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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

A hypothetical gene-set.Gene nodes in red (G1, G2, and G5) contain no probes in CGIs; while nodes in blue (G3, G4, and G6) contain probes in CGI regions. All gene nodes but G5 belong to this gene-set. The number of edges represents the number of genes connected to it.
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f3: A hypothetical gene-set.Gene nodes in red (G1, G2, and G5) contain no probes in CGIs; while nodes in blue (G3, G4, and G6) contain probes in CGI regions. All gene nodes but G5 belong to this gene-set. The number of edges represents the number of genes connected to it.

Mentions: To illustrate how to evaluate the pathway effects, we denote first the CGI-dependent effect βj for each probe Pj, where j = 1, …, 1,675 for the UKOPS data. Each probe Pj was examined first to see if its corresponding gene Gj falls in the kth pathway where k = 1, …, 10. Note that the same gene can occur in more than one pathway, i.e. the indicator function Ik(P)j can be 1 for more than one k. Figure 3 provides a hypothetical gene-set. In each gene node, its CGI status is 1 (Ik(P)j = 1) if Pj is in a CGI, Ejk is the number of neighbors of gene Gj in the kth pathway. No differentiation is made between the incoming and outgoing edges in the current settings. For selecting the best model, we adopt deviance information criterion (DIC), a common measure for Bayesian model selection. Other details of the specifications, computations, and the codes to be used in the R package R2OpenBUGS are listed in Supplementary Text S1.


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

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

A hypothetical gene-set.Gene nodes in red (G1, G2, and G5) contain no probes in CGIs; while nodes in blue (G3, G4, and G6) contain probes in CGI regions. All gene nodes but G5 belong to this gene-set. The number of edges represents the number of genes connected to it.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f3: A hypothetical gene-set.Gene nodes in red (G1, G2, and G5) contain no probes in CGIs; while nodes in blue (G3, G4, and G6) contain probes in CGI regions. All gene nodes but G5 belong to this gene-set. The number of edges represents the number of genes connected to it.
Mentions: To illustrate how to evaluate the pathway effects, we denote first the CGI-dependent effect βj for each probe Pj, where j = 1, …, 1,675 for the UKOPS data. Each probe Pj was examined first to see if its corresponding gene Gj falls in the kth pathway where k = 1, …, 10. Note that the same gene can occur in more than one pathway, i.e. the indicator function Ik(P)j can be 1 for more than one k. Figure 3 provides a hypothetical gene-set. In each gene node, its CGI status is 1 (Ik(P)j = 1) if Pj is in a CGI, Ejk is the number of neighbors of gene Gj in the kth pathway. No differentiation is made between the incoming and outgoing edges in the current settings. For selecting the best model, we adopt deviance information criterion (DIC), a common measure for Bayesian model selection. Other details of the specifications, computations, and the codes to be used in the R package R2OpenBUGS are listed in Supplementary Text S1.

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