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.
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.
Entities:
Keywords:
Cancer Gene; DNA Methylation; Gene Expression Profile; Ovarian Cancer; Protein Interaction Maps
Authors: Laurie A Boyer; Tong Ihn Lee; Megan F Cole; Sarah E Johnstone; Stuart S Levine; Jacob P Zucker; Matthew G Guenther; Roshan M Kumar; Heather L Murray; Richard G Jenner; David K Gifford; Douglas A Melton; Rudolf Jaenisch; Richard A Young Journal: Cell Date: 2005-09-23 Impact factor: 41.582
Authors: Brigitte L Thériault; Halesha D Basavarajappa; Harvey Lim; Sanja Pajovic; Brenda L Gallie; Timothy W Corson Journal: PLoS One Date: 2014-03-13 Impact factor: 3.240