Identification of a disulfidptosis-related genes signature for prognostic implication in ovarian cancer

Abstract

Abstract Background: Ovarian cancer is an extremely deadly gynecological malignancy, with a 5-year survival rate below 30%. Additionally, disulfidptosis, a newly discovered type of cell death, has been found to be closely associated with the onset and progression of tumors. Methods: Disulfidptosis-related clusters were identified by consensus clustering. Univariate and multivariate Cox regression analyses were applied to construct a prognostic risk model. Patients were then divided into high- and low-risk groups. Gene mutation frequency, tumor microenvironment, and drug sensitivity analysis were performed between these two groups. Subsequently, a nomogram was constructed. Results: We identified 721 differentially expressed genes (DEGs) from two disulfidptosis-related clusters, and constructed a risk-prognosis signature. Analysis of the risk score revealed that compared to the high-risk group, the low-risk group had a better prognosis. Gene mutation frequency and tumor microenvironment analysis identified distinct characteristics between two risk groups. We also screened potential chemotherapy drugs that could sensitize ovarian cancer. Finally, the nomogram based on risk score and other clinical features showed a strong prognostic capability to predict overall survival (OS) for ovarian cancer patients. Conclusion: This study constructed a risk model related to disulfidptosis, which has a good prognostic value for ovarian cancer patients. The findings of this research provide novel insights into the understanding of ovarian cancer and could potentially lead to the development of new treatment strategies.