The landscape in telomere related gene prognostic signature for survival and medication treatment effectiveness prediction in hepatocellular carcinoma

Abstract

Objective Telomeres, made of repetitive DNA sequences and shelterin complexes, which were found at the ends of chromosomes and had been extensively studied in cancer research. However, in hepatocellular carcinoma (HCC) was still relatively scarce. In this study, we investigated the correlation between telomerase-related genes (TRGs) and the prognosis and immunotherapy of HCC patients to enhance clinical outcomes. Methods In this work, TRGs were gathered using TelNet, while clinical information and gene expression data for HCC patients were retrieved from the Cancer Genome Atlas (TCGA) database. A risk prediction model based on TRGs was created using COX and Lasso regression analyses, with ROC curves used to assess prognostic efficacy. Univariate and multifactorial COX regression analyses were used to determine if the risk model had an independent impact on prognosis. Nomograms were created to enhance clinical usability, and calibration curves were used to assess predictive ability at various time points. The Tumor Immune Dysfunction and Exclusion (TIDE) score was used to analyze differences in immune infiltrating cells between risk groups. The study analyzed the relationship between risk ratings and drug treatment effectiveness using data from the CellMiner database. The hub gene was identified and its relationship to prognostic markers of HCC patients was examined. The expression of hub genes in immune cell subpopulations was also investigated by single-cell data. Results 2093 TRGs were identified, with 949 showing significant differences in expression between HCC and paracancerous tissues. Seven risk genes were overexpressed in tumor tissues, leading to lower survival rates in high-risk patients. Risk model could independently predict the prognosis of HCC patients. Analysis of tumor immune infiltrating cells revealed significant differences in cell abundance between risk groups, with notable variations in immune subset enrichment between subgroups. Higher risk scores correlated with increased sensitivity to sorafenib, mitoxantrone, oxaliplatin, gemcitabine, and entinostat, while sensitivity decreased for vincristine, etc. CDCA8 was identified as a key gene in the Protein Interaction Network, while high expression associated with poorer overall survival, tumor proliferation and metastasis. The results of single-cell data analysis suggest that CDCA8 may promote the development of HCC by affecting T lymphocytes. Conclusion The TRG-based risk model could predict HCC patient prognosis and closely linked to tumor immune environment, which could offer new possibilities for clinical treatment. Supplementary Information The online version contains supplementary material available at 10.1007/s12672-024-01659-w.