ArticleMolecular therapy. Nucleic acids2024
Molecular classification of geriatric breast cancer displays distinct senescent subgroups of prognostic significance.
Article in Molecular therapy. Nucleic acids, 2024. 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.
- Age-related aberrant alternative splicing as a prognostic tool in older breast cancer patients.Communications biology · 2025Article
- Microneedles as Gateways: Smart Nanoparticle Delivery for Enhanced Breast Cancer Treatment.ACS omega · 2025Review
- Machine learning and single-cell analysis uncover distinctive characteristics of CD300LG within the TNBC immune microenvironment: experimental validation.Clinical and experimental medicine · 2025Article
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25 authors.
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Abstract
Breast cancer in the elderly presents distinct biological characteristics and clinical treatment responses compared with cancer in younger patients. Comprehensive Geriatric Assessment is recommended for evaluating treatment efficacy in elderly cancer patients based on physiological classification. However, research on molecular classification in older cancer patients remains insufficient. In this study, we identified two subgroups with distinct senescent clusters among geriatric breast cancer patients through multi-omics analysis. Using various machine learning algorithms, we developed a comprehensive scoring model called "Sene_Signature," which more accurately distinguished elderly breast cancer patients compared with existing methods and better predicted their prognosis. The Sene_Signature was correlated with tumor immune cell infiltration, as supported by single-cell transcriptomics, RNA sequencing, and pathological data. Furthermore, we observed increased drug responsiveness in patients with a high Sene_Signature to treatments targeting the epidermal growth factor receptor and cell-cycle pathways. We also established a user-friendly web platform to assist investigators in assessing Sene_Signature scores and predicting treatment responses for elderly breast cancer patients. In conclusion, we developed a novel model for evaluating prognosis and therapeutic responses, providing a potential molecular classification that assists in the pre-treatment assessment of geriatric breast cancer.
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