Abstract
Breast cancer is the most common and lethal malignancy among women worldwide. Cuproptosis, a newly identified copper-dependent cell death, is closely associated with cancer development. However, its regulatory mechanisms in breast cancer are not well studied. This study aims to establish a prognostic model for breast cancer to improve risk stratification. The mRNA expression data was downloaded from The Cancer Genome Atlas and Gene Expression Omnibus databases. Consensus clustering identified patterns based on cuproptosis-related genes. Key genes were screened using Weighted Gene Co-Expression Network Analysis and differentially expressed gene analysis. A prognostic model was constructed using Cox regression and evaluated with time-dependent receiver operating characteristic and Kaplan-Meier analyses. Functional pathways, immune cell infiltration, and other tumor characteristics were also analyzed. Two distinct cuproptosis patterns were identified. The top 21 differentially expressed genes, significantly associated with survival, were used to construct the prognostic model. The risk score has a negative correlation with survival. Enrichment analysis showed immune-related pathways enriched in the low-risk group, which also had more immune cell infiltration, higher stromal component, lower tumor purity, and lower tumor heterogeneity. Finally, significant differences of half maximal inhibitory concentration were also observed between patients in high- and low-risk groups who received chemotherapy and targeted therapy drugs. These findings in our study may provide evidence for further research and individualized management of breast cancer.