Cuprotosis-related gene subtypes, prognostic modeling, and tumor microenvironment remodeling in breast cancer

A newly discovered mode of cell death is called cuprotosis. Current evidence confirms that cuprotosis is involved in the occurrence and development of malignant tumors. However, the prognostic significance and function of cuprotosis-related genes (CRGs) in breast cancer (BC) remain unclear. The aim of this study is to establish a model for predicting the prognosis of BC patients with cuprotosis-related pattern-related gene (CRPRG) score, and explore the functional role of CRGs in the tumor microenvironment (TME).

In this study, transcriptome profiles, corresponding clinical information and tumor mutation data were collected from The Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO) database (GSE20685). The multigene prediction model was constructed using the least absolute shrinkage and selection operator (LASSO) and Cox regression analysis, and then validated by Kaplan-Meier and receiver operating characteristic (ROC) analysis.

Using LASSO analysis and Cox regression analysis, we screened out eight cuprotosis-related pattern genes (including PANX1, GPR107, MTFR1, FKBP5, CD24, CRISP3, NPY1R and ADIRF) that were associated with prognosis. According to the median risk score, the patients were divided into high- and low-risk groups. The results of survival analysis, ROC curve analysis and risk curve analysis of the prognostic model showed that the prognosis of the high-risk group was poor, and the predictive ability of the risk prognosis model was also verified. In multivariate Cox regression analysis, risk score was an independent prognostic factor (P<0.001). Functional analysis revealed differences in immune infiltration, tumor mutational burden (TMB), stem cell content, and drug sensitivity between the two groups.

In conclusion, we identified two CRG subtypes and three cuprotosis-related differentially expressed gene (DEG) clusters in BC. CRPRG score has shown good performance in predicting the prognosis and efficacy of treatments for BC and is expected to provide potential biological therapeutic targets for BC.