scholarly journals An analysis about heterogeneity among cancers based on the DNA methylation patterns

BMC Cancer ◽  
2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Yang Liu ◽  
Yue Gu ◽  
Mu Su ◽  
Hui Liu ◽  
Shumei Zhang ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Survival Analysis ◽  
Prognostic Markers ◽  
Differential Methylation ◽  
Specific Gene ◽  
Cancer Type ◽  
Methylation Analysis ◽  
Gene Markers ◽  
Significant Difference ◽  
Differential Methylation Analysis

Abstract Background It is generally believed that DNA methylation, as one of the most important epigenetic modifications, participates in the regulation of gene expression and plays an important role in the development of cancer, and there exits epigenetic heterogeneity among cancers. Therefore, this study tried to screen for reliable prognostic markers for different cancers, providing further explanation for the heterogeneity of cancers, and more targets for clinical transformation studies of cancer from epigenetic perspective. Methods This article discusses the epigenetic heterogeneity of cancer in detail. Firstly, DNA methylation data of seven cancer types were obtained from Illumina Infinium HumanMethylation 450 K platform of TCGA database. Then, differential methylation analysis was performed in the promotor region. Secondly, pivotal gene markers were obtained by constructing the DNA methylation correlation network and the gene interaction network in the KEGG pathway, and 317 marker genes obtained from two networks were integrated as candidate markers for the prognosis model. Finally, we used the univariate and multivariate COX regression models to select specific independent prognostic markers for each cancer, and studied the risk factor of these genes by doing survival analysis. Results First, the cancer type-specific gene markers were obtained by differential methylation analysis and they were found to be involved in different biological functions by enrichment analysis. Moreover, specific and common diagnostic markers for each type of cancer was sorted out and Kaplan-Meier survival analysis showed that there was significant difference in survival between the two risk groups. Conclusions This study screened out reliable prognostic markers for different cancers, providing a further explanation for the heterogeneity of cancer at the DNA methylation level and more targets for clinical conversion studies of cancer.

scholarly journals An analysis about heterogeneity among cancers based on the DNA Methylation Patterns

2019 ◽  
Author(s):  
Yang Liu ◽  
Yue Gu ◽  
Mu Su ◽  
Hui Liu ◽  
Shumei Zhang ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Survival Analysis ◽  
Environmental Factors ◽  
Prognostic Markers ◽  
Cox Regression ◽  
Regulation Of Gene Expression ◽  
Absolute Difference ◽  
Marker Genes ◽  
Gene Markers ◽  
Significant Difference

Abstract Background: The occurrence of cancer is usually the result of a co-effect of genetic and environmental factors. It is generally believed that the main cause of cancer is the accumulation of genetic mutations, and DNA methylation, as one of the epigenetic modifications closely related to environmental factors, participates in the regulation of gene expression and cell differentiation and plays an important role in the development of cancer. Methods: This article discusses the epigenetic heterogeneity of cancer in detail. Firstly DNA methylation data of 7 cancer types were obtained from Illumina Infinium HumanMethylation 450K platform of TCGA database. Diagnostic markers of each cancer were obtained by t-test and absolute difference of DNA differencial methylation analysis. Enrichment analysis of these specific markers indicated that they were involved in different biological functions. Secondly, important gene markers were obtained by constructing the DNA methylation correlation network and the gene interaction network in the KEGG pathway, and 317 marker genes set obtained from two networks were integrated as candidate markers for the prognosis model. The univariate and multivariate COX regression models were used to select specific independent prognostic markers for each cancer, and a risk-score model was constructed to divide patients of each cancer into two groups, highly-risky and lowly-risky groups. Results: Kaplan-Meier survival analysis showed that there was significant difference in survival between the two groups. In the verification set, there was also a difference in survival between the highly and lowly risky groups. Conclusions: This study screened out reliable prognostic markers for different cancers, providing a further explanation for the heterogeneity of cancer at the DNA methylation level and more targets for clinical conversion studies of cancer. Kewords: DNA methylation; cancer; epigenetic heterogeneity; survival analysis

scholarly journals MADA: a web service for analysing DNA methylation array data

2020 ◽  
Vol 21 (S6) ◽  
Author(s):  
Xinyu Hu ◽  
Li Tang ◽  
Linconghua Wang ◽  
Fang-Xiang Wu ◽  
Min Li
Keyword(s):  
Dna Methylation ◽  
Data Analysis ◽  
Web Service ◽  
Differential Methylation ◽  
Methylation Analysis ◽  
Methylation Array ◽  
Array Data ◽  
Dna Methylation Array ◽  
Downstream Analysis ◽  
Differential Methylation Analysis

Abstract Background DNA methylation in the human genome is acknowledged to be widely associated with biological processes and complex diseases. The Illumina Infinium methylation arrays have been approved as one of the most efficient and universal technologies to investigate the whole genome changes of methylation patterns. As methylation arrays may still be the dominant method for detecting methylation in the anticipated future, it is crucial to develop a reliable workflow to analysis methylation array data. Results In this study, we develop a web service MADA for the whole process of methylation arrays data analysis, which includes the steps of a comprehensive differential methylation analysis pipeline: pre-processing (data loading, quality control, data filtering, and normalization), batch effect correction, differential methylation analysis, and downstream analysis. In addition, we provide the visualization of pre-processing, differentially methylated probes or regions, gene ontology, pathway and cluster analysis results. Moreover, a customization function for users to define their own workflow is also provided in MADA. Conclusions With the analysis of two case studies, we have shown that MADA can complete the whole procedure of methylation array data analysis. MADA provides a graphical user interface and enables users with no computational skills and limited bioinformatics background to carry on complicated methylation array data analysis. The web server is available at: http://120.24.94.89:8080/MADA

scholarly journals Empirical Bayes Model Comparisons for Differential Methylation Analysis

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Mingxiang Teng ◽  
Yadong Wang ◽  
Seongho Kim ◽  
Lang Li ◽  
Changyu Shen ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Empirical Bayes ◽  
Differential Methylation ◽  
Methylation Analysis ◽  
Bayes Model ◽  
Tiling Arrays ◽  
Division 1 ◽  
Log Normal ◽  
Methylation Patterns ◽  
Differential Methylation Analysis

A number of empirical Bayes models (each with different statistical distribution assumptions) have now been developed to analyze differential DNA methylation using high-density oligonucleotide tiling arrays. However, it remains unclear which model performs best. For example, for analysis of differentially methylated regions for conservative and functional sequence characteristics (e.g., enrichment of transcription factor-binding sites (TFBSs)), the sensitivity of such analyses, using various empirical Bayes models, remains unclear. In this paper, five empirical Bayes models were constructed, based on either a gamma distribution or a log-normal distribution, for the identification of differential methylated loci and their cell division—(1, 3, and 5) and drug-treatment-(cisplatin) dependent methylation patterns. While differential methylation patterns generated by log-normal models were enriched with numerous TFBSs, we observed almost no TFBS-enriched sequences using gamma assumption models. Statistical and biological results suggest log-normal, rather than gamma, empirical Bayes model distribution to be a highly accurate and precise method for differential methylation microarray analysis. In addition, we presented one of the log-normal models for differential methylation analysis and tested its reproducibility by simulation study. We believe this research to be the first extensive comparison of statistical modeling for the analysis of differential DNA methylation, an important biological phenomenon that precisely regulates gene transcription.

scholarly journals Differential methylation analysis of reduced representation bisulfite sequencing experiments using edgeR

F1000Research ◽  
2018 ◽  
Vol 6 ◽  
pp. 2055 ◽  
Author(s):  
Yunshun Chen ◽  
Bhupinder Pal ◽  
Jane E. Visvader ◽  
Gordon K. Smyth
Keyword(s):  
Dna Methylation ◽  
Bisulfite Sequencing ◽  
Epigenetic Modification ◽  
Differential Methylation ◽  
Design Matrix ◽  
Rna Seq ◽  
Methylation Analysis ◽  
Reduced Representation ◽  
Reduced Representation Bisulfite Sequencing ◽  
Differential Methylation Analysis

Cytosine methylation is an important DNA epigenetic modification. In vertebrates, methylation occurs at CpG sites, which are dinucleotides where a cytosine is immediately followed by a guanine in the DNA sequence from 5' to 3'. When located in the promoter region of a gene, DNA methylation is often associated with transcriptional silencing of the gene. Aberrant DNA methylation is associated with the development of various diseases such as cancer. Bisulfite sequencing (BS-seq) is the current "gold-standard" technology for high-resolution profiling of DNA methylation. Reduced representation bisulfite sequencing (RRBS) is an efficient form of BS-seq that targets CpG-rich DNA regions in order to save sequencing costs. A typical bioinformatics aim is to identify CpGs that are differentially methylated (DM) between experimental conditions. This workflow demonstrates that differential methylation analysis of RRBS data can be conducted using software and methodology originally developed for RNA-seq data. The RNA-seq pipeline is adapted to methylation by adding extra columns to the design matrix to account for read coverage at each CpG, after which the RRBS and RNA-seq pipelines are almost identical. This approach is statistically natural and gives analysts access to a rich collection of analysis tools including generalized linear models, gene set testing and pathway analysis. The article presents a complete start to finish case study analysis of RRBS profiles of different cell populations from the mouse mammary gland using the Bioconductor package edgeR. We show that lineage-committed cells are typically hyper-methylated compared to progenitor cells and this is true on all the autosomes but not the sex chromosomes. We demonstrate a strong negative correlation between methylation of promoter regions and gene expression as measured by RNA-seq for the same cell types, showing that methylation is a regulatory mechanism involved in epithelial linear commitment.

Genome-Wide DNA Methylation Analysis Shows Enrichment of Differential Methylation in “Open Seas” and Enhancers and Reveals Hypomethylation in DNMT3A Mutated Cytogenetically Normal AML (CN-AML)

Blood ◽  
2012 ◽  
Vol 120 (21) ◽  
pp. 653-653 ◽  
Author(s):  
Ying Qu ◽  
Andreas Lennartsson ◽  
Verena I. Gaidzik ◽  
Stefan Deneberg ◽  
Sofia Bengtzén ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Cpg Island ◽  
Cpg Islands ◽  
Differential Methylation ◽  
Methylation Analysis ◽  
Cpg Sites ◽  
Genome Wide ◽  
Genomic Regions ◽  
Methylation Patterns

Abstract Abstract 653 DNA methylation is involved in multiple biologic processes including normal cell differentiation and tumorigenesis. In AML, methylation patterns have been shown to differ significantly from normal hematopoietic cells. Most studies of DNA methylation in AML have previously focused on CpG islands within the promoter of genes, representing only a very small proportion of the DNA methylome. In this study, we performed genome-wide methylation analysis of 62 AML patients with CN-AML and CD34 positive cells from healthy controls by Illumina HumanMethylation450K Array covering 450.000 CpG sites in CpG islands as well as genomic regions far from CpG islands. Differentially methylated CpG sites (DMS) between CN-AML and normal hematopoietic cells were calculated and the most significant enrichment of DMS was found in regions more than 4kb from CpG Islands, in the so called open sea where hypomethylation was the dominant form of aberrant methylation. In contrast, CpG islands were not enriched for DMS and DMS in CpG islands were dominated by hypermethylation. DMS successively further away from CpG islands in CpG island shores (up to 2kb from CpG Island) and shelves (from 2kb to 4kb from Island) showed increasing degree of hypomethylation in AML cells. Among regions defined by their relation to gene structures, CpG dinucleotide located in theoretic enhancers were found to be the most enriched for DMS (Chi χ2<0.0001) with the majority of DMS showing decreased methylation compared to CD34 normal controls. To address the relation to gene expression, GEP (gene expression profiling) by microarray was carried out on 32 of the CN-AML patients. Totally, 339723 CpG sites covering 18879 genes were addressed on both platforms. CpG methylation in CpG islands showed the most pronounced anti-correlation (spearman ρ =-0.4145) with gene expression level, followed by CpG island shores (mean spearman rho for both sides' shore ρ=-0.2350). As transcription factors (TFs) have shown to be crucial for AML development, we especially studied differential methylation of an unbiased selection of 1638 TFs. The most enriched differential methylation between CN-AML and normal CD34 positive cells were found in TFs known to be involved in hematopoiesis and with Wilms tumor protein-1 (WT1), activator protein 1 (AP-1) and runt-related transcription factor 1 (RUNX1) being the most differentially methylated TFs. The differential methylation in WT 1 and RUNX1 was located in intragenic regions which were confirmed by pyro-sequencing. AML cases were characterized with respect to mutations in FLT3, NPM1, IDH1, IDH2 and DNMT3A. Correlation analysis between genome wide methylation patterns and mutational status showed statistically significant hypomethylation of CpG Island (p<0.0001) and to a lesser extent CpG island shores (p<0.001) and the presence of DNMT3A mutations. This links DNMT3A mutations for the first time to a hypomethylated phenotype. Further analyses correlating methylation patterns to other clinical data such as clinical outcome are ongoing. In conclusion, our study revealed that non-CpG island regions and in particular enhancers are the most aberrantly methylated genomic regions in AML and that WT 1 and RUNX1 are the most differentially methylated TFs. Furthermore, our data suggests a hypomethylated phenotype in DNMT3A mutated AML. Disclosures: No relevant conflicts of interest to declare.

scholarly journals THU0024 METHYLATION ANALYSIS OF VITAMIN D SIGNALING PATHWAY GENES IN RHEUMATOID ARTHRITIS PATIENTS

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 225.1-225
Author(s):  
E. Punceviciene ◽  
J. Gaizevska ◽  
R. Sabaliauskaite ◽  
L. Venceviciene ◽  
D. Vitkus ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Dna Methylation ◽  
Vitamin D ◽  
Vitamin D Deficiency ◽  
Methylation Level ◽  
Methylation Pattern ◽  
Healthy Controls ◽  
Methylation Analysis ◽  
Vitamin D Metabolism ◽  
Significant Difference

Background:Vitamin D is known for its immunomodulatory and epigenome interacting effects. Vitamin D deficiency is frequently observed in rheumatoid arthritis (RA) patients compared to healthy controls, is also named as a potential risk factor in RA ethiopatogenesis and may alter DNA methylation of certain genes [1,2]. Still, causality of vitamin D deficiency in RA patients needs to be elucidated.Objectives:The aim of the study was to evaluate relationship between DNA methylation status of vitamin D related genes (VDR,CYP24A1,CYP2R1), miRNA-155 expression, vitamin D level and its association with RA.Methods:CpG islands in promoter region of theVDR,CYP24A1,CYP2R1genes were chosen for DNA methylation analysis by means of pyrosequencing. DNA from blood mononuclear cells of 31 RA patients and 31 age and sex matched healthy controls was assessed for methylation pattern after informed consent was obtained in Vilnius university Hospital Santaros klinikos Centre of Rheumatology. For miRNA analysis quantitative reverse transcription PCR was used. Chemiluminescent microplate immunoassay was used to asses 25(OH)D serum levels.Results:25(OH)D concentrations varied from deficiency (<50 nmol/l), insufficiency (50-75 nmol/l) to normal range (≥75-100 nmol/l) in RA (mean 47.49 nmol/l; SD ± 27.93) and healthy controls (mean 57.38 nmol/l; SD ± 29.93)).CYP24A1methylation level was significantly higher in comparison toVDR(p<0.0001) andCYP2R1(p<0.0001) genes in both groups.CYP24A1hypermethylation was also observed in older subjects (p=0.012). The study demonstrated a significant positive correlation between vitamin D concentration andVDR,CYP2R1genes methylation intensity (r2=0.31, p=0.014; r2=0.25, p=0.042, respectively). However, gene methylation frequency and methylation intensity showed no significant difference between RA patients and healthy controls (VDR– 2.4vs2.6 %,CYP24A1– 16.6vs15.3 %,CYP2R1– 2.6vs2.6 %) (p>0.05). To note, miRNA-155 expression negatively correlated withCYP24A1methylation intensity (r2=-0.43, p=0.009).Conclusion:Our study identified significant associations between theVDRandCYP2R1promoter methylation and vitamin D concentration. However, no significant differences in DNA methylation pattern between RA patients and healthy controls were detected. MiR-155 expression was associated withCYP24A1methylation level, confirming its possible involvement in vitamin D metabolism. The data of our study suggests that epigenetic phenomena are significantly involved in vitamin D metabolism and may have an indirect effect on RA ethiopatogenesis.References:[1]Jeffery LE, et al. Nat Rev Rheumatol. 2016,12.4:201.[2]Fetahu IS et al. Front Physiol. 2014,5:164.Acknowledgments:This project has received funding from the Research Council of Lithuania (LMTLT), agreement No. S-MIP-17-12.Disclosure of Interests:None declared

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