Machine learning-based detoxification enzymes-related genes prognosis model in breast cancer: Immune landscape and Clinical significance

Background Breast cancer is one of the most common malignant tumors, threatening women's health and life globally. Despite significant treatment advances, its prognosis still faces great challenges. With the rapid development of molecular biology and genomics, the role of detoxification enzymes in breast cancer occurrence, development, and prognosis has gained increasing attention. This paper aims to establish a prognostic model based on detoxification enzymes-related genes to predict breast cancer patient survival. Methods Unsupervised clustering was used to analyze breast cancer samples based on detoxification enzymes-related genes expression. Lasso cox regression analysis and univariate and multivariate Cox analysis were used to process the data, and machine learning algorithm was used to construct breast cancer prognosis model. The effect of detoxification enzymes-related genes on breast cancer was analyzed by single cell analysis. Results The samples were classified into two subtypes, and a breast cancer prognosis model based on detoxification enzymes-related genes was constructed and validated using TCGA and GEO cohorts. Significant differences in pathways, immune infiltration, immunotherapy response, and drug sensitivity were observed between high- and low-risk groups. Single-cell analysis revealed that SQLE, a detoxification enzymes-related gene, was highly expressed in breast cancer epithelial cells(cancer cells), where SQLE + epithelial cells primarily influenced exhausted CD8 + T cells via the MIF signaling pathway. Conclusion In summary, the detoxification enzymes-related genes-based prognostic model developed in this study provides an effective tool for predicting breast cancer prognosis and offers new insights for diagnosis and treatment.