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Deciphering the nexus of aging and pan-cancer: Single-cell sequencing reveals microenvironmental remodeling and cellular drivers.

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Bioscience trends 2025 Vol.19(5) p. 511-520
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Han Y, Chen N, Wang P, Zhou C, Karako K, Song P, Tang W

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Aging constitutes a major risk factor for pan-cancer development, with epidemiological studies indicating that 60% of new malignancies occur in adults age 65 and older.

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APA Han Y, Chen N, et al. (2025). Deciphering the nexus of aging and pan-cancer: Single-cell sequencing reveals microenvironmental remodeling and cellular drivers.. Bioscience trends, 19(5), 511-520. https://doi.org/10.5582/bst.2025.01307
MLA Han Y, et al.. "Deciphering the nexus of aging and pan-cancer: Single-cell sequencing reveals microenvironmental remodeling and cellular drivers.." Bioscience trends, vol. 19, no. 5, 2025, pp. 511-520.
PMID 41139485 ↗

Abstract

Aging constitutes a major risk factor for pan-cancer development, with epidemiological studies indicating that 60% of new malignancies occur in adults age 65 and older. This review synthesizes cutting-edge insights from single-cell sequencing databases (e.g., TCGA and GEO) that decipher how aging reprograms the tumor microenvironment (TME) to fuel carcinogenesis. Single-cell RNA sequencing (scRNA-seq) has revealed that senescent cell subpopulations (e.g., CDKN2A/LMNB1 cells) accumulate in aged tissues at frequencies up to 15%, driving genomic instability and secrete pro-tumorigenic senescence-associated secretory phenotype (SASP) factors (IL-6 and TGF-β). These factors remodel the TME by inducing fibroblast activation and extracellular matrix degradation, accelerating metastasis by 40-70% in murine models. Crucially, immunosenescence diminishes anti-tumor immunity, with scRNA-seq profiling showing 40-60% increases in exhausted PD-1 T cells and immunosuppressive myeloid cells in aged TMEs. Pan-cancer analyses have identified conserved aging gene signatures (e.g., p16INK4a upregulation in 12+ cancer types) that correlate with 30-50% poorer survival. While technical challenges persist - including batch effects in scRNA-seq data and low senescent cell abundance (< 5%) - emerging solutions like deep learning can enhance detection sensitivity. Therapeutically, senolytic strategies deplete senescent cells, improving drug response by 3.5-fold in preclinical trials. Future research must integrate multi-omics and AI to examine aging-related targets, advancing personalized interventions for aging-associated malignancies.

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