Integrated Single-Cell and Bulk RNA-Sequencing Analysis Identifies an Aging-Related Gene Signature for Prognosis in Breast Cancer.

BACKGROUND

Cellular senescence exerts a complex influence on BRCA progression and TME remodeling. However, the specific roles of ASIGs in regulating the TME and determining patient outcomes remain unclear.

METHODS

Using TCGA (training), METABRIC (validation), and single-cell RNA-seq datasets, we systematically characterized ASIGs in BRCA. Prognostic ASIGs were identified to define molecular subtypes and construct a 17-gene LASSO-Cox risk model, which was integrated with clinical factors to develop a prognostic nomogram. Microenvironmental features and cell-cell communication networks were deconstructed using computational deconvolution and single-cell algorithms (SCISSOR and CellChat).

RESULTS

We established a robust 17-gene ASIG-based prognostic signature that effectively stratified BRCA patients into high- and low-risk groups and served as an independent prognostic predictor (HR = 3.94, p < 0.001). The nomogram accurately predicted 1-, 3-, and 5-year overall survival. Notably, the two risk groups exhibited strikingly distinct TME landscapes. The low-risk group was characterized by a coordinated, B cell-centric immune network, whereas the high-risk group displayed T cell exhaustion and immunosuppressive myeloid infiltration.

CONCLUSIONS

The ASIG-based prognostic risk model is independent of traditional clinicopathological factors, providing a robust tool for patient risk stratification and offering biological insights into senescence-driven microenvironmental remodeling.