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. 2016 Aug 23;7(34):55343-55351.
doi: 10.18632/oncotarget.10533.

V体育平台登录 - A novel gene expression-based prognostic scoring system to predict survival in gastric cancer

Pin Wang  1 , Yunshan Wang  2   3 , Bo Hang  2 , Xiaoping Zou  1 , Jian-Hua Mao  2
Affiliations

A novel gene expression-based prognostic scoring system to predict survival in gastric cancer

"V体育官网" Pin Wang et al. Oncotarget. .

Abstract

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 VSports手机版. 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. .

Keywords: gastric cancer; gene biomarkers; prognostic score V体育安卓版. .

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Conflict of interest statement

CONFLICTS OF INTEREST

The authors declare that they have no competing interests.

Figures

Figure 1
Figure 1. Schematic diagram for a multi-step strategy to identify gene signature for prognosis in gastric cancer
The results for each step have been summarized.
Figure 2
Figure 2. Kaplan-Meier survival curves for gastric cancer patients according to tumor expression of top rank 6 genes are presented
A. NOTCH3. B. SPRY4. C. TMEM63A. D. R3HDM1. E. UBAP2L. F. GABBR1. The p values were obtained from a log-rank test between two groups.
Figure 3
Figure 3. Functional annotation analysis of 249 genes using the Database for Annotation, Visualization and Integrated Discovery (DAVID)
A. DAVID analysis reveals the potential signaling pathways that are enriched among 249 genes. B. DAVID analysis reveals upstream transcriptional factors that regulate the expression of 249 genes.
Figure 4
Figure 4. Gene correlation networks of the 249 genes
Functional annotations have been indicated for different subnetworks.
Figure 5
Figure 5. Development of a prognostic scoring system for gastric cancer patients
A. Schematic diagram for a multi-step strategy to develop a prognostic scoring system for gastric cancer patients using the TCGA data. B. Distribution of prognostic score between patients with good and bad prognosis. C-D. Prognostic scores are significantly associated with overall survival of gastric cancer patients in TCGA GC data (C) and GSE15459 (D). Kaplan-Meier survival curves for gastric cancer patients according to prognostic scores. The p values were obtained from a log-rank test between two groups.
See this image and copyright information in PMC

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