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Current Bioinformatics

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ISSN (Print): 1574-8936
ISSN (Online): 2212-392X

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Research Article

Genome-wide Differential-based Analysis of the Relationship between DNA Methylation and Gene Expression in Cancer

Author(s): Yuanyuan Zhang*, Chuanhua Kou, Shudong Wang and Yulin Zhang

Volume 14, Issue 8, 2019

Page: [783 - 792] Pages: 10

DOI: 10.2174/1574893614666190424160046

Price: $65

Abstract

Background: DNA methylation is an epigenetic modification that plays an important role in regulating gene expression. There is evidence that the hypermethylation of promoter regions always causes gene silencing. However, how the methylation patterns of other regions in the genome, such as gene body and 3’UTR, affect gene expression is unknown.

Objective: The study aimed to fully explore the relationship between DNA methylation and expression throughout the genome-wide analysis which is important in understanding the function of DNA methylation essentially.

Methods: In this paper, we develop a heuristic framework to analyze the relationship between the methylated change in different regions and that of the corresponding gene expression based on differential analysis.

Results: To understande the methylated function of different genomic regions, a gene is divided into seven functional regions. By applying the method in five cancer datasets from the Synapse database, it was found that methylated regions with a significant difference between cases and controls were almost uniformly distributed in the seven regions of the genome. Also, the effect of DNA methylation in different regions on gene expression was different. For example, there was a higher percentage of positive relationships in 1stExon, gene body and 3’UTR than in TSS1500 and TSS200. The functional analysis of genes with a significant positive and negative correlation between DNA methylation and gene expression demonstrated the epigenetic mechanism of cancerassociated genes.

Conclusion: Differential based analysis helps us to recognize the change in DNA methylation and how this change affects the change in gene expression. It provides a basis for further integrating gene expression and DNA methylation data to identify disease-associated biomarkers.

Keywords: DNA methylation, gene expression, differential analysis, Genome-wide analysis, heuristic, biomarkers.

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