Literature DB >> 23571108

DNA co-methylation analysis suggests novel functional associations between gene pairs in breast cancer samples.

Ruslan Akulenko1, Volkhard Helms.   

Abstract

Localized promoter hypermethylation and overall DNA hypomethylation have been associated with the presence of tumor in humans. Yet, despite the large amount of recently produced epigenetic data, there is still a lack of understanding on how several genes behave in tumor cells with respect to their epigenetic alterations such as DNA methylation. Here we performed a novel type of analysis that measures the correlation of DNA methylation levels between two genes across many samples. We linked this so-called co-methylation to the genomic distance of these genes, their functional similarity and their expression levels. Co-methylation analysis of more than 300 breast cancer samples from the TCGA portal yielded 187 pairs of genes showing Pearson correlation coefficients |r| ≥ 0.75. These pairs were formed by 133 genes. Less than half of these pairs are located on the same chromosome. For these, we found that the level of co-methylation is weakly anti-correlated with genomic distance (r = -0.29). Linking co-methylation with the functional similarity of genes showed that genes with r ≥ 0.8 tend to have similar molecular function and to be involved in the same biological process as described in the Gene Ontology project. Clustering of highly co-methylated genes identified four enriched KEGG pathways. Hence we have introduced co-methylation as a new indicator to discover functional associations between gene pairs in breast cancer and furthermore to discover new candidate genes that should be inspected more closely in the context of the studied disease.

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Year:  2013        PMID: 23571108     DOI: 10.1093/hmg/ddt158

Source DB:  PubMed          Journal:  Hum Mol Genet        ISSN: 0964-6906            Impact factor:   6.150


  15 in total

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2.  Integrative network-based approach identifies key genetic elements in breast invasive carcinoma.

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3.  BEclear: Batch Effect Detection and Adjustment in DNA Methylation Data.

Authors:  Ruslan Akulenko; Markus Merl; Volkhard Helms
Journal:  PLoS One       Date:  2016-08-25       Impact factor: 3.240

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Authors:  Yunzhen Wei; Zhiqiang Chang; Cheng Wu; Yinling Zhu; Kun Li; Yan Xu
Journal:  Oncotarget       Date:  2017-08-04

5.  Within-sample co-methylation patterns in normal tissues.

Authors:  Lillian Sun; Shuying Sun
Journal:  BioData Min       Date:  2019-05-09       Impact factor: 2.522

6.  Preliminary Analysis of Within-Sample Co-methylation Patterns in Normal and Cancerous Breast Samples.

Authors:  Lillian Sun; Surya Namboodiri; Emily Chen; Shuying Sun
Journal:  Cancer Inform       Date:  2019-10-05

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8.  Early-life DNA methylation profiles are indicative of age-related transcriptome changes.

Authors:  Niran Hadad; Dustin R Masser; Laura Blanco-Berdugo; David R Stanford; Willard M Freeman
Journal:  Epigenetics Chromatin       Date:  2019-10-08       Impact factor: 4.954

9.  Coregulation and modulation of NFκB-related genes in celiac disease: uncovered aspects of gut mucosal inflammation.

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Journal:  Hum Mol Genet       Date:  2013-10-24       Impact factor: 6.150

10.  Exemplary multiplex bisulfite amplicon data used to demonstrate the utility of Methpat.

Authors:  Nicholas C Wong; Bernard J Pope; Ida Candiloro; Darren Korbie; Matt Trau; Stephen Q Wong; Thomas Mikeska; Bryce J W van Denderen; Erik W Thompson; Stefanie Eggers; Stephen R Doyle; Alexander Dobrovic
Journal:  Gigascience       Date:  2015-11-26       Impact factor: 6.524

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