Identification of synthetic lethality-associated subtypes and construction of risk model to predict breast cancer prognosis and immune characteristics

Synthetic lethality has emerged as a pivotal concept in cancer therapy, offering novel and promising strategies for treatment. In this study, we identified molecular subtypes associated with synthetic lethality in breast cancer and developed a prognostic signature based on synthetic lethality-related genes. Using the TCGA cohort, we screened differentially expressed synthetic lethal genes (DESLGs) and stratified patients into two distinct molecular subtypes based on their expression patterns. Immune profiling and clinical survival analyses revealed significant differences between these subtypes. A six-gene prognostic risk model was constructed using univariate and multivariate Cox regression analyses combined with LASSO regression, and its robustness was validated in independent GEO datasets. Independent prognostic analyses integrating clinical and pathological features were performed, followed by the construction of a nomogram to improve clinical applicability. The immune microenvironment was characterized using ssGSEA, ESTIMATE, and CIBERSORT algorithms, revealing a lower level of immune infiltration and distinct immune checkpoint expression in the high-risk group. In addition, patients in the high-risk group exhibited worse survival outcomes, higher tumor mutation burden, and increased resistance to several therapeutic agents. RT-qPCR analysis confirmed the differential expression of signature genes between breast cancer and normal mammary epithelial cells. These findings suggest that the synthetic lethality-based molecular subtypes and prognostic model offer reliable tools for evaluating clinical outcomes and guiding individualized therapy in breast cancer patients.