Identification of Copper Homeostasis-Related Gene Signature for Predicting Prognosis in Patients with Epithelial Ovarian Cancer

• Published in: Cancer informatics Vol. 23; p. 11769351241272400
• Main Authors: Yan, Ping, Tian, Yueqin, Li, Xiaojing, Li, Shuangmei, Wu, Haidong, Wang, Tong
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.01.2024; Sage Publications Ltd; SAGE Publishing
• Subjects: Apoptosis; Cancer; Copper; Homeostasis; Immune system; Mast cells; Medical prognosis; Original Research; Ovarian cancer; p53 Protein; Patients; Prognosis; Protein biosynthesis; Protein synthesis; Protein transport; Regression analysis; Regression models; Risk groups; Subgroups; gene signature; machine learning; Epithelial ovarian cancer; copper homeostasis; prognosis
• DOI: 10.1177/11769351241272400
• ISSN: 1176-9351

Objectives: This research aims to establish a copper homeostasis-related gene signature for predicting the prognosis of epithelial ovarian cancer and to investigate its underlying mechanisms. Methods: We mainly constructed the copper homeostasis-related gene signature by LASSO regression analysis. Then multiple methods were used to evaluate the independent predictive ability of the model and explored the mechanisms. Results: The 15-copper homeostasis-related gene (15-CHRG) signature was successfully established. Utilizing an optimal cut-off value of 0.35, we divided the training dataset into high-risk and low-risk subgroups. Kaplan-Meier analysis revealed that survival times for the high-risk subgroup were significantly shorter than those in the low-risk group (P < .05). Additionally, the Area Under the Curve (AUC) of the 15-CHRG signature achieved 0.822 at 1 year, 0.762 at 3 years, and 0.696 at 5 years in the training set. COX regression analysis confirmed the 15-CHRG signature as both accurate and independent. Gene set enrichment (GSEA), Kyoto Encyclopedia of Gene and Genome (KEGG) and Gene Ontology (GO) analysis showed that there were significant differences in apoptosis, p53 pathway, protein synthesis, hydrolase and transport-related pathways between high-risk group and low-risk group. In tumor immune cell (TIC) analysis, the increased expression of resting mast cells was positively correlated with the risk score. Conclusion: Consequently, the 15-CHRG signature shows significant potential as a method for accurately predicting clinical outcomes and treatment responses in patients with epithelial ovarian cancer.