Zhou Liwei, Jiang Zhengye, Shi Zhongjie, Zhao Wenpeng, Lu Zhenwei, Xie Yuanyuan, Zhang Bingchang, Lu Hanwen, Tan Guowei, Wang Zhanxiang
Department of Neurosurgery, Xiamen Key Laboratory of Brain Center, The First Affiliated Hospital of Xiamen University, Xiamen, China.
The Department of Neuroscience, Institute of Neurosurgery, School of Medicine, Xiamen University, Xiamen, China.
Front Cell Dev Biol. 2021 Nov 15;9:739097. doi: 10.3389/fcell.2021.739097. eCollection 2021.
Ferroptosis plays an important role in glioma and significantly affects the prognosis, but the specific mechanism has not yet been elucidated. Recent studies suggest that autophagy regulates the process of ferroptosis. This study aimed to find potential autophagy-ferroptosis genes and explore the prognostic significance in glioma. Ferroptosis and autophagy genes were obtained from two online databases (zhounan.org/ferrdb and autophagy.lu/). The RNAseq data and clinical information were obtained from the Chinese Glioma Genome Atlas (CGGA) database (http://www.cgga.org.cn/). Univariate, multivariate, lasso and Cox regression analysis screened out prognosis-related genes, and a risk model was constructed. Receiver operating characteristic (ROC) curve analysis evaluated the predictive efficiency of the model. Finally, a nomogram was constructed to more accurately predict the prognosis of glioma. We developed a Venn diagram showing 23 autophagy-ferroptosis genes. A total of 660 cases (including RNA sequences and complete clinical information) from two different cohorts (training group = 413, verification group = 247) of the CGGA database was acquired. Cohorts were screened to include five prognosis-related genes (, , , , ). Kaplan-Meier curves showed that the risk model was a good prognostic indicator ( < 0.001). ROC analysis showed good efficacy of the risk model. Multivariate Cox analysis also revealed that the risk model was suitable for clinical factors related to prognosis, including type of disease (primary, recurrence), grade (III-IV), age, temozolomide treatment, and 1p19q state. Using the five prognosis-related genes and the risk score, we constructed a nomogram assessed by C-index (0.7205) and a calibration plot that could more accurately predict glioma prognosis. Using a current database of autophagy and ferroptosis genes, we confirmed the prognostic significance of autophagy-ferroptosis genes in glioma, and we constructed a prognostic model to help guide treatment for high grade glioma in the future.
铁死亡在胶质瘤中起重要作用并显著影响预后,但具体机制尚未阐明。近期研究表明自噬调节铁死亡过程。本研究旨在寻找潜在的自噬-铁死亡基因并探索其在胶质瘤中的预后意义。从两个在线数据库(zhounan.org/ferrdb和autophagy.lu/)获取铁死亡和自噬基因。RNA测序数据和临床信息从中国胶质瘤基因组图谱(CGGA)数据库(http://www.cgga.org.cn/)获取。单因素、多因素、套索和Cox回归分析筛选出预后相关基因,并构建风险模型。受试者工作特征(ROC)曲线分析评估模型的预测效率。最后,构建列线图以更准确地预测胶质瘤的预后。我们绘制了一个维恩图,显示23个自噬-铁死亡基因。从CGGA数据库的两个不同队列(训练组 = 413,验证组 = 247)中获取了总共660例(包括RNA序列和完整临床信息)。筛选队列以纳入五个预后相关基因(,,,,)。Kaplan-Meier曲线表明风险模型是一个良好的预后指标(<0.001)。ROC分析表明风险模型具有良好的效能。多因素Cox分析还显示风险模型适用于与预后相关的临床因素,包括疾病类型(原发性、复发性)、分级(III-IV级)、年龄、替莫唑胺治疗和1p19q状态。使用这五个预后相关基因和风险评分,我们构建了一个通过C指数(0.7205)评估的列线图和一个校准图,其可以更准确地预测胶质瘤预后。利用当前的自噬和铁死亡基因数据库,我们证实了自噬-铁死亡基因在胶质瘤中的预后意义,并构建了一个预后模型以帮助指导未来高级别胶质瘤的治疗。