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Gene-set Analysis with CGI Information for Differential DNA Methylation Profiling.

Chang CW, Lu TP, She CX, Feng YC, Hsiao CK - Sci Rep (2016)

Bottom Line: Here we aimed to include both pathway information and CGI status to rank competing gene-sets and identify among them the genes most likely contributing to DNA methylation changes.Results show that, based on probabilities, the importance of pathways and genes can be determined.The findings confirm that some of these genes are cancer-related and may hold the potential to be targeted in drug development.

View Article: PubMed Central - PubMed

Affiliation: Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei 10055, Taiwan.

ABSTRACT
DNA methylation is a well-established epigenetic biomarker for many diseases. Studying the relationships among a group of genes and their methylations may help to unravel the etiology of diseases. Since CpG-islands (CGIs) play a crucial role in the regulation of transcription during methylation, including them in the analysis may provide further information in understanding the pathogenesis of cancers. Such CGI information, however, has usually been overlooked in existing gene-set analyses. Here we aimed to include both pathway information and CGI status to rank competing gene-sets and identify among them the genes most likely contributing to DNA methylation changes. To accomplish this, we devised a Bayesian model for matched case-control studies with parameters for CGI status and pathway associations, while incorporating intra-gene-set information. Three cancer studies with candidate pathways were analyzed to illustrate this approach. The strength of association for each candidate pathway and the influence of each gene were evaluated. Results show that, based on probabilities, the importance of pathways and genes can be determined. The findings confirm that some of these genes are cancer-related and may hold the potential to be targeted in drug development.

No MeSH data available.


Related in: MedlinePlus

Overview of procedures.
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f1: Overview of procedures.

Mentions: An overview of the procedures, including subject matching, examination of CGI status, and construction of gene-set information, is outlined in Fig. 1. To illustrate how the above model and functions can be applied, we consider first the United Kingdom Ovarian Cancer Population Study (UKOPS)20. The DNA methylation data are available from the GEO database (accession number GSE19711) with 27,578 CpGs per sample. The methylation level of each specific CpG site was calculated from the intensity values of methylated and unmethylated DNA beads as a ratio of fluorescent signals, called β values. The procedures for data management—outlier detection and removal of batch effect, normalization, and matching—are explained in Supplementary Text S1, available online. To remove the confounding effect due to age, we matched the case-control pairs by restricting the difference in age to be less than three, obtaining 104 case-control pairs for subsequent analysis.


Gene-set Analysis with CGI Information for Differential DNA Methylation Profiling.

Chang CW, Lu TP, She CX, Feng YC, Hsiao CK - Sci Rep (2016)

Overview of procedures.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4836301&req=5

f1: Overview of procedures.
Mentions: An overview of the procedures, including subject matching, examination of CGI status, and construction of gene-set information, is outlined in Fig. 1. To illustrate how the above model and functions can be applied, we consider first the United Kingdom Ovarian Cancer Population Study (UKOPS)20. The DNA methylation data are available from the GEO database (accession number GSE19711) with 27,578 CpGs per sample. The methylation level of each specific CpG site was calculated from the intensity values of methylated and unmethylated DNA beads as a ratio of fluorescent signals, called β values. The procedures for data management—outlier detection and removal of batch effect, normalization, and matching—are explained in Supplementary Text S1, available online. To remove the confounding effect due to age, we matched the case-control pairs by restricting the difference in age to be less than three, obtaining 104 case-control pairs for subsequent analysis.

Bottom Line: Here we aimed to include both pathway information and CGI status to rank competing gene-sets and identify among them the genes most likely contributing to DNA methylation changes.Results show that, based on probabilities, the importance of pathways and genes can be determined.The findings confirm that some of these genes are cancer-related and may hold the potential to be targeted in drug development.

View Article: PubMed Central - PubMed

Affiliation: Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei 10055, Taiwan.

ABSTRACT
DNA methylation is a well-established epigenetic biomarker for many diseases. Studying the relationships among a group of genes and their methylations may help to unravel the etiology of diseases. Since CpG-islands (CGIs) play a crucial role in the regulation of transcription during methylation, including them in the analysis may provide further information in understanding the pathogenesis of cancers. Such CGI information, however, has usually been overlooked in existing gene-set analyses. Here we aimed to include both pathway information and CGI status to rank competing gene-sets and identify among them the genes most likely contributing to DNA methylation changes. To accomplish this, we devised a Bayesian model for matched case-control studies with parameters for CGI status and pathway associations, while incorporating intra-gene-set information. Three cancer studies with candidate pathways were analyzed to illustrate this approach. The strength of association for each candidate pathway and the influence of each gene were evaluated. Results show that, based on probabilities, the importance of pathways and genes can be determined. The findings confirm that some of these genes are cancer-related and may hold the potential to be targeted in drug development.

No MeSH data available.


Related in: MedlinePlus