Table of Contents Author Guidelines Submit a Manuscript
BioMed Research International
Volume 2016, Article ID 2090286, 10 pages
http://dx.doi.org/10.1155/2016/2090286
Research Article

Uncovering Driver DNA Methylation Events in Nonsmoking Early Stage Lung Adenocarcinoma

1School of Computer Science and Technology, Xidian University, Xi’an 710000, China
2Department of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Shandong 250061, China
3Key Laboratory of Systems Biology, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China
4Institute of Industrial Science, University of Tokyo, Tokyo 153-8505, Japan
5School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China

Received 28 May 2016; Revised 28 June 2016; Accepted 5 July 2016

Academic Editor: Quan Zou

Copyright © 2016 Xindong Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

As smoking rates decrease, proportionally more cases with lung adenocarcinoma occur in never-smokers, while aberrant DNA methylation has been suggested to contribute to the tumorigenesis of lung adenocarcinoma. It is extremely difficult to distinguish which genes play key roles in tumorigenic processes via DNA methylation-mediated gene silencing from a large number of differentially methylated genes. By integrating gene expression and DNA methylation data, a pipeline combined with the differential network analysis is designed to uncover driver methylation genes and responsive modules, which demonstrate distinctive expressions and network topology in tumors with aberrant DNA methylation. Totally, 135 genes are recognized as candidate driver genes in early stage lung adenocarcinoma and top ranked 30 genes are recognized as driver methylation genes. Functional annotation and the differential network analysis indicate the roles of identified driver genes in tumorigenesis, while literature study reveals significant correlations of the top 30 genes with early stage lung adenocarcinoma in never-smokers. The analysis pipeline can also be employed in identification of driver epigenetic events for other cancers characterized by matched gene expression data and DNA methylation data.