Machine learning-based identification of telomere-related gene signatures for prognosis and immunotherapy response in hepatocellular carcinoma.

Abstract

Telomere in cancers shows a main impact on maintaining chromosomal stability and unlimited proliferative capacity of tumor cells to promote cancer development and progression. So, we targeted to detect telomere-related genes(TRGs) in hepatocellular carcinoma (HCC) to develop a novel predictive maker and response to immunotherapy. We sourced clinical data and gene expression datasets of HCC patients from databases including TCGA and GEO database. The TelNet database was utilized to identify genes associated with telomeres. Genes with altered expression from TCGA and GSE14520 were intersected with TRGs, and Cox regression analysis was conducted to pinpoint genes strongly linked to survival prognosis. The risk model was developed using the Least Absolute Shrinkage and Selection Operator (LASSO) regression technique. Subsequently, evaluation of the risk model focused on immune cell infiltration, checkpoint genes, drug responsiveness, and immunotherapy outcomes across both high- and low-risk patient groups. We obtained 25 TRGs from the overlapping set of 34 genes using Cox regression analysis. Finally, six TRGs (CDC20, TRIP13, EZH2, AKR1B10, ESR1, and DNAJC6) were identified to formulate the risk score (RS) model, which independently predicted prognosis for HCC. The high-risk group demonstrated worse survival outcomes and showed elevated levels of infiltration by Macrophages M0 and Tregs. Furthermore, a notable correlation was observed between the genes in the risk model and immune checkpoint genes. The RS model, derived from TRGs, has been validated for its predictive value in immunotherapy outcomes. In conclusion, this model not only predicted the prognosis of HCC patients but also their immune responses, providing innovative strategies for cancer therapy.