Zhang Xindong, Gao Lin, Liu Zhi-Ping, Jia Songwei, Chen Luonan
School of Computer Science and Technology, Xidian University, Xi'an 710000, China.
Department of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Shandong 250061, China.
Biomed Res Int. 2016;2016:2090286. doi: 10.1155/2016/2090286. Epub 2016 Aug 17.
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.
随着吸烟率下降，从不吸烟者中发生肺腺癌的病例比例相应增加，而异常DNA甲基化被认为与肺腺癌的肿瘤发生有关。从大量差异甲基化基因中区分哪些基因通过DNA甲基化介导的基因沉默在肿瘤发生过程中起关键作用极其困难。通过整合基因表达和DNA甲基化数据，设计了一种结合差异网络分析的流程，以发现驱动甲基化基因和反应模块，这些基因和模块在具有异常DNA甲基化的肿瘤中表现出独特的表达和网络拓扑结构。总共135个基因被识别为早期肺腺癌的候选驱动基因，排名前30的基因被识别为驱动甲基化基因。功能注释和差异网络分析表明了所识别的驱动基因在肿瘤发生中的作用，而文献研究揭示了排名前30的基因与从不吸烟者的早期肺腺癌之间存在显著相关性。该分析流程也可用于识别以匹配的基因表达数据和DNA甲基化数据为特征的其他癌症的驱动表观遗传事件。
