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

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.