Integrative analysis of senescence-related genes identifies robust prognostic clusters with distinct features in hepatocellular carcinoma

Sicheng Liu; Yang Meng; Yaguang Zhang; Lei Qiu; Xiaowen Wan; Xuyang Yang; Yang Zhang; Xueqin Liu; Linda Wen; Xue Lei; Bo Zhang; Junhong Han

doi:10.1016/j.jare.2024.04.007

Integrative analysis of senescence-related genes identifies robust prognostic clusters with distinct features in hepatocellular carcinoma

J Adv Res. 2024 Apr 13:S2090-1232(24)00150-4. doi: 10.1016/j.jare.2024.04.007. Online ahead of print.

Authors

Sicheng Liu¹, Yang Meng¹, Yaguang Zhang¹, Lei Qiu¹, Xiaowen Wan¹, Xuyang Yang², Yang Zhang², Xueqin Liu¹, Linda Wen¹, Xue Lei¹, Bo Zhang², Junhong Han³

Affiliations

¹ Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, and Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610041, China.
² Research Laboratory of Cancer Epigenetics and Genomics, Department of General Surgery, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610041, China.
³ Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, and Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610041, China. Electronic address: hjunhong@scu.edu.cn.

PMID: 38614215
DOI: 10.1016/j.jare.2024.04.007

Abstract

Introduction: Senescence refers to a state of permanent cell growth arrest and is regarded as a tumor suppressive mechanism, whereas accumulative evidence demonstrate that senescent cells play an adverse role during cancer progression. The scarcity of specific and reliable markers reflecting senescence level in cancer impede our understanding of this biological basis.

Objectives: Senescence-related genes (SRGs) were collected for integrative analysis to reveal the role of senescence in hepatocellular carcinoma (HCC).

Methods: Consensus clustering was used to subtype HCC based on SRGs. Several computational methods, including single sample gene set enrichment analysis (ssGSEA), fuzzy c-means algorithm, were performed. Data of drug sensitivities were utilized to screen potential therapeutic agents for different senescence patients. Additionally, we developed a method called signature-related gene analysis (SRGA) for identification of markers relevant to phenotype of interest. Experimental strategies consisting quantitative real-time PCR (qRT-PCR), β-galactosidase assay, western blot, and tumor-T cell co-culture system were used to validate the findings in vitro.

Results: We identified three robust prognostic clusters of HCC patients with distinct survival outcome, mutational landscape, and immune features. We further extracted signature genes of senescence clusters to construct the senescence scoring system and profile senescence level in HCC at bulk and single-cell resolution. Senescence-induced stemness reprogramming was confirmed both in silico and in vitro. HCC patients with high senescence were immune suppressed and sensitive to Tozasertib and other drugs. We suggested that MAFG, PLIN3, and 4 other genes were pertinent to HCC senescence, and MAFG potentially mediated immune suppression, senescence, and stemness.

Conclusion: Our findings provide insights into the role of SRGs in patients stratification and precision medicine.

Keywords: Computational method; Hepatocellular carcinoma; Prognostic clusters; Senescence-associated stemness; Senescence-related genes; Therapeutic strategy.