Chang Chia-Wei, Lu Tzu-Pin, She Chang-Xian, Feng Yen-Chen, Hsiao Chuhsing Kate
Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei 10055, Taiwan.
Bioinformatics and Biostatistics Core, Center of Genomic Medicine, National Taiwan University, Taipei 10055, Taiwan.
Sci Rep. 2016 Apr 19;6:24666. doi: 10.1038/srep24666.
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.
DNA甲基化是许多疾病公认的表观遗传生物标志物。研究一组基因与其甲基化之间的关系可能有助于揭示疾病的病因。由于CpG岛（CGIs）在甲基化过程中的转录调控中起关键作用，将其纳入分析可能会为理解癌症的发病机制提供更多信息。然而，在现有的基因集分析中，此类CGI信息通常被忽视。在这里，我们旨在纳入通路信息和CGI状态，对竞争基因集进行排序，并在其中识别最有可能导致DNA甲基化变化的基因。为实现这一目标，我们设计了一种用于匹配病例对照研究的贝叶斯模型，该模型具有CGI状态和通路关联的参数，同时纳入了基因集内信息。对三项具有候选通路的癌症研究进行了分析，以说明这种方法。评估了每个候选通路的关联强度和每个基因的影响。结果表明，基于概率，可以确定通路和基因的重要性。这些发现证实，其中一些基因与癌症相关，可能具有在药物开发中作为靶点的潜力。
