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

Xiaoke Ma,Xiaotai Huang,Zhongyuan Zhang,Zaiyi Liu,Wanxin Tang

doi:10.1186/s12859-017-1490-6

Abstract

BackgroundWith 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.ResultsTo 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.ConclusionsThe 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 https://github.com/william0701/EMDN.

Highlights

With the increase in the amount of DNA methylation and gene expression data, the epigenetic mechanisms of cancers can be extensively investigate
Performance benchmarking of algorithms on artificial networks For a comparative analysis, three algorithms are selected, including Consensus clustering (CSC) [22], multiplemodularity method (MolTi) [23] and spectral clustering (SPEC) [33]
The accuracy of various algorithms on the artificial networks is shown in Fig. 2c, where Epigenetic Module based on Differential Networks (EMDN) outperforms the others and the MolTi algorithm is better than the CSC and SPEC methods

Summary

Introduction

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. DNA methylation is a chemical modification of cytosine bases, which is critical for cellular differentiation, cell development and disease progression [1,2,3]. DNA methylation directly inhibits the binding of transcription factors [4], and methylation aberrations either predispose to or result in disease progression [5]. High-throughput technologies have generated largescale genome-wide DNA methylation profiles for various cancers and cell lines, providing great opportunities for revealing the epigenetic mechanisms. Hinoue et al [12] recognized four distinct subgroups in colorectal cancer by analyzing large-scale genome-wide DNA methylation

Methods

Results

Conclusion