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 (LASS

SUBMITTER: Lu Z 

PROVIDER: S-EPMC11921577 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature