Abstract

Plain Language Summary Purpose: Aging and metabolic syndrome (MetS) are intertwined risk factors for breast cancer (BC), but the core molecular nexus integrating these states is unknown. This study aimed to identify and validate a causal driver at this intersection. Methods: We integrated transcriptomic datasets from BC, MetS, and aging cohorts to identify common dysregulated genes. Machine learning algorithms prioritized a core diagnostic signature. We used Mendelian randomization to infer causality and characterized the lead candidate using single-cell RNA sequencing and comprehensive preclinical validation. Results: Our analysis identified a 25-gene core at the intersection of BC, MetS, and aging. Machine learning distilled this to five hub genes that formed a highly accurate diagnostic nomogram. Critically, Mendelian randomization established MCRS1 as the sole causal risk factor for BC among these candidates. Single-cell analysis revealed that Mcrs1 is predominantly expressed in proliferating cancer cells, where it drives a transcriptional program of enhanced cell cycle, senescence, and metabolic reprogramming. Accordingly, genetic knockdown of Mcrs1 profoundly suppressed BC cell proliferation and invasion in vitro , and in vivo experiments using an orthotopic BC model in C57BL/6 mice demonstrated significantly reduced tumor growth. Conclusion: This study identifies MCRS1 as a central molecular hub that causally links aging and MetS to BC pathogenesis. MCRS1 is a validated driver of tumor progression and a high-performance biomarker, representing a potential target for therapeutic development, particularly in BC patients with metabolic comorbidities. Plain Language Summary This study explored the connection between aging, metabolic syndrome (MetS), and breast cancer (BC) by identifying key genes involved. Researchers analyzed gene data from BC, MetS, and aging groups, finding 25 common genes. Machine learning highlighted five crucial genes, with MCRS1 emerging