预测低级别胶质瘤预后的免疫相关标志物。

BACKGROUND: Lower-grade gliomas (LGGs) have more favorable outcomes than glioblastomas; however, LGGs often progress to process glioblastomas within a few years. Numerous studies have proven that the tumor microenvironment (TME) is correlated with the prognosis of glioma. METHODS: LGG RNA-Sequencing (RNA-seq) data from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) were extracted and then divided into training and testing cohorts, respectively. Immune-related differentially expressed genes (DEGs) were screened to establish a prognostic signature by a multivariate Cox proportional hazards regression model. The immune-related risk score and clinical information, such as age, sex, World Health Organization (WHO) grade, and isocitrate dehydrogenase 1 (IDH1) mutation, were used to independently validate and develop a prognostic nomogram. GO and KEGG pathway analyses to DEGs between immune-related high-risk and low-risk groups were performed. RESULTS: Sixteen immune-related genes were screened for establishing a prognostic signature. The risk score had a negative correlation with prognosis, with an area under the receiver operating characteristic (ROC) curve of 0.941. The risk score, age, grade, and IDH1 mutation were identified as independent prognostic factors in patients with LGGs. The hazard ratios (HRs) of the high-risk score were 5.247 [95% confidence interval (CI) = 3.060-8.996] in the multivariate analysis. A prognostic nomogram of 1-, 3-, and 5-year survival was established and validated internally and externally. Go and KEGG pathway analyses implied that immune-related biological function and pathways were involved in the TME. CONCLUSION: The immune-related prognostic signature and the prognostic nomogram could accurately predict survival.

背景：低级别胶质瘤（LGG）的预后优于胶质母细胞瘤，但 LGG 常在数年内进展为胶质母细胞瘤。大量研究已经证实，肿瘤微环境（TME）与胶质瘤的预后相关。

方法：从癌症基因组图谱（TCGA）和中国脑胶质瘤基因组图谱（CGGA）中提取 LGG RNA 测序（RNA-seq）数据，并分别将其分为训练和测试队列。通过多变量 Cox 比例风险回归模型筛选免疫相关差异表达基因（DEG），建立预后标志。使用免疫相关风险评分和临床信息（如年龄、性别、世界卫生组织（WHO）分级和异柠檬酸脱氢酶 1（IDH1）突变），对风险评分进行独立验证和建立预后列线图。对免疫相关高低风险组间 DEGs 进行 GO 和 KEGG 通路分析。

结果：筛选出 16 个免疫相关基因建立预后标志。风险评分与预后呈负相关，受试者工作特征（ROC）曲线下面积为 0.941。风险评分、年龄、分级和 IDH1 突变被确定为 LGG 患者的独立预后因素。在多因素分析中，高风险评分的危险比（HR）为 5.247（95%置信区间[CI]：3.060-8.996）。建立并验证了 1 年、3 年和 5 年的生存预后列线图。GO 和 KEGG 通路分析表明，免疫相关的生物学功能和途径涉及肿瘤微环境。

结论：免疫相关预后标志和预后列线图可准确预测生存。