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Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Genet. | doi: 10.3389/fgene.2019.00774

A hybrid ensemble approach for identifying robust differentially methylated loci in pan-cancers

 Qi Tian1, Jianxiao Zou1, Fang Yuan1, Zhongli Yu1, Jianxiong Tang1, Ying Song1 and Shicai Fan1, 2*
  • 1University of Electronic Science and Technology of China, China
  • 2Center for Information in BioMedicine, University of Electronic Science and Technology of China, China

DNA methylation is a widely investigated epigenetic mark which plays a vital role in tumorigenesis. Advancements in high-throughput assays, such as the Infinium 450K platform, provide genome-scale DNA methylation landscapes in single-CpG-locus resolution, and the identification of differentially methylated loci has become an insightful approach to deepen our understanding of cancers. However, the situation that extremely unbalanced numbers of samples and loci (approximately 1:1000), makes it rather difficult to explore differential methylation between the sick and the normal. In this paper, a Hybird approach based on ensemble feature selection for identifying Differentially Methylated Loci (HyDML) was proposed by incorporating instance perturbation and multiple function models. Experiments on data from The Cancer Genome Atlas (TCGA) showed that HyDML not only achieved effective DML identification, but also outperformed the single feature selection approach in terms of classification performance and the robustness of feature selection. The intensive analysis of the DML indicated that different types of cancers have mutual patterns, and the stable DML sharing in pan-caners are of the great potential to be biomarkers, which may strengthen the confidence of domain experts to implement biological validations.

Keywords: DNA Methylation, Differentially methylated loci, Ensemble feature selection, robustness, pan-cancers

Received: 10 May 2019; Accepted: 23 Jul 2019.

Edited by:

Yun Liu, School of Basic Medical Sciences, Fudan University, China

Reviewed by:

Daniel Vaiman, Institut National de la Santé et de la Recherche Médicale (INSERM), France
Osman A. El-Maarri, University of Bonn, Germany  

Copyright: © 2019 Tian, Zou, Yuan, Yu, Tang, Song and Fan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Shicai Fan, University of Electronic Science and Technology of China, Chengdu, China, shicaifan@uestc.edu.cn