Introduction: Ovarian cancer is the third most common gynecologic malignancy. The chemoresistance emergence is a critical challenge in ovarian cancer treatment. Resistance to chemotherapeutic agents is established through genetic and epigenetic alterations that confer upon cancer cells the ability to evade apoptosis and survive cytotoxic damage. The identification of key genes associated with chemoresistance assists in the development of targeted therapies against chemoresistance. In this study, key genes associated with cisplatin resistance in ovarian cancer were identified utilizing bioinformatics tools.
Methods: Three cisplatin-resistant samples and three control samples from the GSE148251 dataset were analyzed using GEO2R. The target genes of deregulated miRNAs were predicted utilizing the miRNet platform. Then, pathway enrichment analysis was carried out for target genes of deregulated miRNAs using the DAVID database. A protein-protein interaction (PPI) network for these genes was constructed with the STRING database and subsequently analyzed in Cytoscape (v3.10.3) to identify key hub genes.
Results: GEO2R analysis revealed the significant downregulation of hsa-miR-105 (log2FC = -3.99, padj = 0.0156) and upregulation of hsa-miR-203 (log2FC = 4.48, padj = 0.0156) and hsa-miR-152 (log2FC = 3.09, padj = 0.0444). A total of 649 target genes were predicted for these deregulated miRNAs. Pathway enrichment analysis indicated that the majority of these genes belong to cancer-associated pathways, particularly the PI3K-Akt signaling pathway. Subsequent PPI network analysis identified AKT1 as the hub gene, exhibiting the highest degree centrality (degree centrality = 146).
Conclusion: The results identified AKT1 as a hub gene of cisplatin resistance in ovarian cancer. The development of inhibitory drugs targeting the AKT1 gene may overcome resistance to cisplatin in ovarian cancer.