A novel gene expression-based prognostic scoring system to predict survival in gastric cancer
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Pin Wang1,*, Yunshan Wang2,3,*, Bo Hang2, Xiaoping Zou1, Jian-Hua Mao2
1Department of Gastroenterology, Drum Tower Clinical Medical School Of Nanjing Medical University, Nanjing, Jiangsu 210008, China
2Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
3International Biotechnology R&D Center, Shandong University School of Ocean, Weihai, Shandong 264209, China
*These authors have contributed equally to this work
Xiaoping Zou, email: [email protected]
Jian-Hua Mao, email: [email protected]
Keywords: gene biomarkers, prognostic score, gastric cancer
Received: March 20, 2016 Accepted: May 26, 2016 Published: July 11, 2016
Analysis of gene expression patterns in gastric cancer (GC) can help to identify a comprehensive panel of gene biomarkers for predicting clinical outcomes and to discover potential new therapeutic targets. Here, a multi-step bioinformatics analytic approach was developed to establish a novel prognostic scoring system for GC. We first identified 276 genes that were robustly differentially expressed between normal and GC tissues, of which, 249 were found to be significantly associated with overall survival (OS) by univariate Cox regression analysis. The biological functions of 249 genes are related to cell cycle, RNA/ncRNA process, acetylation and extracellular matrix organization. A network was generated for view of the gene expression architecture of 249 genes in 265 GCs. Finally, we applied a canonical discriminant analysis approach to identify a 53-gene signature and a prognostic scoring system was established based on a canonical discriminant function of 53 genes. The prognostic scores strongly predicted patients with GC to have either a poor or good OS. Our study raises the prospect that the practicality of GC patient prognosis can be assessed by this prognostic scoring system.
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