Limits...
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) Plots of pathway effects. (A) Scores of strength for the 10 competing pathways in the UKOPS study. (B) Boxplots of posterior samples under each pathway for 32 lung adenocarcinoma patients. A box beyond zero implies a large probability of hypermethylation; while a box below zero indicates a large probability of hypomethylation. (C) Probability density plot for each of the four pathway effects. Most of the red curve for the effect of axonal guidance signaling pathway locates in the positive part, indicating a strong hypermethylation effect for this pathway. (D) Boxplots of posterior samples in each pathway in the breast cancer study.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4836301&req=5

f4: (A) Plots of pathway effects. (A) Scores of strength for the 10 competing pathways in the UKOPS study. (B) Boxplots of posterior samples under each pathway for 32 lung adenocarcinoma patients. A box beyond zero implies a large probability of hypermethylation; while a box below zero indicates a large probability of hypomethylation. (C) Probability density plot for each of the four pathway effects. Most of the red curve for the effect of axonal guidance signaling pathway locates in the positive part, indicating a strong hypermethylation effect for this pathway. (D) Boxplots of posterior samples in each pathway in the breast cancer study.

Mentions: An alternative way to compare these pathways is to rank the distances between the probabilities in Table 1 and 0.5 (i.e., no difference). For the purpose of easy interpretation, we rescaled the distance by multiplying by 2, making the distance range from 0 to 1, and called it the score of strength. Figure 4A plots the scores for the corresponding pathways. Clearly the top two pathways stand out. Details of the values are in Supplementary Table S1. To assess which model was most promising for further inference, we considered the DIC (Deviance Information Criterion) under each model. No specific model fitted better than the others, hence the model with the CGI-dependent pathway effect (model (b2)) was selected based on the principle of parsimony and for its better interpretability. The resulting model (b2) was then used to detect influential genes and was compared with other analyses, as discussed in the next section.


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) Plots of pathway effects. (A) Scores of strength for the 10 competing pathways in the UKOPS study. (B) Boxplots of posterior samples under each pathway for 32 lung adenocarcinoma patients. A box beyond zero implies a large probability of hypermethylation; while a box below zero indicates a large probability of hypomethylation. (C) Probability density plot for each of the four pathway effects. Most of the red curve for the effect of axonal guidance signaling pathway locates in the positive part, indicating a strong hypermethylation effect for this pathway. (D) Boxplots of posterior samples in each pathway in the breast cancer study.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f4: (A) Plots of pathway effects. (A) Scores of strength for the 10 competing pathways in the UKOPS study. (B) Boxplots of posterior samples under each pathway for 32 lung adenocarcinoma patients. A box beyond zero implies a large probability of hypermethylation; while a box below zero indicates a large probability of hypomethylation. (C) Probability density plot for each of the four pathway effects. Most of the red curve for the effect of axonal guidance signaling pathway locates in the positive part, indicating a strong hypermethylation effect for this pathway. (D) Boxplots of posterior samples in each pathway in the breast cancer study.
Mentions: An alternative way to compare these pathways is to rank the distances between the probabilities in Table 1 and 0.5 (i.e., no difference). For the purpose of easy interpretation, we rescaled the distance by multiplying by 2, making the distance range from 0 to 1, and called it the score of strength. Figure 4A plots the scores for the corresponding pathways. Clearly the top two pathways stand out. Details of the values are in Supplementary Table S1. To assess which model was most promising for further inference, we considered the DIC (Deviance Information Criterion) under each model. No specific model fitted better than the others, hence the model with the CGI-dependent pathway effect (model (b2)) was selected based on the principle of parsimony and for its better interpretability. The resulting model (b2) was then used to detect influential genes and was compared with other analyses, as discussed in the next section.

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