Abstract
Aging is a fundamental biological process that influences cancer development in a context-dependent manner; however, how aging-related programs manifest in hepatocellular carcinoma (HCC) remains incompletely understood. Here, we systematically characterized aging-associated features in HCC by establishing a liver cancer-specific aging signature, termed HCCaging, across more than 2,000 tumor samples from 16 independent cohorts. We comprehensively evaluated its heterogeneity and associations with clinical outcomes, tumor stage, immune infiltration, and therapeutic response. The HCCaging score increased with chronological age, was higher in normal liver than tumor tissues, and elevated in early- versus late-stage tumors. In contrast, 13 previously reported aging- or senescence-related gene sets failed to show consistent patterns across these conditions in HCC. Machine learning models, including gradient boosting machines and random forests, achieved higher accuracy in distinguishing tumor from non-tumor samples using the HCCaging score compared with other 13 aging- or senescence-gene sets across eight independent HCC cohorts. Single-cell transcriptomic profiling revealed that HCCaging increased with age, particularly within epithelial compartments, reaching its highest levels in hepatocytes. Notably, although the proportion of T/NK cells declined with aging, their functional programs, including activated effector function, chemokine/chemokine receptor signaling, cytolytic activity, and pro-inflammatory pathways, were enhanced in older individuals. The HCCaging score, together with key genes ACAA1 and ESR1, were negatively correlated with T/NK cell infiltration, anti-inflammatory activity, and anti-apoptotic signatures, but positively correlated with pro-apoptotic, pro-inflammatory, chemokine, and cytolytic pathways. Furthermore, increased expression of XCL1 and XCL2 in T/NK cells with aging correlated positively with HCCaging, ACAA1, and ESR1, suggesting preserved or even enhanced antitumor potential of T/NK cells in older patients. Collectively, these findings highlight the dual role of aging in liver tumorigenesis. Hepatic aging and enhanced T/NK cell effector function may confer tumor-protective effects, whereas the concomitant decline in overall T/NK cell infiltration likely compromises immunosurveillance, thereby increasing carcinogenic susceptibility in the aging liver. This study provides new insights into the heterogeneity of hepatic aging and its complex interplay with the HCC tumor microenvironment and clinical outcomes.
Similar content being viewed by others
Data availability
The analysis incorporated data from public repositories, including bulk sequencing and microarray data from TCGA-LIHC, Cancer Genome Consortium (ICGC), NODE (https://www.biosino.org/node) (OEP000321), National genomics data center, omics raw data archive (OMIX010473) and Gene Expression Omnibus (GEO) database (GSE76427, GSE124751, GSE10143, GSE14520, GSE25097 and GSE63898). The single cell data was obtained from GSE149614, CNP0000650, Genome Sequence Archive at the National Genomics Data Center (PRJCA007744, requested data from authors), GSE235863, GSE151530, GSE125449 and GSE202379. Spatially resolved transcriptomic data were downloaded from the CROST database (VISDP000084).
Code availability
Custom code used for results analysis and figure generation is available on GitHub https://github.com/bioinfo-by-wei/HCCaging).
References
Karsidag, I. & Liu, H. The Recent Changing Global Landscape of Cancer. Life Conflux. https://doi.org/10.71321/e37ss652 (2025).
Fane, M. & Weeraratna, A. T. How the ageing microenvironment influences tumour progression. Nat. Rev. Cancer 20, 89–106 (2020).
Cai, X., Guillot, A. & Liu, H. Cellular senescence in hepatocellular carcinoma: the passenger or the driver. Cells 12, 132 (2022).
Gomes, A. P. et al. Age-induced accumulation of methylmalonic acid promotes tumour progression. Nature 585, 283–287 (2020).
Yan, P. et al. Midkine as a driver of age-related changes and increase in mammary tumorigenesis. Cancer Cell 42, 1936–1954.e9 (2024).
Gurung, S. et al. Stromal lipid species dictate melanoma metastasis and tropism. Cancer Cell. 43, 1108–1124.e11 (2025).
Saul, D. et al. A new gene set identifies senescent cells and predicts senescence-associated pathways across tissues. Nat. Commun. 13, 4827 (2022).
Avelar, R. A. et al. A multidimensional systems biology analysis of cellular senescence in aging and disease. Genome Biol. 21, 91 (2020).
de Magalhães, J. P. & Toussaint, O. GenAge: a genomic and proteomic network map of human ageing. FEBS Lett. 571, 243–7 (2004).
Saul, D. & Kosinsky, R. L. Single-cell transcriptomics reveals the expression of aging- and senescence-associated genes in distinct cancer cell populations. Cells 10, 3126 (2021).
Takahashi, A. et al. Downregulation of cytoplasmic DNases is implicated in cytoplasmic DNA accumulation and SASP in senescent cells. Nat. Commun. 9, 1249 (2018).
Wang, J. et al. A transcriptome-based human universal senescence index (hUSI) robustly predicts cellular senescence under various conditions. Nat. Aging 5, 1159–1175 (2025).
Kamat, P. et al. Single-cell morphology encodes functional subtypes of senescence in aging human dermal fibroblasts. Sci. Adv. 11, eads1875 (2025).
Chatsirisupachai, K., Palmer, D., Ferreira, S. & de Magalhães, J. P. A human tissue-specific transcriptomic analysis reveals a complex relationship between aging, cancer, and cellular senescence. Aging Cell 18, e13041 (2019).
Shah, Y. et al. Pan-cancer analysis reveals molecular patterns associated with age. Cell Rep. 37, 110100 (2021).
Bray, F. et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 74, 229–263 (2024).
Rumgay, H. et al. Global, regional and national burden of primary liver cancer by subtype. Eur. J. Cancer 161, 108–118 (2022).
He, Y. et al. The cellular senescence score (CSS) is a comprehensive biomarker to predict prognosis and assess senescence and immune characteristics in hepatocellular carcinoma (HCC). Biochem Biophys. Res Commun. 739, 150576 (2024).
Zhang, Q. et al. Comprehensive pan-cancer analysis identifies cellular senescence as a new therapeutic target for cancer: multi-omics analysis and single-cell sequencing validation. Am. J. Cancer Res. 12, 4103–4119 (2022).
Chhabra, Y. et al. Sex-dependent effects in the aged melanoma tumor microenvironment influence invasion and resistance to targeted therapy. Cell 187, 6016–6034.e25 (2024).
Kang, T. W. et al. Senescence surveillance of pre-malignant hepatocytes limits liver cancer development. Nature 479, 547–51 (2011).
Wang, X. et al. Comprehensive assessment of cellular senescence in the tumor microenvironment. Brief. Bioinform 23, bbac118 (2022).
Li, K. et al. Pan-cancer characterization of cellular senescence reveals its inter-tumor heterogeneity associated with the tumor microenvironment and prognosis. Comput Biol. Med 182, 109196 (2024).
Tian, M. et al. ISENICS: a model for identifying senescent immune cells and samples and characterization of their roles in tumor microenvironment. Brief. Bioinform 26, bbaf469 (2025).
Angarola, B. L. et al. Comprehensive single-cell aging atlas of healthy mammary tissues reveals shared epigenomic and transcriptomic signatures of aging and cancer. Nat. Aging 5, 122–143 (2025).
Wang, J. H., Wong, R. & Liu, G. S. Retinal aging transcriptome and cellular landscape in association with the progression of age-related macular degeneration. Invest Ophthalmol. Vis. Sci. 64, 32 (2023).
Sanfilippo, C. et al. Synaptic pruning genes networks in Alzheimera’s disease: correlations with neuropathology and cognitive decline. Geroscience 48, 1291–1312 (2025).
Bhat, A. G. & Ramanathan, M. Artificial intelligence modeling of biomarker-based physiological age: impact on phase 1 drug-metabolizing enzyme phenotypes. CPT Pharmacomet. Syst. Pharm. 14, 302–316 (2025).
Altab, G. et al. Unravelling the transcriptomic symphony of muscle ageing: key pathways and hub genes altered by ageing and caloric restriction in rat muscle revealed by RNA sequencing. BMC Genom. 26, 29 (2025).
Martínez-Gutiérrez, L. et al. Cross-Trait Meta-Analysis Reveals a Genetic Link between Inflammation and Aging in Giant Cell Arteritis. Aging Dis. https://doi.org/10.14336/AD.2025.0609 (2025).
Hong, M. G. et al. Profiles of histidine-rich glycoprotein associate with age and risk of all-cause mortality. Life Sci. Alliance 3, e202000817 (2020).
Matsuzaki, T. et al. Transthyretin deposition promotes progression of osteoarthritis. Aging Cell 16, 1313–1322 (2017).
Lu, H. et al. RNA-sequencing quantification of hepatic ontogeny and tissue distribution of mRNAs of phase II enzymes in mice. Drug Metab. Dispos. 41, 844–57 (2013).
Schluessel, S. et al. 11-beta-hydroxysteroid dehydrogenase type 1 (HSD11B1) gene expression in muscle is linked to reduced skeletal muscle index in sarcopenic patients. Aging Clin. Exp. Res. 35, 3073–3083 (2023).
Lomniczi, A. et al. Age-related increase in the expression of 11β-hydroxysteroid dehydrogenase type 1 in the hippocampus of male rhesus macaques. Front Aging Neurosci. 16, 1328543 (2024).
Yang, P. et al. From aldehyde metabolism to delay aging: targeting ALDH2 as a novel strategy. Free Radic. Biol. Med. 236, 70–86 (2025).
Yoon, M., Madden, M. C. & Barton, H. A. Developmental expression of aldehyde dehydrogenase in rat: a comparison of liver and lung development. Toxicol. Sci. 89, 386–98 (2006).
Parsons, A. et al. Cell populations in human breast cancers are molecularly and biologically distinct with age. Nat. Aging. 5, 2546–2563 (2025).
Zhang, Z. et al. A panoramic view of cell population dynamics in mammalian aging. Science 387, eadn3949 (2025).
Li, S. et al. Multi-omics spatial characteristics of CD8(+)TRM cells in hepatocellular carcinoma and immunotherapy response prediction. Front Immunol. 16, 1710741 (2025).
Shimada, K. et al. An estrogen receptor signaling transcriptional program linked to immune evasion in human hormone receptor-positive breast cancer. bioRxiv 11, 619172 (2024). 2024[pii].
Arima, J. et al. ESR1 expression negatively correlates with immune cell infiltration and response to immune checkpoint inhibitors in estrogen receptor-positive/HER2-negative breast cancer. Ann. Surg. Oncol. 33, 758–768 (2026).
Zhang, G. Prognostic clinical phenotypes associated with tumor stemness in the immune microenvironment of T-cell exhaustion for hepatocellular carcinoma. Discov. Oncol. 14, 203 (2023).
Gao, Q. et al. Integrated proteogenomic characterization of HBV-related hepatocellular carcinoma. Cell. 179, 561–577.e22 (2019).
Grinchuk, O. V. et al. Tumor-adjacent tissue co-expression profile analysis reveals pro-oncogenic ribosomal gene signature for prognosis of resectable hepatocellular carcinoma. Mol. Oncol. 12, 89–113 (2018).
Kang, H. J. et al. Immunogenomic landscape of hepatocellular carcinoma with immune cell stroma and EBV-positive tumor-infiltrating lymphocytes. J. Hepatol. 71, 91–103 (2019).
Moeini, A. et al. An immune gene expression signature associated with development of human hepatocellular carcinoma identifies mice that respond to chemopreventive agents. Gastroenterology 157, 1383–1397.e11 (2019).
Roessler, S. et al. A unique metastasis gene signature enables prediction of tumor relapse in early-stage hepatocellular carcinoma patients. Cancer Res. 70, 10202–12 (2010).
Tung, E. K. et al. Clinicopathological and prognostic significance of serum and tissue Dickkopf-1 levels in human hepatocellular carcinoma. Liver Int. 31, 1494–504 (2011).
Villanueva, A. et al. DNA methylation-based prognosis and epidrivers in hepatocellular carcinoma. Hepatology 61, 1945–56 (2015).
Lu, Y. et al. A single-cell atlas of the multicellular ecosystem of primary and metastatic hepatocellular carcinoma. Nat. Commun. 13, 4594 (2022).
Sun, Y. et al. Single-cell landscape of the ecosystem in early-relapse hepatocellular carcinoma. Cell 184, 404–421.e16 (2021).
Xue, R. et al. Liver tumour immune microenvironment subtypes and neutrophil heterogeneity. Nature 612, 141–147 (2022).
Guo, X. et al. Contrasting cytotoxic and regulatory T cell responses underlying distinct clinical outcomes to anti-PD-1 plus lenvatinib therapy in cancer. Cancer Cell 43, 248–268.e9 (2025).
Ma, L. et al. Single-cell atlas of tumor cell evolution in response to therapy in hepatocellular carcinoma and intrahepatic cholangiocarcinoma. J. Hepatol. 75, 1397–1408 (2021).
Ma, L. et al. Tumor cell biodiversity drives microenvironmental reprogramming in liver cancer. Cancer Cell 36, 418–430.e6 (2019).
Gribben, C. et al. Acquisition of epithelial plasticity in human chronic liver disease. Nature. 630, 166–173 (2024).
Wang, G. et al. CROST: a comprehensive repository of spatial transcriptomics. Nucleic Acids Res. 52, D882–D890 (2024).
Wu, R. et al. Comprehensive analysis of spatial architecture in primary liver cancer. Sci. Adv. 7, eabg3750 (2021).
Chu, Y. et al. Pan-cancer T cell atlas links a cellular stress response state to immunotherapy resistance. Nat. Med 29, 1550–1562 (2023).
Tang, Z., Kang, B., Li, C., Chen, T. & Zhang, Z. GEPIA2: an enhanced web server for large-scale expression profiling and interactive analysis. Nucleic Acids Res. 47, W556–W560 (2019).
Acknowledgements
This research was funded by the National Natural Science Foundation of China [grant number 32500823], the Education Department of Hainan Province [grant number Hnky2025-24], Hainan Vocational University of Science and Technology General Research Project [HKKY2024-46] and the Hainan Medical University 2024 Talent Introduction Start-up Fund [grant number 2024055].
Author information
Authors and Affiliations
Contributions
Conceptualization: Jinfen Wei and Bin Yu. Data curation: Bin Yu, Meiling Hu and Yajuan Zhang. Formal analysis: Jinfen Wei, Bin Yu, Yong Tang and Meiling Hu. Investigation: Bin Yu, Yajuan Zhang and Meiling Hu. Methodology: Bin Yu and Meiling Hu. Visualization: Bin Yu, Yong Tang and Jinfen Wei. Writing – original draft: Jinfen Wei and Bin Yu. Writing – review and editing: Jinfen Wei. Supervision: Jinfen Wei.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary information
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
About this article
Cite this article
Yu, B., Zhang, Y., Tang, Y. et al. HCCaging: a liver physiological aging-related biomarker for hepatocellular carcinoma diagnosis based on transcriptome data. npj Aging (2026). https://doi.org/10.1038/s41514-026-00370-0
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41514-026-00370-0
