scholarly journals Integrative analysis with expanded DNA methylation data reveals common key regulators and pathways in cancers

2018 ◽  
Author(s):  
Shicai Fan ◽  
Jianxiong Tang ◽  
Nan Li ◽  
Ying Zhao ◽  
Rizi Ai ◽  
...  

AbstractThe integration of genomic and DNA methylation data has been demonstrated as a powerful strategy in understanding cancer mechanisms and identifying therapeutic targets. The TCGA consortium has mapped DNA methylation in thousands of cancer samples using Illumina Infinium Human Methylation 450K BeadChip (Illumina 450K array) that only covers about 1.5% of CpGs in the human genome. Therefore, increasing the coverage of the DNA methylome would significantly leverage the usage of the TCGA data. Here, we present a new model called EAGLING that can expand the Illumina 450K array data 18 times to cover about 30% of the CpGs in the human genome. We applied it to analyze 13 cancers in TCGA. By integrating the expanded methylation, gene expression and somatic mutation data, we identified the genes showing differential patterns in each of the 13 cancers. Many of the triple-evidenced genes identified in the majority of the cancers are biomarkers or potential biomarkers. Pan-cancer analysis also revealed the pathways in which the triple-evidenced genes are enriched, which include well known ones as well as new ones such as axonal guidance signaling pathway and pathways related to inflammatory processing or inflammation response. Triple-evidenced genes, particularly TNXB, RRM2, CELSR3, SLC16A3, FANCI, MMP9, MMP11, SIK1, TRIM59, showed superior predictive power in both tumor diagnosis and prognosis. These results have demonstrated that the integrative analysis using the expanded methylation data is powerful in identifying critical genes/pathways that may serve as new therapeutic targets.

2018 ◽  
Vol 47 (3) ◽  
pp. 899-907 ◽  
Author(s):  
Ida K Karlsson ◽  
Alexander Ploner ◽  
Yunzhang Wang ◽  
Margaret Gatz ◽  
Nancy L Pedersen ◽  
...  

Abstract Background This study aims to investigate if DNA methylation of the apolipoprotein E (APOE) locus affects the risks of dementia, Alzheimeŕs disease (AD) or cardiovascular disease (CVD). Methods DNA methylation across theAPOE gene has previously been categorized into three distinct regions: a hypermethylated region in the promoter, a hypomethylated region in the first two introns and exons and a hypermethylated region in the 3′exon that also harbours theAPOE ε2 and ε4 alleles. DNA methylation levels in leukocytes were measured using the Illumina 450K array in 447 Swedish twins (mean age 78.1 years). We used logistic regression to investigate whether methylation levels in those regions affect the odds of disease. Results We found that methylation levels in the promoter region were associated with dementia and AD after adjusting for sex, age at blood draw, education, smoking and relatedness among twins [odds ratio (OR) 1.32 per standard deviation increase in methylation levels, 95% confidence interval (CI) 1.08–1.62 for dementia; OR 1.38, 95% CI 1.07–1.78 for AD). We did not detect any difference in methylation levels between CVD cases and controls. Results were similar when comparing within discordant twin pairs, and did not differ as a function ofAPOE genotype. Conclusions We found that higher DNA methylation levels in the promoter region ofAPOE increase the odds of dementia and AD, but not CVD. The effect was independent ofAPOE genotype, indicating that allelic variation and methylation variation inAPOE may act independently to increase the risk of dementia.


Epigenetics ◽  
2018 ◽  
Vol 13 (1) ◽  
pp. 19-32 ◽  
Author(s):  
Marie Forest ◽  
Kieran J. O'Donnell ◽  
Greg Voisin ◽  
Helene Gaudreau ◽  
Julia L. MacIsaac ◽  
...  

2019 ◽  
Vol 4 (1) ◽  
Author(s):  
Shicai Fan ◽  
Jianxiong Tang ◽  
Nan Li ◽  
Ying Zhao ◽  
Rizi Ai ◽  
...  

2013 ◽  
Vol 6 (1) ◽  
pp. 26 ◽  
Author(s):  
Roderick C Slieker ◽  
Steffan D Bos ◽  
Jelle J Goeman ◽  
Judith VMG Bovée ◽  
Rudolf P Talens ◽  
...  

2016 ◽  
Vol 27 (9) ◽  
pp. 2627-2640
Author(s):  
Chenyang Wang ◽  
Qi Shen ◽  
Li Du ◽  
Jinfeng Xu ◽  
Hong Zhang

DNA methylation has been shown to play an important role in many complex diseases. The rapid development of high-throughput DNA methylation scan technologies provides great opportunities for genomewide DNA methylation-disease association studies. As methylation is a dynamic process involving time, it is quite plausible that age contributes to its variation to a large extent. Therefore, in analyzing genomewide DNA methylation data, it is important to identify age-related DNA methylation marks and delineate their functional relationship. This helps us to better understand the underlying biological mechanism and facilitate early diagnosis and prognosis analysis of complex diseases. We develop a functional beta model for analyzing DNA methylation data and detecting age-related DNA methylation marks on the whole genome by naturally taking sampling scheme into account and accommodating flexible age-methylation dynamics. We focus on DNA methylation data obtained through the widely used bisulfite conversion technique and propose to use a beta model to relate the DNA methylation level to the age. Adjusting for certain confounders, the functional age effect is left completely unspecified, offering great flexibility and allowing extra data dynamics. An efficient algorithm is developed for estimating unknown parameters, and the Wald test is used to detect age-related DNA methylation marks. Simulation studies and several real data applications were provided to demonstrate the performance of the proposed method.


Epigenetics ◽  
2013 ◽  
Vol 8 (11) ◽  
pp. 1141-1152 ◽  
Author(s):  
Paul Yousefi ◽  
Karen Huen ◽  
Raul Aguilar Schall ◽  
Anna Decker ◽  
Emon Elboudwarej ◽  
...  

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