Liu Zhentao, Zhang Hao, Hu Hongkang, Cai Zheng, Lu Chengyin, Liang Qiang, Qian Jun, Wang Chunhui, Jiang Lei
Department of Neurosurgery, Changzheng Hospital, Naval Medical University, Shanghai, China.
Department of Neurosurgery, No. 988 Hospital of Joint Logistic Support Force, Zhengzhou, China.
Front Genet. 2021 Mar 11;12:634116. doi: 10.3389/fgene.2021.634116. eCollection 2021.
Glioblastoma multiforme (GBM) is a devastating brain tumor and displays divergent clinical outcomes due to its high degree of heterogeneity. Reliable prognostic biomarkers are urgently needed for improving risk stratification and survival prediction. In this study, we analyzed genome-wide mRNA profiles in GBM patients derived from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to identify mRNA-based signatures for GBM prognosis with survival analysis. Univariate Cox regression model was used to evaluate the relationship between the expression of mRNA and the prognosis of patients with GBM. We established a risk score model that consisted of six mRNA (AACS, STEAP1, STEAP2, G6PC3, FKBP9, and LOXL1) by the LASSO regression method. The six-mRNA signature could divide patients into a high-risk and a low-risk group with significantly different survival rates in training and test sets. Multivariate Cox regression analysis confirmed that it was an independent prognostic factor in GBM patients, and it has a superior predictive power as compared with age, IDH mutation status, MGMT, and G-CIMP methylation status. By combining this signature and clinical risk factors, a nomogram can be established to predict 1-, 2-, and 3-year OS in GBM patients with relatively high accuracy.
多形性胶质母细胞瘤（GBM）是一种极具破坏性的脑肿瘤，因其高度异质性而表现出不同的临床结果。迫切需要可靠的预后生物标志物来改善风险分层和生存预测。在本研究中，我们分析了来自癌症基因组图谱（TCGA）和基因表达综合数据库（GEO）的GBM患者的全基因组mRNA谱，通过生存分析确定基于mRNA的GBM预后特征。使用单变量Cox回归模型评估mRNA表达与GBM患者预后之间的关系。我们通过LASSO回归方法建立了一个由六个mRNA（AACS、STEAP1、STEAP2、G6PC3、FKBP9和LOXL1）组成的风险评分模型。这六个mRNA特征可将患者分为高风险组和低风险组，在训练集和测试集中生存率有显著差异。多变量Cox回归分析证实，它是GBM患者的独立预后因素，与年龄、异柠檬酸脱氢酶（IDH）突变状态、O6-甲基鸟嘌呤-DNA甲基转移酶（MGMT）和胶质母细胞瘤相关甲基化表型（G-CIMP）甲基化状态相比，具有更高的预测能力。通过结合这一特征和临床风险因素，可以建立一个列线图，以相对较高的准确性预测GBM患者1年、2年和3年的总生存期（OS）。
