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BMC Bioinformatics. 2017 Jan 31;18(1):72. doi: 10.1186/s12859-017-1490-6.

Multiple network algorithm for epigenetic modules via the integration of genome-wide DNA methylation and gene expression data.

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School of Computer Science and Technology, Xidian University, No.2 South TaiBai Road, Xi'an, People's Republic of China.
Xidian-Ningbo Information Technology Institute, Xidian University, No. 777 Zhongguanxi Road, Ningbo, People's Republic of China.
Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Zhongshan Road, Guangzhou, People's Republic of China.
School of Statistics and Mathematics, Central University of Finance and Economics, 39 South College Road, Haidian District, Beijing, People's Republic of China.
Department of Nephrology, West China Hospital, Sichuan University, Wuhou District, Chengdu, People's Republic of China.



With the increase in the amount of DNA methylation and gene expression data, the epigenetic mechanisms of cancers can be extensively investigate. Available methods integrate the DNA methylation and gene expression data into a network by specifying the anti-correlation between them. However, the correlation between methylation and expression is usually unknown and difficult to determine.


To address this issue, we present a novel multiple network framework for epigenetic modules, namely, Epigenetic Module based on Differential Networks (EMDN) algorithm, by simultaneously analyzing DNA methylation and gene expression data. The EMDN algorithm prevents the specification of the correlation between methylation and expression. The accuracy of EMDN algorithm is more efficient than that of modern approaches. On the basis of The Cancer Genome Atlas (TCGA) breast cancer data, we observe that the EMDN algorithm can recognize positively and negatively correlated modules and these modules are significantly more enriched in the known pathways than those obtained by other algorithms. These modules can serve as bio-markers to predict breast cancer subtypes by using methylation profiles, where positively and negatively correlated modules are of equal importance in the classification of cancer subtypes. Epigenetic modules also estimate the survival time of patients, and this factor is critical for cancer therapy.


The proposed model and algorithm provide an effective method for the integrative analysis of DNA methylation and gene expression. The algorithm is freely available as an R-package at .


Epigenetic module; Methylation; Multiple networks; Network biology

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