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

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. 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.

Authors:
 [1];  [2];  [3];  [1];  [3]
  1. Drum Tower Clinical Medical School Of Nanjing Medical University (China). Department of Gastroenterology
  2. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Biological Systems and Engineering Division; Shandong University School of Ocean, Weihai (China). International Biotechnology R&D Center
  3. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Biological Systems and Engineering Division
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1378727
Grant/Contract Number:
AC02-05CH11231
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Oncotarget
Additional Journal Information:
Journal Volume: 7; Journal Issue: 34; Journal ID: ISSN 1949-2553
Publisher:
Impact Journals
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; 60 APPLIED LIFE SCIENCES; gene biomarkers; prognostic score; gastric cancer

Citation Formats

Wang, Pin, Wang, Yunshan, Hang, Bo, Zou, Xiaoping, and Mao, Jian-Hua. A novel gene expression-based prognostic scoring system to predict survival in gastric cancer. United States: N. p., 2016. Web. doi:10.18632/oncotarget.10533.
Wang, Pin, Wang, Yunshan, Hang, Bo, Zou, Xiaoping, & Mao, Jian-Hua. A novel gene expression-based prognostic scoring system to predict survival in gastric cancer. United States. doi:10.18632/oncotarget.10533.
Wang, Pin, Wang, Yunshan, Hang, Bo, Zou, Xiaoping, and Mao, Jian-Hua. 2016. "A novel gene expression-based prognostic scoring system to predict survival in gastric cancer". United States. doi:10.18632/oncotarget.10533. https://www.osti.gov/servlets/purl/1378727.
@article{osti_1378727,
title = {A novel gene expression-based prognostic scoring system to predict survival in gastric cancer},
author = {Wang, Pin and Wang, Yunshan and Hang, Bo and Zou, Xiaoping and Mao, Jian-Hua},
abstractNote = {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.},
doi = {10.18632/oncotarget.10533},
journal = {Oncotarget},
number = 34,
volume = 7,
place = {United States},
year = 2016,
month = 7
}

Journal Article:
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