Predicting immunotherapy outcomes in breast cancer: a model leveraging copper metabolism signatures

Background: Copper ions play multiple roles in cancer metastasis and invasion. Breast cancer prognosis and immunotherapy efficacy are intricately linked with genes associated with copper metabolism. Studies have shown the therapeutic potential of eliminating or overloading copper ions in cancer. Further study is needed to elucidate the correlation between copper ion metabolism and cancer progression. Methods: Based on The Cancer Genome Atlas (TCGA) dataset, this study first performed univariate COX regression and LASSO regression analyses on 111 copper metabolism-related genes. Subsequently, patients were stratified into high- and low-risk groups, and systematic comparisons were conducted between the two groups in terms of clinical stratification, immune cell infiltration, tumor mutational burden, and predicted immunotherapy response. To further validate the clinical relevance of the model genes, this study analyzed their protein expression patterns using the Human Protein Atlas database and performed immunohistochemical experiments with clinical breast cancer tissues and matched adjacent normal samples for confirmation. Results: A prognostic model was created to predict using the lasso-Cox regression results and the genes DLAT, SLC31A1, LOXL1, and MTF1. The low copper metabolism gene score group showed better survival outcomes, while the high copper metabolism gene score group was associated with enhanced immunotherapy response. Immunohistochemical staining revealed high DLAT, SLC31A1, LOXL1, and MTF1 protein expression. Conclusion: This work highlights the profound impact of copper metabolism on breast cancer biology, linking it to patient survival, immune landscape, and therapeutic response. These insights have direct implications for developing predictive biomarkers to guide personalized immunotherapy. The results provide a new perspective for breast cancer treatment and may offer a basis for optimizing diagnosis and treatment strategies.