Prognostic Value of a Nine-Gene Signature in Glioma Patients Based on mRNA Expression Profiling
Prognostic Value of a Nine-Gene Signature in Glioma Patients Based on mRNA Expression Profiling
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基于 mRNA 表达谱的神经胶质瘤患者九基因特征的预后价值
DOI：
10.1111/cns.12171
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发表时间：
2014-02-01
影响因子：
5.5
通讯作者：
Jiang, Tao
中科院分区：
文献类型：
--
作者：
Bao, Zhao-Shi;Li, Ming-Yang;Jiang, Tao
简介胶质瘤是成人最常见的原发性脑肿瘤，也是癌症相关死亡的重要原因。A9-gene signature被鉴定为一种能明显反映胶质瘤生存状况的新型预后模型AimsTo identify an mRNA expression signature to improve outcome prediction for patients with different glioma graduates.ResultsWe used whole genome mRNA expression microarray data of 220 glioma samples of all grades from the Chinese Glioma Genome Atlas（CGGA）database（http：www.cgga.org.cn）as a discovery set and data from伦勃朗and GSE 16011 for validation sets.采用Kaplan-Meier方法和双侧对数秩检验对每个年级的数据进行分析。应用单变量考克斯回归和线性风险评分公式推导出具有更好预后性能的基因签名。我们发现，与低风险评分的患者相比，根据签名具有高风险评分的患者的总体生存率较差。采用基因本体论（GO）和基因集变异分析（GSVA）对高危人群中的高表达基因进行分析。其结果是，胶质瘤的可分性的原因可能是由于细胞的生命过程和adhesion.ConclusionThis 9-gene-signature预测模型提供了一个更准确的预测预后，表示高风险评分的患者预后不良。此外，这些风险模型的基础上定义的分子概况显示了相当大的前景的个性化癌症管理。
IntroductionGliomas are the most common primary brain tumors in adults and a significant cause of cancer-related mortality. A 9-gene signature was identified as a novel prognostic model reflecting survival situation obviously in gliomas.AimsTo identify an mRNA expression signature to improve outcome prediction for patients with different glioma grades.ResultsWe used whole-genome mRNA expression microarray data of 220 glioma samples of all grades from the Chinese Glioma Genome Atlas (CGGA) database (http://www.cgga.org.cn) as a discovery set and data from Rembrandt and GSE16011 for validation sets. Data from every single grade were analyzed by the Kaplan-Meier method with a two-sided log-rank test. Univariate Cox regression and linear risk score formula were applied to derive a gene signature with better prognostic performance. We found that patients who had high risk score according to the signature had poor overall survival compared with patients who had low risk score. Highly expressed genes in the high-risk group were analyzed by gene ontology (GO) and gene set variation analysis (GSVA). As a result, the reason for the divisibility of gliomas was likely due to cell life processes and adhesion.ConclusionThis 9-gene-signature prediction model provided a more accurate predictor of prognosis that denoted patients with high risk score have poor outcome. Moreover, these risk models based on defined molecular profiles showed the considerable prospect in personalized cancer management.