神经胶质瘤是一种常见的颅内肿瘤，通常预后不良。最近，大量研究表明 5-甲基胞嘧啶 (m5C) RNA 修饰对肿瘤发生的重要性。然而，m5C 在神经胶质瘤中的预后价值和免疫相关性仍不清楚。我们从癌症基因组图谱 (TCGA) 和中国胶质瘤基因组图谱 (CGGA) 数据集中获取了 RNA 表达和临床信息进行分析。使用非负矩阵分解（NMF）将患者分为两个亚组，并比较这些患者的生存率和临床病理特征。采用 CIBERSORT 和单样本基因集算法 (ssGSEA) 方法研究 m5C 与免疫环境之间的关系。使用加权相关网络分析 (WGCNA) 和单变量 Cox 比例风险模型 (CoxPH) 构建 m5C 相关特征。大多数 m5C RNA 甲基化调节因子呈现差异表达和预后价值。免疫浸润细胞与m5C调节因子尤其是NSUN7之间存在明显的关系。在 WGCNA 的 m5C 相关模块中，我们发现 SEPT3、CHI3L1、PLBD1、PHYHIPL、SAMD8、RAP1B、B3GNT5、RER1、PTPN7、SLC39A1 和 MXI1 是神经胶质瘤的预后因素，它们被用来构建签名。 m5C 相关特征在预测神经胶质瘤患者生存方面的重要意义在验证集和 CGGA 队列中得到了证实。

Glioma is a common intracranial tumor and is generally associated with poor prognosis. Recently, numerous studies illustrated the importance of 5-methylcytosine (m5C) RNA modification to tumorigenesis. However, the prognostic value and immune correlation of m5C in glioma remain unclear. We obtained RNA expression and clinical information from The Cancer Genome Atlas (TCGA) and The Chinese Glioma Genome Atlas (CGGA) datasets to analyze. Nonnegative matrix factorization (NMF) was used to classify patients into two subgroups and compare these patients in survival and clinicopathological characteristics. CIBERSORT and single-sample gene-set algorithm (ssGSEA) methods were used to investigate the relationship between m5C and the immune environment. The Weighted correlation network analysis (WGCNA) and univariate Cox proportional hazard model (CoxPH) were used to construct a m5C-related signature. Most of m5C RNA methylation regulators presented differential expression and prognostic values. There were obvious relationships between immune infiltration cells and m5C regulators, especially NSUN7. In the m5C-related module from WGCNA, we found SEPT3, CHI3L1, PLBD1, PHYHIPL, SAMD8, RAP1B, B3GNT5, RER1, PTPN7, SLC39A1, and MXI1 were prognostic factors for glioma, and they were used to construct the signature. The great significance of m5C-related signature in predicting the survival of patients with glioma was confirmed in the validation sets and CGGA cohort.