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Updated: May 13, 2025
Author Spotlight: Investigating Immune Cell Dynamics in the Tumor Microenvironment — Challenges and Innovations in Cancer Prognosis
Published on: April 12, 2024
A gene signature associated with cellular senescence serves as an important prognostic indicator in hepatocellular
Yongfeng Yan1, Kai Chen1, Qian Sun1
1Department of Laboratory, Tianjin Beichen Hospital, Tianjin, China.
Background:
Hepatocellular carcinoma (HCC) is a lethal tumor. Predicting the prognosis of HCC remains challenging. Cellular senescence, which is one of the hallmarks of cancer, and its related prognostic-gene signature can provide critical information for clinical decision making. Our objective was to investigate the role of cellular senescence in HCC.
Methods:
The RNA sequencing data and clinical information of HCC patients from The Cancer Genome Atlas (TCGA) database were obtained. The HCC subtypes and a senescence score model were established to predict the prognosis of HCC.
Results:
In this study, patients from TCGA-HCC dataset were stratified into low- and high-risk groups based on cellular senescence-related genes. The analysis of the various subtypes revealed that the distribution of Cluster 1 (C1) was significantly correlated with numerous factors, including age, sex, pathological T stage, tumor node metastasis (TNM) classification, and grade staging. Further, the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis indicated that the upregulated genes in the high-risk C1 group were primarily engaged in pathways related to the cell cycle, DNA replication, cellular senescence, extracellular matrix (ECM)-receptor interactions, and the mechanisms of mismatch repair. Conversely, the 90 downregulated genes were mainly associated with metabolic pathways, chemical carcinogenesis involving DNA adducts, complement and coagulation cascades, and the peroxisome proliferator-activated receptor (PPAR) signaling pathway. The resultant boxplots revealed significant differences in the populations of immune cells, such as B cells, endothelial cells, natural killer (NK) cells, macrophages, cluster of differentiation (CD)4+ T cells, and CD8+ T cells, in the C1 HCC samples compared to the C2 HCC samples. Additionally, the prognostic outcomes of the HCC patients were predicted using a cellular senescence-related gene model that included VDAC2, CXCL8, MYBL2, RAD9A, LIN52, RHEB, GADD45G, E2F5, MAP2K2, CDC25A, PPP1CB, and HRAS.
Conclusions:
This study established a prognostic model of HCC based on cellular senescence-related gene expression. Our findings may provide insights that can be used to develop novel potential targeted therapies.
