DataSheet1_A Novel Six Autophagy-Related Genes Signature Associated With Outcomes and Immune Microenvironment in Lower-Grade Glioma.pdf

Since autophagy and the immune microenvironment are deeply involved in the tumor development and progression of Lower-grade gliomas (LGG), our study aimed to construct an autophagy-related risk model for prognosis prediction and investigate the relationship between the immune microenvironment and risk signature in LGG. Therefore, we identified six autophagy-related genes (BAG1, PTK6, EEF2, PEA15, ITGA6, and MAP1LC3C) to build in the training cohort (n = 305 patients) and verify the prognostic model in the validation cohort (n = 128) and the whole cohort (n = 433), based on the data from The Cancer Genome Atlas (TCGA). The six-gene risk signature could divide LGG patients into high- and low-risk groups with distinct overall survival in multiple cohorts (all p < 0.001). The prognostic effect was assessed by area under the time-dependent ROC (t-ROC) analysis in the training, validation, and whole cohorts, in which the AUC value at the survival time of 5 years was 0.837, 0.755, and 0.803, respectively. Cox regression analysis demonstrated that the risk model was an independent risk predictor of OS (HR > 1, p < 0.05). A nomogram including the traditional clinical parameters and risk signature was constructed, and t-ROC, C-index, and calibration curves confirmed its robust predictive capacity. KM analysis revealed a significant difference in the subgroup analyses’ survival. Functional enrichment analysis revealed that these autophagy-related signatures were mainly involved in the phagosome and immune-related pathways. Besides, we also found significant differences in immune cell infiltration and immunotherapy targets between risk groups. In conclusion, we built a powerful predictive signature and explored immune components (including immune cells and emerging immunotherapy targets) in LGG.

鉴于自噬和免疫微环境在低级别胶质瘤（LGG）的发展与进展中扮演着至关重要的角色，本研究旨在构建一个与自噬相关的风险模型以预测预后，并探讨免疫微环境与LGG风险标志物之间的关系。因此，我们识别出六个与自噬相关的基因（BAG1、PTK6、EEF2、PEA15、ITGA6和MAP1LC3C），并在训练队列（n = 305例病人）中构建，并在验证队列（n = 128例）和整个队列（n = 433例）中验证预后模型，数据来源于癌症基因组图谱（TCGA）。该六基因风险标志物能够将LGG患者划分为高风险组和低风险组，并在多个队列中显示出不同的总生存率（所有p < 0.001）。通过时间依赖性ROC曲线下面积（t-ROC）分析，在训练、验证和整个队列中评估了预后效果，其中5年生存时间的AUC值分别为0.837、0.755和0.803。Cox回归分析表明，该风险模型是独立的风险预测因子（HR > 1，p < 0.05）。构建了一个包括传统临床参数和风险标志物的评分图，t-ROC、C指数和校准曲线证实了其强大的预测能力。KM分析揭示了亚组分析中生存率的显著差异。功能富集分析显示，这些与自噬相关的标志物主要涉及吞噬体和免疫相关途径。此外，我们还发现风险组在免疫细胞浸润和免疫治疗靶点之间存在显著差异。总之，我们构建了一个强大的预测标志物，并探索了LGG中的免疫成分（包括免疫细胞和新兴的免疫治疗靶点）。