Multi-cohort and single-cell profiling of aging genes reveals prognostic and therapeutic targets in breast cancer.

Aging-related transcriptional programs shape breast cancer progression, immune regulation, and therapeutic response. We integrated curated aging-associated genes with 108 survival modeling strategies to derive a machine learning-based aging gene signature. Trained in the TCGA-BRCA cohort and evaluated across twelve independent datasets, the eight-gene MLAG score consistently stratified patients by survival risk across clinical contexts. High MLAG exhibited increased genomic instability, including elevated tumor mutational burden and copy number alterations, together with distinct regulatory and intercellular communication patterns. In contrast, low MLAG showed enhanced immune infiltration, coordinated microenvironment signaling, and higher immune checkpoint expression. Integration of bulk and single-cell transcriptomic analyses localized MLAG-associated aging programs primarily to malignant epithelial cells and linked them to aneuploidy and stress-response pathways. The MLAG score associated with immune checkpoint blockade response and supported identification of candidate therapeutic strategies. These findings highlight the relevance of aging-associated transcriptional states in breast cancer prognosis and treatment stratification.