神经胶质瘤是最常见的原发性脑肿瘤，患者通常表现出较差的预后。越来越多的证据表明端粒维持机制在神经胶质瘤的发展中发挥着至关重要的作用。然而，端粒相关基因在神经胶质瘤中的预后价值仍不确定。本研究旨在构建端粒相关基因的预后模型，并进一步阐明两者之间的潜在关联。我们获取了低级别胶质瘤（LGG）和胶质母细胞瘤（GBM）的RNA-seq数据，以及相应的临床信息。癌症基因组图谱（TCGA）数据库和基因型组织表达（GTEX）数据库中的正常脑组织数据进行差异分析。端粒相关基因从 TelNet 获得。最初，我们对TCGA和GTEX数据进行差异分析，以确定差异表达的端粒相关基因，随后对这些基因进行基因本体论（GO）和京都基因与基因组百科全书（KEGG）富集分析。随后，采用单变量Cox分析和对数秩检验来获得与预后相关的基因。依次利用最小绝对收缩和选择算子（LASSO）回归分析和多元Cox回归分析构建预后模型。使用受试者工作特征（ROC）曲线分析证明了该模型的稳健性，并计算了临床特征和预后模型的风险评分的多变量Cox回归以评估独立的预后因素。上述结果使用中国胶质瘤基因组图谱（CGGA）数据集进行了验证。最后，利用CIBERSORT算法分析高危组和低危组之间免疫细胞浸润水平的差异，并在人类蛋白质图谱（HPA）数据库中验证候选基因。差异分析得出496个差异表达的端粒相关基因。 GO和KEGG通路分析表明这些基因主要参与端粒相关的生物过程和通路。随后，通过单变量Cox回归分析、log-rank检验、LASSO回归分析和多变量Cox回归分析，构建了包含10个端粒相关基因的预后模型。根据风险评分将患者分为高风险组和低风险组。 Kaplan-Meier (K-M) 生存分析显示，与低风险组相比，高风险组的预后更差，并确定该预后模型是神经胶质瘤患者的重要独立预后因素。最后进行免疫浸润分析，发现高危人群和低危人群多重免疫细胞浸润比例存在显着差异，并在HPA数据库中验证了8个候选基因。本研究成功构建了端粒相关的预后模型基因可以更准确地预测胶质瘤患者的预后，为胶质瘤的治疗提供潜在靶点和理论基础，并通过免疫浸润分析为免疫治疗提供参考。2024转化癌症研究。版权所有。

Gliomas are the most prevalent primary brain tumors, and patients typically exhibit poor prognoses. Increasing evidence suggests that telomere maintenance mechanisms play a crucial role in glioma development. However, the prognostic value of telomere-related genes in glioma remains uncertain. This study aimed to construct a prognostic model of telomere-related genes and further elucidate the potential association between the two.We acquired RNA-seq data for low-grade glioma (LGG) and glioblastoma (GBM), along with corresponding clinical information from The Cancer Genome Atlas (TCGA) database, and normal brain tissue data from the Genotype-Tissue Expression (GTEX) database for differential analysis. Telomere-related genes were obtained from TelNet. Initially, we conducted a differential analysis on TCGA and GTEX data to identify differentially expressed telomere-related genes, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses on these genes. Subsequently, univariate Cox analysis and log-rank tests were employed to obtain prognosis-related genes. Least absolute shrinkage and selection operator (LASSO) regression analysis and multivariate Cox regression analysis were sequentially utilized to construct prognostic models. The model's robustness was demonstrated using receiver operating characteristic (ROC) curve analysis, and multivariate Cox regression of risk scores for clinical characteristics and prognostic models were calculated to assess independent prognostic factors. The aforementioned results were validated using the Chinese Glioma Genome Atlas (CGGA) dataset. Finally, the CIBERSORT algorithm analyzed differences in immune cell infiltration levels between high- and low-risk groups, and candidate genes were validated in the Human Protein Atlas (HPA) database.Differential analysis yielded 496 differentially expressed telomere-related genes. GO and KEGG pathway analyses indicated that these genes were primarily involved in telomere-related biological processes and pathways. Subsequently, a prognostic model comprising ten telomere-related genes was constructed through univariate Cox regression analysis, log-rank test, LASSO regression analysis, and multivariate Cox regression analysis. Patients were stratified into high-risk and low-risk groups based on risk scores. Kaplan-Meier (K-M) survival analysis revealed worse outcomes in the high-risk group compared to the low-risk group, and establishing that this prognostic model was a significant independent prognostic factor for glioma patients. Lastly, immune infiltration analysis was conducted, uncovering notable differences in the proportion of multiple immune cell infiltrations between high- and low-risk groups, and eight candidate genes were verified in the HPA database.This study successfully constructed a prognostic model of telomere-related genes, which can more accurately predict glioma patient prognosis, offer potential targets and a theoretical basis for glioma treatment, and serve as a reference for immunotherapy through immune infiltration analysis.2024 Translational Cancer Research. All rights reserved.