Ma Xiaoke, Liu Zaiyi, Zhang Zhongyuan, Huang Xiaotai, Tang Wanxin
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.
BMC Bioinformatics. 2017 Jan 31;18(1):72. doi: 10.1186/s12859-017-1490-6.
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 https://github.com/william0701/EMDN .
随着DNA甲基化和基因表达数据量的增加，癌症的表观遗传机制能够得到广泛研究。现有方法通过指定DNA甲基化与基因表达数据之间的反相关性将它们整合到一个网络中。然而，甲基化与表达之间的相关性通常未知且难以确定。
为解决这一问题，我们通过同时分析DNA甲基化和基因表达数据，提出了一种用于表观遗传模块的新型多重网络框架，即基于差异网络的表观遗传模块（EMDN）算法。EMDN算法避免了对甲基化与表达之间相关性的指定。EMDN算法的准确性比现代方法更高。基于癌症基因组图谱（TCGA）乳腺癌数据，我们观察到EMDN算法能够识别正相关和负相关模块，并且这些模块在已知通路中的富集程度明显高于其他算法得到的模块。这些模块可以作为生物标志物，通过使用甲基化谱来预测乳腺癌亚型，其中正相关和负相关模块在癌症亚型分类中具有同等重要性。表观遗传模块还能估计患者的生存时间，而这一因素对癌症治疗至关重要。
所提出的模型和算法为DNA甲基化和基因表达的综合分析提供了一种有效方法。该算法可作为R包在https://github.com/william0701/EMDN上免费获取。
