A novel telomere-associated genes signature for the prediction of prognosis and treatment responsiveness of hepatocellular carcinoma.

Background: Hepatocellular carcinoma (HCC) is a prevalent malignancy worldwide, characterized by its high malignancy and poor prognosis. Telomeres, crucial components of eukaryotic chromosomes, have been increasingly recognized for their involvement in tumorigenesis, development, and impact on the prognosis of cancer patients. However, the precise role of telomere-associated genes in HCC remains incompletely elucidated.

Methods: The Cancer Genome Atlas (TCGA) database was utilized to download data from 374 HCC and 50 normal liver tissue samples. Differential genes were screened and intersected with 2093 telomere-related genes (TRGs) in GeneCards, resulting in the identification of 704 TRGs exhibiting survival differences. Through univariate Cox regression analysis, multivariate Cox regression analysis, and LASSO regression, a prognostic model consisting of 18 TRGs for HCC risk assessment was developed. The single-cell and spatial transcriptomics were utilized to analyze the expression and distribution of 18 TRGs in HCC. Subsequently, Mendelian randomization (MR) analysis confirmed a causal relationship between ASF1A and alcoholic HCC among the identified 18 TRGs. The expression and functional significance of ASF1A in HCC cell lines were investigated through colony formation assays, Transwell migration assays, and wound healing experiments.

Results: We developed a prognostic risk model for HCC incorporating 18 TRGs. Kaplan-Meier analysis demonstrated that the overall survival (OS) rate of the high-risk group was significantly inferior to that of the low-risk group. Cox regression analysis identified age (HR = 1.017, 95% CI: 1.002-1.032, P = 0.03), stage (HR = 1.389, 95% CI: 1.111-1.737, P = 0.004), and risk score (HR = 5.097, 95% CI: 3.273-7.936, P < 0.001) as three independent risk factors for HCC patients. The five-year receiver operating characteristic curve (ROC) and multivariate Cox regression analysis further validated the accuracy of our model. Time-dependent ROC results revealed that the 1-year, 3-year, and 5-year AUC values were AUC = 0.801, AUC = 0.734, and AUC = 0.690, respectively. The expression and distribution of 18 TRGs in HCC were further validated through single-cell and spatial transcriptomics data. Additionally, immune subtype analysis indicated a significantly lower proportion of C3 and C4 subtypes in the high-risk TRG group compared to the low-risk group. Meanwhile, tumor immune dysfunction and exclusion (TIDE) were significantly higher in the high-risk group than in the low-risk group. Furthermore, we observed differences in IC50 values among nine chemotherapeutic drugs across different TRG risk subtypes which partially confirmed our model's predictive efficacy for immunotherapy. Amongst these eighteen TRGs analyzed by MR analysis, ASF1A was found to be associated with alcoholic HCC pathogenesis. We further confirmed ASF1A was significant overexpression in HCC by Western blotting. We also explored it's the carcinogenic role of ASF1A in HCC via the transwell, wound healing, and clone formation experiments.

Conclusion: In this study, we developed a novel prognostic model comprising 18 TRGs for HCC, which exhibited remarkable accuracy in predicting HCC patients' prognosis. Additionally, through MR analysis, we have successfully established a causal relationship between ASF1A and alcoholic HCC for the first time, which also provided a new theoretical foundation for the management of alcoholic HCC.