Genes · 2026
Integrated Single-Cell and Bulk RNA-Sequencing Analysis Identifies an Aging-Related Gene Signature for Prognosis in Breast Cancer.
СтатьяНазвание и аннотация: Перевод в очереди
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Chen Pengcheng, Lin Yindan, Li Jingjia, Gu Yiwei, Zhang Xueyun
Аннотация
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.
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.