Prognostic Value of a Nine‐Gene Signature in Glioma Patients Based on mRNA Expression Profiling. (27th November 2013)

• Record Type: Journal Article
• Authors: Bao, Zhao‐Shi; Li, Ming‐Yang; Wang, Jia‐Yin; Zhang, Chuan‐Bao; Wang, Hong‐Jun; Yan, Wei; Liu, Yan‐Wei; Zhang, Wei; Chen, Ling; Jiang, Tao
• Is Part Of: CNS neuroscience & therapeutics. Volume 20:Number 2(2014)
• Page Start: 112
• Page End: 118
• Publication Date: 2013-11-27
• Subjects: Neuropharmacology -- Periodicals; Central nervous system -- Diseases -- Effect of drugs on -- Periodicals; 612.8
• DOI: 10.1111/cns.12171
• Languages: English
• ISSNs: 1755-5930

Abstract

<abstract abstract-type="main" id="cns12171-abs-0001"> <title>Summary</title> <sec id="cns12171-sec-0001" sec-type="section"> <title>Introduction</title> <p>Gliomas 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.</p> </sec> <sec id="cns12171-sec-0002" sec-type="section"> <title>Aims</title> <p>To identify an mRNA expression signature to improve outcome prediction for patients with different glioma grades.</p> </sec> <sec id="cns12171-sec-0003" sec-type="section"> <title>Results</title> <p>We 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.</p> </sec> <sec id="cns12171-sec-0004" sec-type="section"> <title>Conclusion</title> <p>This 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.</p> </sec> </abstract>