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

Oct 26, 2025Bioscience trends

How Aging Changes the Cancer Environment and Key Cells Across Many Cancer Types Revealed by Single-Cell Analysis

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Longevity & Aging on OpenScience ↗PubMed ↗DOI ↗

Abstract

60% of new malignancies occur in adults age 65 and older.

Aging is linked to the accumulation of senescent cell subpopulations in tissues, reaching frequencies up to 15%.
Senescent cells drive genomic instability and secrete pro-tumorigenic factors such as IL-6 and TGF-β.
These factors contribute to tumor microenvironment remodeling, leading to a 40-70% acceleration in metastasis in murine models.
Immunosenescence in aged tumor microenvironments is associated with 40-60% increases in exhausted immune cells.
Conserved aging gene signatures correlate with 30-50% poorer survival across 12 or more cancer types.
Senolytic strategies that deplete senescent cells have been shown to improve drug response by 3.5-fold in preclinical trials.

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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. 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/LMNB1cells) 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-1T 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. + -+