Abstract

Hepatocellular carcinoma (HCC), a leading liver tumor globally, is influenced by diverse risk factors. Cellular senescence, marked by permanent cell cycle arrest, plays a crucial role in cancer biology, but its markers and roles in the HCC immune microenvironment remain unclear. Three machine learning methods, namely k nearest neighbor (KNN), support vector machine (SVM), and random forest (RF), are utilized to identify eight key HCC cell senescence markers (HCC-CSMs). Consensus clustering revealed molecular subtypes. The single-cell analysis explored the tumor microenvironment, immune checkpoints, and immunotherapy responses. In vitro, RNA interference mediated knockdown, and co-culture experiments assessed its impact. Cellular senescence-related genes predicted HCC survival information better than differential expression genes (DEGs). Eight key HCC-CSMs were identified, which revealed two distinct clusters with different clinical characteristics and mutation patterns. By single-cell RNA-seq data, we investigated the immunological microenvironment and observed that increasing immune cells allow hepatocytes to regain population dominance. This phenomenon may be associated with the HCC-CSMs identified in our study. By combining bulk RNA sequencing and single-cell RNA sequencing data, we identified the key gene and the natural killer (NK) cells that express at the highest levels. knockdown increased NK cell proliferation but reduced function, potentially aiding tumor survival. These findings provide insights into senescence-driven HCC progression and potential therapeutic targets.

MeSH

Cells, Genes, Carcinoma, Hepatocellular, Killer Cells, Natural, Neoplasms, Cellular Senescence, Machine Learning, Learning

Cited by

• Cellular senescence in cancer: from mechanism paradoxes to precision therapeutics. Mol Cancer, 2025