Extracting coordinated patterns of DNA methylation and gene expression in ovarian cancer
- 1Division of Biomedical Informatics, Seoul National University Biomedical Informatics (SNUBI), Seoul, Republic of Korea
- 2Systems Biomedical Informatics National Core Research Center, Seoul National University College of Medicine, Seoul, Republic of Korea
- 3Institute of Endemic Diseases, Seoul National University College of Medicine, Seoul, Korea
- Correspondence to Professor Ju Han Kim, Seoul National University Biomedical Informatics (SNUBI), Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul 110-799, Korea;
- Received 13 December 2012
- Revised 18 February 2013
- Accepted 29 March 2013
- Published Online First 18 April 2013
Objective DNA methylation, a regulator of gene expression, plays an important role in diverse biological processes including developmental process, carcinogenesis and aging. In particular, aberrant DNA methylation has been largely observed in several types of cancers. Currently, it is important to extract disease-specific gene sets associated with the regulation of DNA methylation.
Materials and methods Here we propose a novel approach to find the minimum regulatory units of genes, co-methylated and co-expressed gene pairs (MEGP) that are highly correlated gene pairs between DNA methylation and gene expression showing the co-regulatory relationship. To evaluate whether our method is applicable to extract disease-associated genes, we applied our method to a large-scale dataset from the Cancer Genome Atlas extracting significantly associated MEGP and analyzed their functional correlation.
Results We observed that many MEGP physically interacted with each other and showed high semantic similarity with gene ontology terms. Furthermore, we performed gene set enrichment tests to identify how they are correlated in a complex biological process. Our MEGP were highly enriched in the biological pathway associated with ovarian cancers.
Conclusions Our approach is useful for discovering coordinated epigenetic markers associated with specific diseases.