ArticleFrontiers in immunology2025
Integrative multidimensional analysis of age-associated synthetic lethal genes and development of a prognostic model in breast cancer.
Article in Frontiers in immunology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- MRI quantification of intratumoral heterogeneity for predicting HER2-positive status in breast cancer: a retrospective multicenter study.BMC cancer · 2026Article
- Integrated bioinformatics and mendelian randomization reveal a six-gene diagnostic signature and key role of CYP26B1 in sarcopenia.Frontiers in molecular biosciences · 2026Article
- Macrophage-associated prognostic modeling uncovers immunotherapy response mechanisms and defines HAGHL as a novel oncogenic driver in breast cancer.Frontiers in immunology · 2026Article
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6 authors.
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Abstract
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.
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