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Abstract：
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
摘要：
■胶质瘤是最常见的原发性脑肿瘤，患者通常表现出不良的预后。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起着至关重要的作用。然而,端粒相关基因在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，并进一步阐明二者之间的潜在关联。
■我们获得了低级别神经胶质瘤（LGG）和胶质母细胞瘤（GBM）的RNA-seq数据，以及来自癌症基因组图谱（TCGA）数据库的相应临床信息，和来自基因型-组织表达(GTEX)数据库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数据进行了差异分析，以鉴定差异表达的端粒相关基因，接下来是基因本体论（GO）和京都基因和基因组百科全书（KEGG）对这些基因的富集分析。随后，采用单变量Cox分析和对数秩检验获得预后相关基因。依次使用最小绝对收缩和选择算子（LASSO）回归分析和多变量Cox回归分析来构建预后模型。使用接收器工作特性(ROC)曲线分析证明了模型的鲁棒性，计算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。使用中国胶质瘤基因组图谱(CGGA)数据集验证上述结果。最后,CIBERSORT算法分析了高危组和低危组之间免疫细胞浸润水平的差异，和候选基因在人类蛋白质图谱（HPA）数据库中进行了验证。
■差异分析产生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学过程和通路。随后，通过单变量Cox回归分析构建了包含10个端粒相关基因的预后模型,对数秩检验，LASSO回归分析，和多变量Cox回归分析。根据风险评分将患者分为高风险和低风险组。Kaplan-Meier（K-M）生存分析显示，与低风险组相比，高风险组的预后较差。并建立该预后模型是胶质瘤患者的重要独立预后因素。最后,进行了免疫浸润分析，发现高危组和低危组之间多种免疫细胞浸润比例的显着差异，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基因的预后模型,可以更准确地预测胶质瘤患者的预后，为胶质瘤治疗提供潜在的靶点和理论基础，并通过免疫浸润分析为免疫治疗提供参考。
■我们获得了低级别神经胶质瘤（LGG）和胶质母细胞瘤（GBM）的RNA-seq数据，以及来自癌症基因组图谱（TCGA）数据库的相应临床信息，和来自基因型-组织表达(GTEX)数据库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数据进行了差异分析，以鉴定差异表达的端粒相关基因，接下来是基因本体论（GO）和京都基因和基因组百科全书（KEGG）对这些基因的富集分析。随后，采用单变量Cox分析和对数秩检验获得预后相关基因。依次使用最小绝对收缩和选择算子（LASSO）回归分析和多变量Cox回归分析来构建预后模型。使用接收器工作特性(ROC)曲线分析证明了模型的鲁棒性，计算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。使用中国胶质瘤基因组图谱(CGGA)数据集验证上述结果。最后,CIBERSORT算法分析了高危组和低危组之间免疫细胞浸润水平的差异，和候选基因在人类蛋白质图谱（HPA）数据库中进行了验证。
■差异分析产生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学过程和通路。随后，通过单变量Cox回归分析构建了包含10个端粒相关基因的预后模型,对数秩检验，LASSO回归分析，和多变量Cox回归分析。根据风险评分将患者分为高风险和低风险组。Kaplan-Meier（K-M）生存分析显示，与低风险组相比，高风险组的预后较差。并建立该预后模型是胶质瘤患者的重要独立预后因素。最后,进行了免疫浸润分析，发现高危组和低危组之间多种免疫细胞浸润比例的显着差异，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基因的预后模型,可以更准确地预测胶质瘤患者的预后，为胶质瘤治疗提供潜在的靶点和理论基础，并通过免疫浸润分析为免疫治疗提供参考。
