Chen Zhuohui, Wu Tong, Yan Zhouyi, Zhang Mengqi
Department of Neurology, Xiangya Hospital, Central South University, Changsha, China.
Front Cell Dev Biol. 2021 Jun 23;9:652599. doi: 10.3389/fcell.2021.652599. eCollection 2021.
Glioma is the most common primary malignant brain tumor with significant mortality and morbidity. Ferroptosis, a novel form of programmed cell death (PCD), is critically involved in tumorigenesis, progression and metastatic processes.
We revealed the relationship between ferroptosis-related genes and glioma by analyzing the mRNA expression profiles from The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), GSE16011, and the Repository of Molecular Brain Neoplasia Data (REMBRANDT) datasets. The least absolute shrinkage and selection operator (LASSO) Cox regression analysis was performed to construct a ferroptosis-associated gene signature in the TCGA cohort. Glioma patients from the CGGA, GSE16011, and REMBRANDT cohorts were used to validate the efficacy of the signature. Receiver operating characteristic (ROC) curve analysis was applied to measure the predictive performance of the risk score for overall survival (OS). Univariate and multivariate Cox regression analyses of the 11-gene signature were performed to determine whether the ability of the prognostic signature in predicting OS was independent. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted to identify the potential biological functions and pathways of the signature. Subsequently, we performed single sample gene set enrichment analysis (ssGSEA) to explore the correlation between risk scores and immune status. Finally, seven putative small molecule drugs were predicted by Connectivity Map.
The 11-gene signature was identified to divide patients into two risk groups. ROC curve analysis indicated the 11-gene signature as a potential diagnostic factor in glioma patients. Multivariate Cox regression analyses showed that the risk score was an independent predictive factor for overall survival. Functional analysis revealed that genes were enriched in iron-related molecular functions and immune-related biological processes. The results of ssGSEA indicated that the 11-gene signature was correlated with the initiation and progression of glioma. The small molecule drugs we selected showed significant potential to be used as putative drugs.
we identified a novel ferroptosis-related gene signature for prognostic prediction in glioma patients and revealed the relationship between ferroptosis-related genes and immune checkpoint molecules.
胶质瘤是最常见的原发性恶性脑肿瘤,具有显著的死亡率和发病率。铁死亡是一种新型的程序性细胞死亡(PCD)形式,在肿瘤发生、进展和转移过程中起关键作用。
我们通过分析来自癌症基因组图谱(TCGA)、中国胶质瘤基因组图谱(CGGA)、GSE16011和分子脑肿瘤数据存储库(REMBRANDT)数据集的mRNA表达谱,揭示了铁死亡相关基因与胶质瘤之间的关系。采用最小绝对收缩和选择算子(LASSO)Cox回归分析在TCGA队列中构建铁死亡相关基因特征。来自CGGA、GSE16011和REMBRANDT队列的胶质瘤患者用于验证该特征的有效性。应用受试者工作特征(ROC)曲线分析来衡量风险评分对总生存期(OS)的预测性能。对11基因特征进行单变量和多变量Cox回归分析,以确定预后特征预测OS的能力是否独立。进行基因本体(GO)分析和京都基因与基因组百科全书(KEGG)通路分析,以确定该特征的潜在生物学功能和通路。随后,我们进行了单样本基因集富集分析(ssGSEA),以探索风险评分与免疫状态之间的相关性。最后,通过连通性图谱预测了七种推定的小分子药物。
鉴定出的11基因特征可将患者分为两个风险组。ROC曲线分析表明,11基因特征是胶质瘤患者的潜在诊断因素。多变量Cox回归分析表明,风险评分是总生存期的独立预测因素。功能分析显示,基因富集于铁相关分子功能和免疫相关生物学过程。ssGSEA结果表明,11基因特征与胶质瘤的发生和进展相关。我们选择的小分子药物显示出作为推定药物的显著潜力。
我们鉴定出一种用于胶质瘤患者预后预测的新型铁死亡相关基因特征,并揭示了铁死亡相关基因与免疫检查点分子之间的关系。