Abstract
Background Ovarian cancer is a common gynecological malignancy leading to female mortality. Although the existing means of treatment for ovarian cancer are effective, the diagnosis and prognosis of ovarian cancer need to be further explored due to its highly heterogeneous nature.Methods We screened differential genes in ovarian cancer by TCGA database and GEO database, and further screened mitochondria-related genes (MRGs) in ovarian cancer by overlapping differential genes with mitochondrial genes, and analyzed the enrichment. Kaplan-Meier (KM) survival analysis and univariate and multivariate Cox regression analyses were used to validate the prognosis of the genes, and gene set enrichment analysis (GSEA) was performed to elucidate the molecular mechanisms of risk scores. In addition, the correlation between the eight MRGs and immune status was evaluated. Finally, drug sensitivity analysis was performed by CellMiner database.Results In our study, eight MRGs in ovarian cancer were screened and a prognostic risk model was constructed. The accuracy of the prognostic model was verified by combining the ROC curve and differential protein expression. Furthermore, MRGs are widely expressed in immune cells infiltrating in the tumor microenvironment and are significantly correlated with immune processes. In addition, GSEA enrichment analysis showed that metabolism and immune signaling and other related pathways were significantly different in high- and low-risk patients. Finally, drug susceptibility testing screened out 24 drugs that may play a role in treating OC by targeting the above-mentioned risk MRGs.Conclusion These findings reveal key mitochondrial genes affecting OC, as well as interactions with the immune microenvironment. In addition, the prognostic model established based on MRGs provides a potential prognostic strategy and provides new ideas for the diagnosis, prognosis and treatment of OC.