成年人的中枢神经系统最常见的原发恶性肿瘤是胶质瘤，低级别胶质瘤（LGG）的生长缓慢。然而，大部分LGG病例都会进展为高级别胶质瘤，给预后预测带来挑战。肿瘤微环境（TME）通过端粒相关基因和免疫细胞浸润的特征，强烈影响胶质瘤生长和治疗反应。因此，我们的目标是开发一个融合端粒相关基因和免疫细胞环境的端粒-TME（TM-TME）分类器，用于评估胶质瘤的预后和治疗反应。本研究涵盖了TCGA和CCGA数据库中的LGG患者。TM评分和TME评分分别基于LGG中端粒相关基因的表达特征和免疫细胞的存在。通过将TM和TME评分结合起来建立TM-TME分类器，以有效预测预后。随后，我们进行了Kaplan-Meier生存估计、单因素Cox回归分析和接收器操作特性曲线，以验证TM-TME分类器在多个队列中的预后预测能力。进行了基因本体论（GO）分析、生物过程和蛋白质图谱分析，以注释每个亚群的功能方面，并可视化细胞信号通路。TM_low+TME_high亚群与其他亚群相比具有优越的预后和治疗反应（P<0.001）。这一发现可能归因于不同的肿瘤体细胞突变和癌症细胞信号通路。GO分析表明，TM_low+TME_high亚群与神经系统和化学突触传递调节相关。相反，TM_high+TME_low亚群与细胞周期和DNA代谢过程密切相关。此外，该分类器在TCGA LGG队列中显著区分总生存，并且在TCGA队列（P<0.001）和CCGA队列（P<0.001）中作为LGG患者的独立预后因子。总体而言，我们的发现强调了TM-TME分类器在胶质瘤预后和免疫治疗反应预测中的重要性，为每个亚群内复杂的免疫环境提供了启示。此外，我们的结果表明将风险分层与精确治疗相结合在LGG中具有潜力。版权所有 © 2023 韩、延、范、关、胡、李、欧、崔、安、张和龚。

Gliomas, the most prevalent primary malignant tumors of the central nervous system in adults, exhibit slow growth in lower-grade gliomas (LGG). However, the majority of LGG cases progress to high-grade gliomas, posing challenges for prognostication. The tumor microenvironment (TME), characterized by telomere-related genes and immune cell infiltration, strongly influences glioma growth and therapeutic response. Therefore, our objective was to develop a Telomere-TME (TM-TME) classifier that integrates telomere-related genes and immune cell landscape to assess prognosis and therapeutic response in glioma.This study encompassed LGG patients from the TCGA and CCGA databases. TM score and TME score were derived from the expression signatures of telomere-related genes and the presence of immune cells in LGG, respectively. The TM-TME classifier was established by combining TM and TME scores to effectively predict prognosis. Subsequently, we conducted Kaplan-Meier survival estimation, univariate Cox regression analysis, and receiver operating characteristic curves to validate the prognostic prediction capacity of the TM-TME classifier across multiple cohorts. Gene Ontology (GO) analysis, biological processes, and proteomaps were performed to annotate the functional aspects of each subgroup and visualize the cellular signaling pathways.The TM_low+TME_high subgroup exhibited superior prognosis and therapeutic response compared to other subgroups (P<0.001). This finding could be attributed to distinct tumor somatic mutations and cancer cellular signaling pathways. GO analysis indicated that the TM_low+TME_high subgroup is associated with the neuronal system and modulation of chemical synaptic transmission. Conversely, the TM_high+TME_low subgroup showed a strong association with cell cycle and DNA metabolic processes. Furthermore, the classifier significantly differentiated overall survival in the TCGA LGG cohort and served as an independent prognostic factor for LGG patients in both the TCGA cohort (P<0.001) and the CGGA cohort (P<0.001).Overall, our findings underscore the significance of the TM-TME classifier in predicting prognosis and immune therapeutic response in glioma, shedding light on the complex immune landscape within each subgroup. Additionally, our results suggest the potential of integrating risk stratification with precision therapy for LGG.Copyright © 2023 Han, Yan, Fan, Guan, Hu, Li, Ou, Cui, An, Zhang and Gong.