Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Beijing Institute of Brain Disorders, Laboratory of Brain Disorders, Ministry of Science and Technology, Collaborative Innovation Center for Brain Disorders, Capital Medical University, Beijing, China.
Front Immunol. 2023 Aug 17;14:1220100. doi: 10.3389/fimmu.2023.1220100. eCollection 2023.
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.
神经胶质瘤是成人中枢神经系统最常见的原发性恶性肿瘤，低级别神经胶质瘤（LGG）生长缓慢。然而，大多数 LGG 病例会进展为高级别神经胶质瘤，这对预后评估提出了挑战。肿瘤微环境（TME）以端粒相关基因和免疫细胞浸润为特征，强烈影响神经胶质瘤的生长和治疗反应。因此，我们的目标是开发一种端粒-TME（TM-TME）分类器，该分类器整合端粒相关基因和免疫细胞景观，以评估神经胶质瘤的预后和治疗反应。
本研究纳入了 TCGA 和 CCGA 数据库中的 LGG 患者。TM 评分和 TME 评分分别来自 LGG 中端粒相关基因的表达特征和免疫细胞的存在。通过结合 TM 和 TME 评分来建立 TM-TME 分类器，以有效地预测预后。随后，我们进行了 Kaplan-Meier 生存估计、单因素 Cox 回归分析和受试者工作特征曲线，以验证 TM-TME 分类器在多个队列中的预后预测能力。进行基因本体论（GO）分析、生物学过程和蛋白质图谱，以注释每个亚组的功能方面并可视化细胞信号通路。
TM_low+TME_high 亚组的预后和治疗反应优于其他亚组（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 亚组与神经元系统和化学突触传递的调节有关。相反，TM_high+TME_low 亚组与细胞周期和 DNA 代谢过程密切相关。此外，该分类器在 TCGA LGG 队列中显著区分了总生存期，并且是 TCGA 队列（P<0.001）和 CGGA 队列（P<0.001）中 LGG 患者的独立预后因素。
总之，我们的研究结果强调了 TM-TME 分类器在预测神经胶质瘤预后和免疫治疗反应中的重要性，揭示了每个亚组内复杂的免疫景观。此外，我们的结果表明，将风险分层与精准治疗相结合可能适用于 LGG。
