Dataset Information

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

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.

SUBMITTER: Chang CW

PROVIDER: S-EPMC4836301 | biostudies-literature | 2016 Apr

REPOSITORIES: biostudies-literature

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

Chang Chia-Wei CW Lu Tzu-Pin TP She Chang-Xian CX Feng Yen-Chen YC Hsiao Chuhsing Kate CK

Scientific reports 20160419

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 ...[more]

PMID: 27090937

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Project description:BACKGROUND: The Human Papillomavirus (HPV) genome is divided into early and late coding sequences, including 8 open reading frames (ORFs) and a regulatory region (LCR). Viral gene expression may be regulated through epigenetic mechanisms, including cytosine methylation at CpG dinucleotides. We have analyzed the distribution of CpG sites and CpG islands/clusters (CGI) among 92 different HPV genomes grouped in function of their preferential tropism: cutaneous or mucosal. We calculated the proportion of CpG sites (PCS) for each ORF and calculated the expected CpG values for each viral type. RESULTS: CpGs are underrepresented in viral genomes. We found a positive correlation between CpG observed and expected values, with mucosal high-risk (HR) virus types showing the smallest O/E ratios. The ranges of the PCS were similar for most genomic regions except E4, where the majority of CpGs are found within islands/clusters. At least one CGI belongs to each E2/E4 region. We found positive correlations between PCS for each viral ORF when compared with the others, except for the LCR against four ORFs and E6 against three other ORFs. The distribution of CpG islands/clusters among HPV groups is heterogeneous and mucosal HR-HPV types exhibit both lower number and shorter island sizes compared to cutaneous and mucosal Low-risk (LR) HPVs (all of them significantly different). CONCLUSIONS: There is a difference between viral and cellular CpG underrepresentation. There are significant correlations between complete genome PCS and a lack of correlations between several genomic region pairs, especially those involving LCR and E6. L2 and L1 ORF behavior is opposite to that of oncogenes E6 and E7. The first pair possesses relatively low numbers of CpG sites clustered in CGIs while the oncogenes possess a relatively high number of CpG sites not associated to CGIs. In all HPVs, E2/E4 is the only region with at least one CGI and shows a higher content of CpG sites in every HPV type with an identified E4. The mucosal HR-HPVs show either the shortest CGI size, followed by the mucosal LR-HPVs and lastly by the cutaneous viral subgroup, and a trend to the lowest CGI number, followed by the cutaneous viral subgroup and lastly by the mucosal LR-HPVs.

Project description:Multiple genetic approaches have identified microRNAs as key effectors in psychiatric disorders as they post-transcriptionally regulate expression of thousands of target genes. However, their role in specific psychiatric diseases remains poorly understood. In addition, epigenetic mechanisms such as DNA methylation, which affect the expression of both microRNAs and coding genes, are critical for our understanding of molecular mechanisms in schizophrenia. Using clinical, imaging, genetic, and epigenetic data of 103 patients with schizophrenia and 111 healthy controls of the Mind Clinical Imaging Consortium (MCIC) study of schizophrenia, we conducted gene set enrichment analysis to identify markers for schizophrenia-associated intermediate phenotypes. Genes were ranked based on the correlation between DNA methylation patterns and each phenotype, and then searched for enrichment in 221 predicted microRNA target gene sets. We found the predicted hsa-miR-219a-5p target gene set to be significantly enriched for genes (EPHA4, PKNOX1, ESR1, among others) whose methylation status is correlated with hippocampal volume independent of disease status. Our results were strengthened by significant associations between hsa-miR-219a-5p target gene methylation patterns and hippocampus-related neuropsychological variables. IPA pathway analysis of the respective predicted hsa-miR-219a-5p target genes revealed associated network functions in behavior and developmental disorders. Altered methylation patterns of predicted hsa-miR-219a-5p target genes are associated with a structural aberration of the brain that has been proposed as a possible biomarker for schizophrenia. The (dys)regulation of microRNA target genes by epigenetic mechanisms may confer additional risk for developing psychiatric symptoms. Further study is needed to understand possible interactions between microRNAs and epigenetic changes and their impact on risk for brain-based disorders such as schizophrenia.

Project description:BackgroundDNA methylation is a key epigenetic regulator contributing to cancer development. To understand the role of DNA methylation in tumorigenesis, it is important to investigate and compare differential methylation (DM) patterns between normal and case samples across different cancer types. However, current pan-cancer analyses call DM separately for each cancer, which suffers from lower statistical power and fails to provide a comprehensive view for patterns across cancers.MethodsIn this work, we propose a rigorous statistical model, PanDM, to jointly characterize DM patterns across diverse cancer types. PanDM uses the hidden correlations in the combined dataset to improve statistical power through joint modeling. PanDM takes summary statistics from separate analyses as input and performs methylation site clustering, differential methylation detection, and pan-cancer pattern discovery. We demonstrate the favorable performance of PanDM using simulation data. We apply our model to 12 cancer methylome data collected from The Cancer Genome Atlas (TCGA) project. We further conduct ontology- and pathway-enrichment analyses to gain new biological insights into the pan-cancer DM patterns learned by PanDM.ResultsPanDM outperforms two types of separate analyses in the power of DM calling in the simulation study. Application of PanDM to TCGA data reveals 37 pan-cancer DM patterns in the 12 cancer methylomes, including both common and cancer-type-specific patterns. These 37 patterns are in turn used to group cancer types. Functional ontology and biological pathways enriched in the non-common patterns not only underpin the cancer-type-specific etiology and pathogenesis but also unveil the common environmental risk factors shared by multiple cancer types. Moreover, we also identify PanDM-specific DM CpG sites that the common strategy fails to detect.ConclusionsPanDM is a powerful tool that provides a systematic way to investigate aberrant methylation patterns across multiple cancer types. Results from real data analyses suggest a novel angle for us to understand the common and specific DM patterns in different cancers. Moreover, as PanDM works on the summary statistics for each cancer type, the same framework can in principle be applied to pan-cancer analyses of other functional genomic profiles. We implement PanDM as an R package, which is freely available at http://www.sta.cuhk.edu.hk/YWei/PanDM.html .

Dataset Information

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

Publications

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

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