Develop a prognostic and drug therapy efficacy prediction model for hepatocellular carcinoma based on telomere maintenance-associated genes.

BACKGROUND

Hepatocellular carcinoma (HCC) poses a substantial global health challenge because of its grim prognosis and limited therapeutic options. Telomere maintenance mechanisms (TMM) significantly influence cancer progression, yet their prognostic value in HCC remains largely unexamined. This research aims to establish a telomere maintenance-associated genes(TMGs)-based prognostic model using transcriptomic and clinical data to evaluate its effectiveness in predicting patient outcomes in HCC.

METHODS

The identified differentially expressed genes (DEGs) were derived from the analysis of transcriptomic and clinical information sourced from the database of the Cancer Genome Atlas (TCGA) and were cross-referenced with TMGs. Candidate risk factors were initially assessed using univariate Cox regression, subsequently followed by LASSO, and then refined through multivariate Cox regression to establish a risk prediction model. This model's predictive accuracy was validated through Kaplan-Meier(K-M) survival analysis, with external validation in the Gene Expression Omnibus (GEO) dataset. Additionally, a nomogram incorporating age and tumor stage was developed. Tumor mutation burden (TMB), immune profile, and drug sensitivity in HCC were also analyzed. Furthermore, we employed RT-PCR to confirm the expression levels of the genes related to TMGs in HepG2 cell lines.

RESULTS

A prognostic model comprising 3 core genes was constructed, with high-risk individuals showing significantly lower overall survival (OS). The association between elevated TMB and diminished survival in high-risk patients was uncovered through TMB analysis. Immune profiling indicated notable disparities in immune infiltration among these groups, with high-risk patients displaying elevated Tumor Immune Dysfunction and Exclusion (TIDE) scores, suggesting potential immune evasion.

CONCLUSION

In short, our prognosis model based on TMGs effectively categorized HCC patients using risk scores, enabling dependable prognostic forecasts and identification of potential therapeutic targets for personalized treatment in HCC management. Future studies should explore integrating this model into clinical practice to improve patient outcomes.