Identification and validation of a prognostic 9-genes expression signature for gastric cancer
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Zhiqiang Wang1,2,*, Gongxing Chen3,*, Qilong Wang1, Wei Lu4,5 and Meidong Xu1
1Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China
2Department of General Surgery, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
3The Laboratory Center of Medical School of Hangzhou Normal University, Hangzhou, China
4Department of General Surgery, Xinhua Hospital, Shanghai JiaoTong University, School of Medicine, Shanghai, China
5Institute of Biliary Tract Diseases, Shanghai JiaoTong University School of Medicine, Shanghai, China
*These authors have contributed equally to this work
Meidong Xu, email: email@example.com
Wei Lu, email: firstname.lastname@example.org
Keywords: gastric cancer, prognostic model, survival analysis, clustering analysis
Received: November 26, 2016 Accepted: April 11, 2017 Published: May 10, 2017
Gastric cancer (GC) is a common malignant tumor with high incidence and mortality. Reasonable assessment of prognosis is essential to improve the outcomes of patients. In this study, we constructed and validated a prognostic gene model to evaluate the risks of GC patients. To identify the differentially expressed genes between GC patients and controls, we extracted Gene expression profiles of GC patients (N=432) from Gene Expression Omnibus database and then stable signature genes by using Robust likelihood-based modeling with 1000 iterations. Unsupervised hierarchical clustering of all samples was performed basing on the characteristics of gene expressions. Meanwhile, the differences between the clusters were analyzed by Kaplan Meier survival analysis. A 9-genes model was obtained (frequency = 999; p=1.333628e-18), including two negative impact factors (NR1I2 and LGALSL) and 7 positive ones (C1ORF198, CST2, LAMP5, FOXS1, CES1P1, MMP7 and COL8A1). This model was verified in single factor survival analysis (p=0.004447558) and significant analysis with recurrence time (p=0.001474831) by using independent datasets from TCGA. The constructed 9-genes model was stable and effective, which might serve as prognostic signature to predict the survival of GC patients and monitor the long-term treatment of GC.
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