Table 1_Develop a prognostic and drug therapy efficacy prediction model for hepatocellular carcinoma based on telomere maintenance-associated genes.docx

BackgroundHepatocellular 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.
MethodsThe 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.
ResultsA 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.
ConclusionIn 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.

背景 肝细胞癌（Hepatocellular carcinoma, HCC）因预后极差、治疗手段有限，已成为严峻的全球公共卫生挑战。端粒维持机制（Telomere maintenance mechanisms, TMM）可显著影响癌症进展，但其在HCC中的预后价值尚未得到充分研究。本研究旨在基于转录组学与临床数据，构建端粒维持相关基因（Telomere maintenance-associated genes, TMGs）预后模型，以评估其在HCC患者预后预测中的效能。
方法 本研究从癌症基因组图谱（The Cancer Genome Atlas, TCGA）数据库获取转录组学与临床信息，经分析得到差异表达基因（differentially expressed genes, DEGs），并与TMGs进行交叉比对。首先通过单变量Cox回归初步筛选候选风险因素，随后采用LASSO回归筛选，再经多变量Cox回归优化，最终构建风险预测模型。采用Kaplan-Meier（K-M）生存分析验证模型的预测效能，并在基因表达综合数据库（Gene Expression Omnibus, GEO）数据集进行外部验证。此外，本研究还构建了纳入年龄与肿瘤分期的列线图。同时分析了HCC患者的肿瘤突变负荷（Tumor mutation burden, TMB）、免疫特征及药物敏感性。此外，本研究通过逆转录聚合酶链式反应（RT-PCR）验证了HepG2细胞系中TMGs相关基因的表达水平。
结果 本研究构建了包含3个核心基因的预后模型，高风险组患者的总生存期（Overall Survival, OS）显著更短。通过TMB分析发现，高风险患者中TMB升高与生存期缩短显著相关。免疫特征分析显示，两组患者的免疫浸润水平存在显著差异，高风险组的肿瘤免疫功能异常与排斥（Tumor Immune Dysfunction and Exclusion, TIDE）评分更高，提示其可能存在免疫逃逸现象。
结论 综上，本研究基于TMGs构建的预后模型可通过风险评分有效区分HCC患者，能够实现可靠的预后预测，并可为HCC个体化治疗挖掘潜在治疗靶点。未来可进一步探索将该模型应用于临床实践，以改善HCC患者的预后。