Predicting Diagnostic Gene Biomarkers for Non-Small-Cell Lung Cancer

被引:9
|
作者
Liang, Bin [1 ]
Shao, Yang [1 ]
Long, Fei [1 ]
Jiang, Shu-Juan [1 ]
机构
[1] Shandong Univ, Shandong Prov Hosp, Dept Resp Med, Jinan 250021, Shandong, Peoples R China
基金
中国国家自然科学基金;
关键词
EXPRESSION; NETWORK; GEMCITABINE; PROTEINS; DATABASE; PATHWAY; RISK;
D O I
10.1155/2016/3952494
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 ; 0836 ; 090102 ; 100705 ;
摘要
Lung cancer is the primary reason for death due to cancer worldwide, and non-small-cell lung cancer (NSCLC) is the most common subtype of lung cancer. Most patients die from complications of NSCLC due to poor diagnosis. In this paper, we aimed to predict gene biomarkers that may be of use for diagnosis of NSCLC by integrating differential gene expression analysis with functional association network analysis. We first constructed an NSCLC-specific functional association network by combining gene expression correlation with functional association. Then, we applied a network partition algorithm to divide the network into gene modules and identify the most NSCLC-specific gene modules based on their differential expression pattern in between normal and NSCLC samples. Finally, from these modules, we identified genes that exhibited the most impact on the expression of their functionally associated genes in between normal and NSCLC samples and predicted them as NSCLC biomarkers. Literature review of the top predicted gene biomarkers suggested that most of them were already considered critical for development of NSCLC.
引用
收藏
页数:8
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