Table 1_Integrative multidimensional analysis of age-associated synthetic lethal genes and development of a prognostic model in breast cancer.docx

BackgroundBreast cancer (BRCA) is the most common malignancy and leading cause of mortality among women, with rising incidence in younger patients. Although treatments have advanced, outcomes for advanced BRCA remain poor. Synthetic lethality (SL) offers promise in precision oncology, but resistance limits its benefit.
MethodsWe integrated TCGA-BRCA and GEO datasets with SL gene sets to identify candidate genes. Differential expression analysis and WGCNA were performed, with key modules defined by clinical subgroups (≤40 vs. >40 years). Candidate genes were further validated by machine learning, Mendelian randomization (MR), and single-cell transcriptomic analysis. Functional experiments were conducted for confirmation.
ResultsSixteen age-associated SL genes were identified. NEK2, IBSP, and PYCR1 showed strong diagnostic value (AUC > 0.90), enriched in cell cycle, DNA repair, and drug resistance pathways. MR consistently confirmed SLC7A5 as a robust candidate gene, linking metabolic regulation to BRCA risk.
ConclusionsAge-associated SL genes play critical roles in BRCA, with SLC7A5 highlighted as a promising biomarker and therapeutic target. These findings provide insights for early diagnosis and metabolism-based precision therapy.