Ferroptosis-related gene signature correlates with the tumor immune features and predicts the prognosis of glioma patients.
Ferroptosis-related gene signature correlates with the tumor immune features and predicts the prognosis of glioma patients.
复制标题
铁死亡相关基因特征与肿瘤免疫特征相关并预测神经胶质瘤患者的预后
DOI：
10.1042/bsr20211640
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发表时间：
2021-12-22
影响因子：
4
通讯作者：
Zhu X
中科院分区：
文献类型：
--
作者：
Hu Y;Tu Z;Lei K;Huang K;Zhu X
摘要背景：脑胶质瘤是颅内恶性肿瘤，也是致死率最高的恶性肿瘤。神经胶质瘤的临床进展中的作用是不清楚的。方法：使用单变量和最小绝对收缩和选择算子（Lasso）考克斯回归方法，使用中国胶质瘤基因组图谱（CGGA）中的胶质瘤患者队列开发铁蛋白沉积相关特征（FRSig），并使用癌症基因组图谱（TCGA）中的胶质瘤患者独立队列进行验证。使用单样品基因集富集分析（ssGSEA）来计算免疫浸润的水平。采用多因素考克斯回归分析确定临床病理因素的独立预后作用，建立可供临床应用的诺模图模型。结果如下：我们分析了临床病理特征和铁凋亡相关基因（FRG）表达之间的相关性，并建立了FRSig来计算个体胶质瘤患者的风险评分。患者分为两个亚组，具有不同的临床结局。胶质瘤微环境中的免疫细胞浸润和免疫相关指标与FRSig显著相关，肿瘤突变负荷（TMB）、拷贝数改变（CNA）和免疫检查点表达也与FRSig评分显著正相关。最终，使用独立预后因素年龄、世界卫生组织（WHO）分级和FRSig评分构建基于FRSig的诺模图模型。结论：FRSig可用于胶质瘤患者的预后评估。FRSig也代表了胶质瘤微环境的状态。我们的FRSig将通过提供用于精确治疗的分子生物标志物签名，为改善患者管理和个体化治疗做出贡献。
Abstract Background: Glioma is a malignant intracranial tumor and the most fatal cancer. The role of ferroptosis in the clinical progression of gliomas is unclear. Method: Univariate and least absolute shrinkage and selection operator (Lasso) Cox regression methods were used to develop a ferroptosis-related signature (FRSig) using a cohort of glioma patients from the Chinese Glioma Genome Atlas (CGGA), and was validated using an independent cohort of glioma patients from The Cancer Genome Atlas (TCGA). A single-sample gene set enrichment analysis (ssGSEA) was used to calculate levels of the immune infiltration. Multivariate Cox regression was used to determine the independent prognostic role of clinicopathological factors and to establish a nomogram model for clinical application. Results: We analyzed the correlations between the clinicopathological features and ferroptosis-related gene (FRG) expression and established an FRSig to calculate the risk score for individual glioma patients. Patients were stratified into two subgroups with distinct clinical outcomes. Immune cell infiltration in the glioma microenvironment and immune-related indexes were identified that significantly correlated with the FRSig, the tumor mutation burden (TMB), copy number alteration (CNA), and immune checkpoint expression was also significantly positively correlated with the FRSig score. Ultimately, an FRSig-based nomogram model was constructed using the independent prognostic factors age, World Health Organization (WHO) grade, and FRSig score. Conclusion: We established the FRSig to assess the prognosis of glioma patients. The FRSig also represented the glioma microenvironment status. Our FRSig will contribute to improve patient management and individualized therapy by offering a molecular biomarker signature for precise treatment.