[The Role of Cuproptosis Related Key Genes in Ovarian Cancer and the Construction of a Prognostic Model].

Authors

Huimin Wang, Ying Jiang, Jinxin Chen, Peng Mu, Jingru Zhang

Published in

Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition. Volume 57. Issue 3. Pages 725-735. May 20, 2026.

Abstract

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.
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.
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.
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.

PMID:
42369705
Bibliographic data and abstract were imported from PubMed on 29 Jun 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.