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Bioinformatics
Article . 2017 . Peer-reviewed
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Bioinformatics
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Bioinformatics
Article . 2019
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Article . 2018
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Integrating 5hmC and gene expression data to infer regulatory mechanisms

Authors: Cristina Mitrea; Priyanga Wijesinghe; Gregory Dyson; Adéle Kruger; Douglas M. Ruden; Sorin Draghici; Aliccia Bollig-Fischer;

Integrating 5hmC and gene expression data to infer regulatory mechanisms

Abstract

Abstract Motivation Epigenetic mechanisms are known to play a major role in breast cancer. However, the role of 5-hydroxymethylcytosine (5hmC) remains understudied. We hypothesize that 5hmC mediates redox regulation of gene expression in an aggressive subtype known as triple negative breast cancer (TNBC). To address this, our objective was to highlight genes that may be the target of this process by identifying redox-regulated, antioxidant-sensitive, gene-localized 5hmC changes associated with mRNA changes in TNBC cells. Results We proceeded to develop an approach to integrate novel Pvu-sequencing and RNA-sequencing data. The result of our approach to merge genome-wide, high-throughput TNBC cell line datasets to identify significant, concordant 5hmC and mRNA changes in response to antioxidant treatment produced a gene set with relevance to cancer stem cell function. Moreover, we have established a method that will be useful for continued research of 5hmC in TNBC cells and tissue samples. Availability and implementation Data are available at Gene Expression Omnibus (GEO) under accession number GSE103850.

Related Organizations
Keywords

Sequence Analysis, RNA, Gene Expression Profiling, Computational Biology, Triple Negative Breast Neoplasms, Sequence Analysis, DNA, Epigenesis, Genetic, Gene Expression Regulation, Neoplastic, Cell Line, Tumor, 5-Methylcytosine, Humans, Female

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    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    11
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
11
Top 10%
Average
Top 10%
gold
Related to Research communities
Cancer Research