scholarly journals Contribution of DNA methylation to oncogenesis: Results of a genome scanning approach in multiple human tumors

10.1038/87252 ◽  
2001 ◽  
Vol 27 (S4) ◽  
pp. 80-80
Author(s):  
Christoph Plass ◽  
Michael C. Frühwald ◽  
Laura J. Rush ◽  
Zunyan Dai ◽  
Laura T. Smith ◽  
...  
2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Pinpin Long ◽  
Qiuhong Wang ◽  
Yizhi Zhang ◽  
Xiaoyan Zhu ◽  
Kuai Yu ◽  
...  

Abstract Background Acute coronary syndrome (ACS) is a cardiac emergency with high mortality. Exposure to high copper (Cu) concentration has been linked to ACS. However, whether DNA methylation contributes to the association between Cu and ACS is unclear. Methods We measured methylation level at > 485,000 cytosine-phosphoguanine sites (CpGs) of blood leukocytes using Human Methylation 450 Bead Chip and conducted a genome-wide meta-analysis of plasma Cu in a total of 1243 Chinese individuals. For plasma Cu-related CpGs, we evaluated their associations with the expression of nearby genes as well as major cardiovascular risk factors. Furthermore, we examined their longitudinal associations with incident ACS in the nested case-control study. Results We identified four novel Cu-associated CpGs (cg20995564, cg18608055, cg26470501 and cg05825244) within a 5% false discovery rate (FDR). DNA methylation level of cg18608055, cg26470501, and cg05825244 also showed significant correlations with expressions of SBNO2, BCL3, and EBF4 gene, respectively. Higher DNA methylation level at cg05825244 locus was associated with lower high-density lipoprotein cholesterol level and higher C-reactive protein level. Furthermore, we demonstrated that higher cg05825244 methylation level was associated with increased risk of ACS (odds ratio [OR], 1.23; 95% CI 1.02–1.48; P = 0.03). Conclusions We identified novel DNA methylation alterations associated with plasma Cu in Chinese populations and linked these loci to risk of ACS, providing new insights into the regulation of gene expression by Cu-related DNA methylation and suggesting a role for DNA methylation in the association between copper and ACS.


2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Louis Y. El Khoury ◽  
Shuang Fu ◽  
Ryan A. Hlady ◽  
Ryan T. Wagner ◽  
Liguo Wang ◽  
...  

Abstract Background Despite using prognostic algorithms and standard surveillance guidelines, 17% of patients initially diagnosed with low risk clear cell renal cell carcinoma (ccRCC) ultimately relapse and die of recurrent disease, indicating additional molecular parameters are needed for improved prognosis. Results To address the gap in ccRCC prognostication in the lower risk population, we performed a genome-wide analysis for methylation signatures capable of distinguishing recurrent and non-recurrent ccRCCs within the subgroup classified as ‘low risk’ by the Mayo Clinic Stage, Size, Grade, and Necrosis score (SSIGN 0–3). This approach revealed that recurrent patients have globally hypermethylated tumors and differ in methylation significantly at 5929 CpGs. Differentially methylated CpGs (DMCpGs) were enriched in regulatory regions and genes modulating cell growth and invasion. A subset of DMCpGs stratified low SSIGN groups into high and low risk of recurrence in independent data sets, indicating that DNA methylation enhances the prognostic power of the SSIGN score. Conclusions This study reports a global DNA hypermethylation in tumors of recurrent ccRCC patients. Furthermore, DMCpGs were capable of discriminating between aggressive and less aggressive tumors, in addition to SSIGN score. Therefore, DNA methylation presents itself as a potentially strong biomarker to further improve prognostic power in patients with low risk SSIGN score (0–3).


Diagnostics ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 51
Author(s):  
Nam-Yun Cho ◽  
Ji-Won Park ◽  
Xianyu Wen ◽  
Yun-Joo Shin ◽  
Jun-Kyu Kang ◽  
...  

Cancer tissues have characteristic DNA methylation profiles compared with their corresponding normal tissues that can be utilized for cancer diagnosis with liquid biopsy. Using a genome-scale DNA methylation approach, we sought to identify a panel of DNA methylation markers specific for cell-free DNA (cfDNA) from patients with colorectal cancer (CRC). By comparing DNA methylomes between CRC and normal mucosal tissues or blood leukocytes, we identified eight cancer-specific methylated loci (ADGRB1, ANKRD13, FAM123A, GLI3, PCDHG, PPP1R16B, SLIT3, and TMEM90B) and developed a five-marker panel (FAM123A, GLI3, PPP1R16B, SLIT3, and TMEM90B) that detected CRC in liquid biopsies with a high sensitivity and specificity with a droplet digital MethyLight assay. In a set of cfDNA samples from CRC patients (n = 117) and healthy volunteers (n = 60), a panel of five markers on the platform of the droplet digital MethyLight assay detected stages I–III and stage IV CRCs with sensitivities of 45.9% and 95.7%, respectively, and a specificity of 95.0%. The number of detected markers was correlated with the cancer stage, perineural invasion, lymphatic emboli, and venous invasion. Our five-marker panel with the droplet digital MethyLight assay showed a high sensitivity and specificity for the detection of CRC with cfDNA samples from patients with metastatic CRC.


Genes ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 441
Author(s):  
Fanny Pineau ◽  
Davide Caimmi ◽  
Sylvie Taviaux ◽  
Maurane Reveil ◽  
Laura Brosseau ◽  
...  

Cystic fibrosis (CF) is a chronic genetic disease that mainly affects the respiratory and gastrointestinal systems. No curative treatments are available, but the follow-up in specialized centers has greatly improved the patient life expectancy. Robust biomarkers are required to monitor the disease, guide treatments, stratify patients, and provide outcome measures in clinical trials. In the present study, we outline a strategy to select putative DNA methylation biomarkers of lung disease severity in cystic fibrosis patients. In the discovery step, we selected seven potential biomarkers using a genome-wide DNA methylation dataset that we generated in nasal epithelial samples from the MethylCF cohort. In the replication step, we assessed the same biomarkers using sputum cell samples from the MethylBiomark cohort. Of interest, DNA methylation at the cg11702988 site (ATP11A gene) positively correlated with lung function and BMI, and negatively correlated with lung disease severity, P. aeruginosa chronic infection, and the number of exacerbations. These results were replicated in prospective sputum samples collected at four time points within an 18-month period and longitudinally. To conclude, (i) we identified a DNA methylation biomarker that correlates with CF severity, (ii) we provided a method to easily assess this biomarker, and (iii) we carried out the first longitudinal analysis of DNA methylation in CF patients. This new epigenetic biomarker could be used to stratify CF patients in clinical trials.


2020 ◽  
Vol 14 ◽  
Author(s):  
Mette Soerensen ◽  
Dominika Marzena Hozakowska-Roszkowska ◽  
Marianne Nygaard ◽  
Martin J. Larsen ◽  
Veit Schwämmle ◽  
...  

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Benjamin I. Laufer ◽  
J. Antonio Gomez ◽  
Julia M. Jianu ◽  
Janine M. LaSalle

Abstract Background Down syndrome (DS) is characterized by a genome-wide profile of differential DNA methylation that is skewed towards hypermethylation in most tissues, including brain, and includes pan-tissue differential methylation. The molecular mechanisms involve the overexpression of genes related to DNA methylation on chromosome 21. Here, we stably overexpressed the chromosome 21 gene DNA methyltransferase 3L (DNMT3L) in the human SH-SY5Y neuroblastoma cell line and assayed DNA methylation at over 26 million CpGs by whole genome bisulfite sequencing (WGBS) at three different developmental phases (undifferentiated, differentiating, and differentiated). Results DNMT3L overexpression resulted in global CpG and CpG island hypermethylation as well as thousands of differentially methylated regions (DMRs). The DNMT3L DMRs were skewed towards hypermethylation and mapped to genes involved in neurodevelopment, cellular signaling, and gene regulation. Consensus DNMT3L DMRs showed that cell lines clustered by genotype and then differentiation phase, demonstrating sets of common genes affected across neuronal differentiation. The hypermethylated DNMT3L DMRs from all pairwise comparisons were enriched for regions of bivalent chromatin marked by H3K4me3 as well as differentially methylated sites from previous DS studies of diverse tissues. In contrast, the hypomethylated DNMT3L DMRs from all pairwise comparisons displayed a tissue-specific profile enriched for regions of heterochromatin marked by H3K9me3 during embryonic development. Conclusions Taken together, these results support a mechanism whereby regions of bivalent chromatin that lose H3K4me3 during neuronal differentiation are targeted by excess DNMT3L and become hypermethylated. Overall, these findings demonstrate that DNMT3L overexpression during neurodevelopment recreates a facet of the genome-wide DS DNA methylation signature by targeting known genes and gene clusters that display pan-tissue differential methylation in DS.


2014 ◽  
Vol 22 (S3) ◽  
pp. 1419-1427 ◽  
Author(s):  
Pei-Ching Lin ◽  
Jen-Kou Lin ◽  
Chien-Hsing Lin ◽  
Hung-Hsin Lin ◽  
Shung-Haur Yang ◽  
...  

2019 ◽  
Author(s):  
Zeineb Achour ◽  
Johann Joets ◽  
Martine Leguilloux ◽  
Hélène Sellier ◽  
Jean-Philippe Pichon ◽  
...  

ABSTRACTCharacterizing the molecular processes developed by plants to respond to environmental cues is a major task to better understand local adaptation. DNA methylation is a chromatin mark involved in the transcriptional silencing of transposable elements (TEs) and gene expression regulation. While the molecular bases of DNA methylation regulation are now well described, involvement of DNA methylation in plant response to environmental cues remains poorly characterized. Here, using the TE-rich maize genome and analyzing methylome response to prolonged cold at the chromosome and feature scales, we investigate how genomic architecture affects methylome response to stress in a cold-sensitive genotype. Interestingly, we show that cold stress induces a genome-wide methylation increase through the hypermethylation of TE sequences and centromeres. Our work highlights a cytosine context-specific response of TE methylation that depends on TE types, chromosomal location and proximity to genes. The patterns observed can be explained by the parallel transcriptional activation of multiple DNA methylation pathways that methylate TEs in the various chromatin locations where they reside. Our results open new insights into the possible role of genome-wide DNA methylation in phenotypic response to stress.


2021 ◽  
Author(s):  
Xin Chen ◽  
Qingrun Zhang ◽  
Thierry Chekouo

Abstract Background: DNA methylations in critical regions are highly involved in cancer pathogenesis and drug response. However, to identify causal methylations out of a large number of potential polymorphic DNA methylation sites is challenging. This high-dimensional data brings two obstacles: first, many established statistical models are not scalable to so many features; second, multiple-test and overfitting become serious. To this end, a method to quickly filter candidate sites to narrow down targets for downstream analyses is urgently needed. Methods: BACkPAy is a pre-screening Bayesian approach to detect biological meaningful clusters of potential differential methylation levels with small sample size. BACkPAy prioritizes potentially important biomarkers by the Bayesian false discovery rate (FDR) approach. It filters non-informative sites (i.e. non-differential) with flat methylation pattern levels accross experimental conditions. In this work, we applied BACkPAy to a genome-wide methylation dataset with 3 tissue types and each type contains 3 gastric cancer samples. We also applied LIMMA (Linear Models for Microarray and RNA-Seq Data) to compare its results with what we achieved by BACkPAy. Then, Cox proportional hazards regression models were utilized to visualize prognostics significant markers with The Cancer Genome Atlas (TCGA) data for survival analysis. Results: Using BACkPAy, we identified 8 biological meaningful clusters/groups of differential probes from the DNA methylation dataset. Using TCGA data, we also identified five prognostic genes (i.e. predictive to the progression of gastric cancer) that contain some differential methylation probes, whereas no significant results was identified using the Benjamin-Hochberg FDR in LIMMA. Conclusions: We showed the importance of using BACkPAy for the analysis of DNA methylation data with extremely small sample size in gastric cancer. We revealed that RDH13, CLDN11, TMTC1, UCHL1 and FOXP2 can serve as predictive biomarkers for gastric cancer treatment and the promoter methylation level of these five genes in serum could have prognostic and diagnostic functions in gastric cancer patients.


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