Integrative Multidimensional Analysis of Age-Associated Synthetic Lethal Genes and Development of a Prognostic Model in Breast Cancer

Qinqing Wu¹Dong Xu Ma²Heng Cao³WANG Xiang²Wenjuan Zhang⁴Wenbing Zhang^5*

• ¹Shantou University Medical College, Shantou, China
• ²Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
• ³Beijing Chaoyang Hospital Affiliated to Capital Medical University, Beijing, China
• ⁴Qingpu Branch of Zhongshan Hospital Affiliated to Fudan University, Shanghai, China
• ⁵Anhui Medical University, Hefei, China

Background: Breast 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. Methods: We 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. Results: Sixteen 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. Conclusions: Age-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.