionally related to the cell cycle. These can be targeted in quest of potential therapeutics for OvCa treatment.
Niharika et al. (Mon,) studied this question.