Objective Using ovarian cancer datasets from public databases, identify copper death-related genes in ovarian cancer tissues and construct a clinical prognostic risk scoring model for ovarian cancer patients based on these genes.

Methods We downloaded the OC data of TCGA, the GSE26193, GSE63885 dataset from GEO and retrieved 10 cuproptosis related genes (CRGs) and analyzed their chromosomal localization, expression correlation, and mutation patterns based on the datasets. Using these genes, we clustered the OC samples to identify different molecular subtypes of copper induced death. We analyzed the differential genes and functional enrichment between different subtypes and obtained feature genes with predictive ability for prognosis through survival regression analyses. Based on these feature genes, we constructed a risk scoring model and incorporated the clinical characteristics ofpatients to jointly predict their survival rate.

Results In ovarian cancer samples, 10 copper death-related genes can stably divide the samples into two molecular subtypes, and there are significant differences in clinical and immune characteristics and drug sensitivity between them. After further screening, seven prognostic genes (RARRES1、CXCL10、PI3、CXCL11、THEMIS2、GBP2、RPL39L) were obtained, and the risk model based on them combined with age predicted that the AUC of patients' 1-, 3-, and 5-year survival rates were all greater than 0.7, showing good clinical application prospects.

Conclusion The mechanism of cuproptosis and its key genes might become therapeutic targets for ovarian cancer. The subtypes of cuproptosis provide a theoretical basis for personalized clinical treatment. The predictive model constructed by key prognostic genes has promising clinical application effects.