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Frontiers in genetics
2022;13 1099148.

A cellular senescence-related genes model allows for prognosis and treatment stratification of hepatocellular carcinoma: A bioinformatics analysis and experimental verification.

Jiaming Li, Rongzhi Tan, Jie Wu, Wenjie Guo, Yupeng Wang, Guoxing You, Yuting Zhang, Zhiyong Yu, Yan Geng, Jie Zan, Jianfen Su
Author Information
  1. Jiaming Li: School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, Guangzhou, China.
  2. Rongzhi Tan: School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, Guangzhou, China.
  3. Jie Wu: The Second School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong, China.
  4. Wenjie Guo: School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, Guangzhou, China.
  5. Yupeng Wang: School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, Guangzhou, China.
  6. Guoxing You: School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, Guangzhou, China.
  7. Yuting Zhang: School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, Guangzhou, China.
  8. Zhiyong Yu: School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, Guangzhou, China.
  9. Yan Geng: School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, Guangzhou, China.
  10. Jie Zan: School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, Guangzhou, China.
  11. Jianfen Su: Department of Pharmacy, Guangzhou Panyu Central Hospital, Guangzhou, China.
PMID: 36712870 DOI: 10.3389/fgene.2022.1099148

Abstract

Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer with low 5-year survival rate. Cellular senescence, characterized by permanent and irreversible cell proliferation arrest, plays an important role in tumorigenesis and development. This study aims to develop a cellular senescence-based stratified model, and a multivariable-based nomogram for guiding clinical therapy for HCC. The mRNAs expression data of HCC patients and cellular senescence-related genes were obtained from TCGA and CellAge database, respectively. Through multiple analysis, a four cellular senescence-related genes-based prognostic stratified model was constructed and its predictive performance was validated through various methods. Then, a nomogram based on the model was constructed and HCC patients stratified by the model were analyzed for tumor mutation burden, tumor microenvironment, immune infiltration, drug sensitivity and immune checkpoint. Functional enrichment analysis was performed to explore potential biological pathways. Finally, we verified this model by siRNA transfection, scratch assay and Transwell Assay. We established an cellular senescence-related genes-based stratified model, and a multivariable-based nomogram, which could accurately predict the prognosis of HCC patients in the ICGC database. The low and high risk score HCC patients stratified by the model showed different tumor mutation burden, tumor microenvironment, immune infiltration, drug sensitivity and immune checkpoint expressions. Functional enrichment analysis suggested several biological pathways related to the process and prognosis of HCC. Scratch assay and transwell assay indicated the promotion effects of the four cellular senescence-related genes (EZH2, G6PD, CBX8, and NDRG1) on the migraiton and invasion of HCC. We established a cellular senescence-based stratified model, and a multivariable-based nomogram, which could predict the survival of HCC patients and guide clinical treatment.

Keywords

cellular senescence clinical treatment hepatocellular carcinoma immune checkpoint precision medication stratified model

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