A systems-level integrative framework for genome-wide DNA methylation and gene expression data identifies differential gene expression modules under epigenetic control

Jiao, Yinming; Widschwendter, Martin; Teschendorff, Andrew E.

doi:10.1093/bioinformatics/btu316

Cited by 132 publications

(166 citation statements)

References 22 publications

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“…Specifically, for the TSS200, 1st exon and TSS1500 regions, DNAm levels in these regions generally exhibited an anti-correlation to gene expression, in line with the usual paradigm (Deaton and Bird 2011;Tate and Bird 1993). Given that these results were obtained in individual normal samples, using high-quality matched data, and that these results were congruent across so many independent samples, it does indeed support the view that for the 450k array probes, the TSS200 and 1st exon regions are the most predictive of gene expression (Jiao et al 2014). …”

supporting

confidence: 61%

“…Given that supervised network analysis of DNA methylation data is a promising approach , it is therefore natural to also consider a three-way integration of DNA methylation and gene expression with a PPI network, as done by the FEM (Functional Epigenetic Modules) algorithm (Jiao et al 2014;Jones et al 2013) (Fig. 8.2).…”

Section: Systems-level Integration Of Dna Methylation and Gene Expresmentioning

confidence: 99%

“…Doing so in the context of cancer may not only reveal functional epigenetic drivers of cancer but may also shed light on specific signalling pathways or mechanisms which contribute to carcinogenesis. Indeed, a clear example of how such integration can pinpoint a cancer driver was a study performed in endometrial tumours, which using FEM identified a gene called HAND2 as causally implicated in the genesis of this cancer (Jiao et al 2014;Jones et al 2013;. The FEM algorithm is freely available from www.…”

Section: Systems-level Integration Of Dna Methylation and Gene Expresmentioning

confidence: 99%

“…Vanderkraats et al 2013). A recent study (Jiao et al 2014) averaged the DNAm levels of probes falling within different gene regions and, using high-quality matched Illumina 450k and RNA-Seq data of many normal tissue samples, concluded that in all samples analysed the region 200 bp upstream of the TSS (TSS200) was the most informative of gene expression, followed by DNAm levels in the 1st exon, and finally DNAm levels located up to 1,500 bp upstream of the TSS (TSS1500). Specifically, for the TSS200, 1st exon and TSS1500 regions, DNAm levels in these regions generally exhibited an anti-correlation to gene expression, in line with the usual paradigm (Deaton and Bird 2011;Tate and Bird 1993).…”

mentioning

confidence: 95%

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Systems Epigenomics and Applications to Ageing and Cancer

Teschendorff

2015

Translational Bioinformatics

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One way to view epigenomics is in terms of representing the software of living cells. It is increasingly recognised that complex diseases like cancer are not only driven by defects in the genetic machinery (i.e. the underlying hardware) but also by defects in the epigenome. However, to improve our understanding of how epigenomic aberrations may contribute to the causal development of diseases like cancer will require a systems-level epigenomics approach which integrates different omic data types together. In this chapter, we describe three systems-level statistical methods which have been successful in identifying novel biomarkers for ageing, for cancer risk and for early detection of cancer. In addition, these systems-level methods have provided us with substantial novel insights into systems-level aspects of carcinogenesis, which we also describe.

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supporting

confidence: 61%

Section: Systems-level Integration Of Dna Methylation and Gene Expresmentioning

confidence: 99%

Section: Systems-level Integration Of Dna Methylation and Gene Expresmentioning

confidence: 99%

mentioning

confidence: 95%

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Systems Epigenomics and Applications to Ageing and Cancer

Teschendorff

2015

Translational Bioinformatics

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“…The Spearman correlation coefficient was calculated using the β value of each probe and is shown as log2 transformed 1+ reads per kilobase of transcript per million mapped reads values ( Figure 6). In accordance with the algorithm presented by Jiao et al (66) for each gene, the average β value of probes for the TSS200 was calculated.…”

Section: Methodsmentioning

confidence: 99%

Profiling cancer testis antigens in non–small-cell lung cancer

Djureinovic¹,

Hallström²,

Horie³

et al. 2016

JCI Insight

104

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Epigenome‐wide DNA methylation analysis of myasthenia gravis

et al. 2023

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Myasthenia gravis (MG) is a common neuromuscular junction disorder and autoimmune disease mediated by several antibodies. Several studies have shown that genetic factors play an important role in MG pathogenesis. To gain insight into the epigenetic factors affecting MG, we report here genome‐scale DNA methylation profiles of MG. DNA was extracted from eight MG patients and four healthy controls for genome‐wide DNA methylation analysis using the Illumina HumanMethylation 850K BeadChip. Verification of pyrosequencing was conducted based on differential methylation positions. Subsequently, C2C12 and HT22 cell lines (derived from mouse) were treated with demethylation drugs. Transcribed mRNA of the screened differential genes was detected using quantitative real‐time PCR. The control and MG group were compared, and two key probe positions were selected. The corresponding genes were CAMK1D and CREB5 (P < 0.05). Similarly, the myasthenic crisis (MC) and non‐MC group were compared and four key probe positions were selected. The corresponding genes were SAV1, STK3, YAP1, and WWTR1 (P < 0.05). Subsequently, pyrosequencing was performed for verification, revealing that hypomethylation of CAMK1D was significantly different between the MG and control group (P < 0.001). Moreover, transcription of CREB5, PKD, YAP1, and STK3 genes in the C2C12 cells was downregulated (P < 0.05) after drug treatment, but only YAP1 mRNA was downregulated in HT22 cells (P < 0.05). This is the first study to investigate genome‐scale DNA methylation profiles of MG using 850 K BeadChip. The identified molecular markers of methylation may aid in the prevention, diagnosis, treatment, and prognosis of MG.

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A systems-level integrative framework for genome-wide DNA methylation and gene expression data identifies differential gene expression modules under epigenetic control

Abstract: FEM is freely available as an R-package from http://sourceforge.net/projects/funepimod.

Cited by 132 publications

References 22 publications

Systems Epigenomics and Applications to Ageing and Cancer

Systems Epigenomics and Applications to Ageing and Cancer

Profiling cancer testis antigens in non–small-cell lung cancer

Epigenome‐wide DNA methylation analysis of myasthenia gravis

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