Comprehensive DNA Methylation Analysis Research Articles

Insight Between the Epigenetics and Transcription Responding of Cotton Hypocotyl Cellular Elongation Under Salt-Alkaline Stress.

Gossypium barbadense is a cultivated cotton not only known for producing superior fiber but also for its salt and alkaline resistance. Here, we used Whole Genome Bisulfite Sequencing (WGBS) technology to map the cytosine methylation of the whole genome of the G. barbadense hypocotyl at single base resolution. The methylation sequencing results showed that the mapping rates of the three samples were 75.32, 77.54, and 77.94%, respectively. In addition, the Bisulfite Sequence (BS) conversion rate was 99.78%. Approximately 71.03, 53.87, and 6.26% of the cytosine were methylated at CG, CHG, and CHH sequence contexts, respectively. A comprehensive analysis of DNA methylation and transcriptome data showed that the methylation level of the promoter region was a positive correlation in the CHH context. Saline-alkaline stress was related to the methylation changes of many genes, transcription factors (TFs) and transposable elements (TEs), respectively. We explored the regulatory mechanism of DNA methylation in response to salt and alkaline stress during cotton hypocotyl elongation. Our data shed light into the relationship of methylation regulation at the germination stage of G. barbadense hypocotyl cell elongation and salt-alkali treatment. The results of this research help understand the early growth regulation mechanism of G. barbadense in response to abiotic stress.

DNA-methylation patterns imply a common cellular origin of virus- and UV-associated Merkel cell carcinoma

Merkel cell carcinoma (MCC) is a neuroendocrine tumor either induced by integration of the Merkel cell polyomavirus into the cell genome or by accumulation of UV-light-associated mutations (VP-MCC and UV-MCC). Whether VP- and UV-MCC have the same or different cellular origins is unclear; with mesenchymal or epidermal origins discussed. DNA-methylation patterns have a proven utility in determining cellular origins of cancers. Therefore, we used this approach to uncover evidence regarding the cell of origin of classical VP- and UV-MCC cell lines, i.e., cell lines with a neuroendocrine growth pattern (n = 9 and n = 4, respectively). Surprisingly, we observed high global similarities in the DNA-methylation of UV- and VP-MCC cell lines. CpGs of lower methylation in VP-MCC cell lines were associated with neuroendocrine marker genes such as SOX2 and INSM1, or linked to binding sites of EZH2 and SUZ12 of the polycomb repressive complex 2, i.e., genes with an impact on carcinogenesis and differentiation of neuroendocrine cancers. Thus, the observed differences appear to be rooted in viral compared to mutation-driven carcinogenesis rather than distinct cells of origin. To test this hypothesis, we used principal component analysis, to compare DNA-methylation data from different epithelial and non-epithelial neuroendocrine cancers and established a scoring model for epithelial and neuroendocrine characteristics. Subsequently, we applied this scoring model to the DNA-methylation data of the VP- and UV-MCC cell lines, revealing that both clearly scored as epithelial cancers. In summary, our comprehensive analysis of DNA-methylation suggests a common epithelial origin of UV- and VP-MCC cell lines.

Genomic methylation variations predict the susceptibility of six chemotherapy related adverse effects and cancer development for Chinese colorectal cancer patients

Colorectal cancer (CRC) remains a major concern with high morbidity and mortality worldwide. Despite the positive influence of chemotherapy on the decline in CRC mortality, the negative influence of chemotherapy-related adverse effects (CRAEs) caused by capecitabine (Cap) remains a challenging problem. DNA methylation alteration plays a pivotal role in gene expression regulation. Here, we aimed to screen reliable and novel biomarkers for CRC diagnosis and CRAE prediction using the advanced Illumina Infinium MethylationEPIC (850 K) BeadChip. Paired tumor and normal tissues from 21 Chinese CRC patients who received Cap-based adjuvant chemotherapy were analyzed. CRC-related methylation was characterized by hypermethylated promoter islands and hypomethylated intragenic openseas; CRAE-related methylation was characterized by hyper- (or hypo-) methylated intragenic (or intergenic) regions. Based on three types of methylation profiles (differentially methylated probes, differentially methylated regions, and gene-function-differentially methylated regions), pathway enrichment analyses revealed that CRC-related genes were significantly enriched in the neuronal system, metabolism of RNA, and extracellular matrix organization; CRAE-related genes were abundantly enriched in pathways controlling regeneration functions and immune response. Finally, based on genes within the mostly related pathways and LASSO logistic regression selection, the integrated-methylation-marker systems developed here demonstrated high discriminative accuracy in both CRC diagnosis (AUROC = 1) and CRAE prediction (AUROC = 0.817–1). In conclusion, we conducted a comprehensive DNA methylation analysis of CRC patients with chemotherapy, which provided new insights into the formation of CRC and CRAEs. Most importantly, our findings identified potentially CRAE-related metabolic pathways and markers, providing a valuable reference for personalized medicine promising better safety.Trail registration:ClinicalTrials.gov,NCT03030508, Registered 25 January 2017,https://www.clinicaltrials.gov/ct2/show/NCT03030508?term=NCT03030508&draw=2&rank=1.

Comprehensive analysis of DNA methylation and gene expression profiles in gestational diabetes mellitus.

Gestational diabetes mellitus (GDM) has a high prevalence during pregnancy. This research aims to identify genes and their pathways related to GDM by combining bioinformatics analysis.The DNA methylation and gene expression profiles data set was obtained from Gene Expression Omnibus. Differentially expressed genes (DEG) and differentially methylated genes (DMG) were screened by R package limma. The methylation-regulated differentially expressed genes (MeDEGs) were obtained by overlapping the DEGs and DMGs. A protein–protein interaction network was constructed using the search tool for searching interacting genes. The results are visualized in Cytoscape. Disease-related miRNAs and pathways were retrieved from Human MicroRNA Disease Database and Comparative Toxic Genome Database. Real-time quantitative PCR further verified the expression changes of these genes in GDM tissues and normal tissues.After overlapping DEGs and DMGs, 138 MeDEGs were identified. These genes were mainly enriched in the biological processes of the “immune response,” “defense response,” and “response to wounding.” Pathway enrichment shows that these genes are involved in “Antigen processing and presentation,” “Graft-versus-host disease,” “Type I diabetes mellitus,” and “Allograft rejection.” Six mRNAs (including superoxide dismutase 2 (SOD2), mitogen-activated protein kinase kinase kinase kinase 3 (MAP4K3), dual specificity phosphatase 5 (DUSP5), p21-activated kinases 2 (PAK2), serine protease inhibitor clade E member 1 (SERPINE1), and protein phosphatase 1 regulatory subunit 15B (PPP1R15B)) were identified as being related to GDM. The results obtained by real-time quantitative PCR are consistent with the results of the microarray analysis.This study identified new types of MeDEGs and discovered their related pathways and functions in GDM, which may be used as molecular targets and diagnostic biomarkers for the precise diagnosis and treatment of GDM.

Comprehensive Methylation Analysis in Pediatric Patients with Acute Myeloid Leukemia —the JCCG Study, JPLSG AML-05-

IntroductionThe genetic heterogeneity of acute myeloid leukemia (AML) makes it difficult to predict an appropriate prognosis of patients with AML. Currently, several gene mutations associated with the pathogenesis of AML have been identified owing to recent advances in massively parallel sequencing technologies. However, despite incorporating these molecular markers, the prognosis of many patients remains uncertain. Aberrant DNA methylation has frequently been observed in myeloid cells of patients with AML. Reportedly, DNA methylation patterns are associated with molecular features. Furthermore, a previous report has demonstrated that the methylation levels of polycomb target genes are associated with the outcome, implying that the definition of DNA methylation patterns of AML is expected to reveal clinical and molecular features and survival in patients with AML. However, there are few reports of DNA methylation in pediatric patients with AML. Thus, we analyzed genome-wide DNA methylation in pediatric patients with AML to reveal its association with clinical features, genetic alterations, and prognostic impact. Besides, our previous study revealed that the high expression of MECOM (also known as EVI1 ) or PRDM16 (also known as MEL1 ) was associated with inferior outcomes in patients with AML. We also investigated the correlation between DNA methylation and gene expression in pediatric patients with AML.MethodsBetween 2006 and 2010, 443 pediatric patients with de novo AML (0-17 years) participated in the Japanese AML-05 trial conducted by the Japanese Pediatric Leukemia/Lymphoma Study Group. Of these, 369 samples were available, and 64 patients were enrolled in this study. The cytogenetic features of 64 patients were as follows: normal karyotype, 33; RUNX1-RUNX1T1 , 8; KMT2A rearrangement, 10; complex karyotype, 6; and other cytogenetics, 7. This cohort included 15 patients with FLT3 -internal tandem duplication (ITD), 8 with CEBPA biallelic mutations, 10 with high MECOM expression, and 30 with high PRDM16 expression. We performed comprehensive DNA methylation analysis using Infinium MethylationEPIC BeadChip (Illumina, Inc., San Diego, CA, USA) in 64 pediatric patients with AML to reveal the correlations among DNA methylation patterns, clinical features, genetic alterations, and prognostic impact in pediatric AML.Results and DiscussionUnsupervised hierarchical clustering based on methylation values of the 567 CpG sites, which showed most variable methylation values between 64 individuals, generated four clusters (clusters 1-4). The number of patients in clusters 1, 2, 3, and 4 was 23, 12, 13, and 16, respectively. Interestingly, these four clusters harbored their molecular features. Cluster 1 included patients with RUNX1/RUNX1T1 , normal karyotype with low PRDM16 expression, or KMT2A rearrangement with low MECOM expression. Cluster 2 included patients with FLT3 -ITD and/or normal karyotype with high PRDM16 expression. Cluster 3 included patients with normal karyotype with CEBPA biallelic mutations and cluster 4 patients with FLT3 -ITD, normal karyotype with high PRDM 16 expression, or KMT2A rearrangement with high MECOM expression. Differences in frequencies of common molecular aberrations among clusters were assessed with the Fisher-Freeman-Halton test. There was an unequal distribution of FLT3 -ITD and high PRDM16 expression (P = 0.003 for both), with concordantly high frequencies in clusters 2 and 4 and low in clusters 1 and 3. Clusters 2 and 4 tended to be correlated with older age, higher white blood cell counts at diagnosis, high relapse rate, and high mortality. These results suggest that DNA methylation can stratify the risk in pediatric AML. Conversely, no independent methylation marker was present to predict the adverse outcome, thereby indicating that an assortment of several methylation sites may be a useful marker for risk stratification.ConclusionWe investigated DNA methylation patterns in pediatric patients with AML and found four distinct clusters. Furthermore, our findings indicated that quantitative determination of DNA methylation percentage at specific CpG sites may be useful for risk stratification in pediatric AML. Considering the need of further analyses to reveal the significance of DNA methylation, we are analyzing the results currently and would like to discuss these data at the annual meeting. DisclosuresNo relevant conflicts of interest to declare.

Endonuclease enrichment TAPS for cost-effective genome-wide base-resolution DNA methylation detection.

Whole genome base-resolution methylome sequencing allows for the most comprehensive analysis of DNA methylation, however, the considerable sequencing cost often limits its applications. While reduced representation sequencing can be an affordable alternative, over 80% of CpGs in the genome are not covered. Building on our recently developed TET-assisted pyridine borane sequencing (TAPS) method, we here described endonuclease enrichment TAPS (eeTAPS), which utilizes dihydrouracil (DHU)-cleaving endonuclease digestion of TAPS-converted DNA to enrich methylated CpG sites (mCpGs). eeTAPS can accurately detect 87% of mCpGs in the mouse genome with a sequencing depth equivalent to 4× whole genome sequencing. In comparison, reduced representation TAPS (rrTAPS) detected less than 4% of mCpGs with 2.5× sequencing depth. Our results demonstrate eeTAPS to be a new strategy for cost-effective genome-wide methylation analysis at single-CpG resolution that can fill the gap between whole-genome and reduced representation sequencing.

Comprehensive analysis of gene expression and DNA methylation for preeclampsia progression.

The purpose of our study is to identify novel preeclampsia (PE)-related methylation genes and uncover the molecular mechanism of PE. All the datasets of gene expression and DNA methylation datasets for PE and normal samples were obtained from the Gene Expression Omnibus database. We first identified the differentially expressed genes (DEGs) and differential methylation genes (DMGs) between PE and normal samples followed by the functional enrichment analysis. Comprehensive analysis of DEGs and DMGs was also conducted for the identification of valuable PE-related biomarkers. The methylation validation was also performed with MassARRAY. Three DNA methylation and three gene expression datasets were incorporated. We obtained 1754 DEGs and 99 DMGs in PE samples with the thresholds of p value <0.05, |Δbeta| > 0.1, and p value <0.05, respectively. Functional analysis of DEGs obtained cell adhesion molecules and leukocyte transendothelial migration. Besides, several valuable biomarkers of PE, including OCA2, CDK2AP1, and ADAM12, were identified through the integrated analysis of gene expression and DNA methylation datasets. Four methylation sites (cg03449867, cg09084244, cg09247979, and cg24194674) were validated, among which cg03449867 and cg09084244 were found to be hypermethylated and the related genes of OCA2 and CDK2AP1 were downregulated in PE compared with normal samples simultaneously. cg24194674 was hypomethylated and its correlated gene ADAM12 was upregulated in PE compared with normal samples simultaneously. Our study should be helpful for the development of potential biomarkers and therapeutic targets for PE.

Epigenetic Characteristics of Human Subtelomeres Vary in Cells Utilizing the Alternative Lengthening of Telomeres (ALT) Pathway.

Most human cancers circumvent senescence by activating a telomere length maintenance mechanism, most commonly involving telomerase activation. A minority of cancers utilize the recombination-based alternative lengthening of telomeres (ALT) pathway. The exact requirements for unleashing normally repressed recombination at telomeres are yet unclear. Epigenetic modifications at telomeric regions were suggested to be pivotal for activating ALT; however, conflicting data exist regarding their exact nature and necessity. To uncover common ALT-positive epigenetic characteristics, we performed a comprehensive analysis of subtelomeric DNA methylation, histone modifications, and TERRA expression in several ALT-positive and ALT-negative cell lines. We found that subtelomeric DNA methylation does not differentiate between the ALT-positive and ALT-negative groups, and most of the analyzed subtelomeres within each group do not share common DNA methylation patterns. Additionally, similar TERRA levels were measured in the ALT-positive and ALT-negative groups, and TERRA levels varied significantly among the members of the ALT-positive group. Subtelomeric H3K4 and H3K9 trimethylation also differed significantly between samples in the ALT-positive group. Our findings do not support a common route by which epigenetic modifications activate telomeric recombination in ALT-positive cells, and thus, different therapeutic approaches will be necessary to overcome ALT-dependent cellular immortalization.

Comprehensive analysis of epigenetic signatures of human transcription control.

Advances in sequencing technologies have enabled exploration of epigenetic and transcriptional profiles at a genome-wide level. The epigenetic and transcriptional landscapes are now available in hundreds of mammalian cell and tissue contexts. Many studies have performed multi-omics analyses using these datasets to enhance our understanding of relationships between epigenetic modifications and transcription regulation. Nevertheless, most studies so far have focused on the promoters/enhancers and transcription start sites, and other features of transcription control including exons, introns and transcription termination remain underexplored. We investigated the interplay between epigenetic modifications and diverse transcription features using the data generated by the Roadmap Epigenomics project. A comprehensive analysis of histone modifications, DNA methylation, and RNA-seq data of thirty-three human cell lines and tissue types allowed us to confirm the generality of previously described relationships, as well as to generate new hypotheses about the interplay between epigenetic modifications and transcription features. Importantly, our analysis included previously under-explored features of transcription control, namely, transcription termination sites, exon-intron boundaries, and the exon inclusion ratio. We have made the analyses freely available to the scientific community at joshiapps.cbu.uib.no/perepigenomics_app/ for easy exploration, validation and hypothesis generation.

Comprehensive Analysis of DNA Methylation and Prediction of Response to NeoadjuvantTherapy in Locally Advanced Rectal Cancer.

Simple SummaryPatients with locally advanced rectal cancer have been treated with chemoradiotherapy followed by surgery, which results in variable therapy response. To date, there is a lack of established predictive biomarkers to distinguish responsive from non-responsive patients. Therefore, patients can be overtreated resulting in unnecessary toxicity, or therapy changes can occur later when the cancer is more aggressive. We used pre-treatment biopsies to evaluate changes in DNA methylation that could predict the chemoradiotherapy response. Data from an external dataset were used to confirm our findings. We identified and validated a classifier, composed of three candidates, that was able to distinguish responders from non-responders. The genomic context of our biomarkers was explored, giving evidence that they play a role in regulating gene expression. The biomarkers herein described can be easily evaluated in the clinical practice and can help to guide rectal cancer patients’ treatment by identifying responders and non-responders.The treatment for locally advanced rectal carcinomas (LARC) is based on neoadjuvant chemoradiotherapy (nCRT) and surgery, which results in pathological complete response (pCR) in up to 30% of patients. Since epigenetic changes may influence response to therapy, we aimed to identify DNA methylation markers predictive of pCR in LARC patients treated with nCRT. We used high-throughput DNA methylation analysis of 32 treatment-naïve LARC biopsies and five normal rectal tissues to explore the predictive value of differentially methylated (DM) CpGs. External validation was carried out with The Cancer Genome Atlas-Rectal Adenocarcinoma (TCGA-READ 99 cases). A classifier based on three-CpGs DM (linked to OBSL1, GPR1, and INSIG1 genes) was able to discriminate pCR from incomplete responders with high sensitivity and specificity. The methylation levels of the selected CpGs confirmed the predictive value of our classifier in 77 LARCs evaluated by bisulfite pyrosequencing. Evaluation of external datasets (TCGA-READ, GSE81006, GSE75546, and GSE39958) reproduced our results. As the three CpGs were mapped near to regulatory elements, we performed an integrative analysis in regions associated with predicted cis-regulatory elements. A positive and inverse correlation between DNA methylation and gene expression was found in two CpGs. We propose a novel predictive tool based on three CpGs potentially useful for pretreatment screening of LARC patients and guide the selection of treatment modality.

A four-methylated LncRNA signature predicts survival of osteosarcoma patients based on machine learning

Risk stratification using prognostic markers facilitates clinical decision-making in treatment of osteosarcoma (OS). In this study, we performed a comprehensive analysis of DNA methylation and transcriptome data from OS patients to establish an optimal methylated lncRNA signature for determining OS patient prognosis. The original OS datasets were downloaded from the the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. Univariate, Lasso, and machine learning algorithm-iterative Lasso Cox regression analyses were used to establish a methylated lncRNA signature that significantly correlated with OS patient survival. The validity of this signature was verified by the Kaplan-Meier curves, Receiver Operating Characteristic (ROC) curves. We established a four-methylated lncRNA signature that can predict OS patient survival (verified in independent cohort [GSE39055]). Kaplan-Meier analysis showed that the signature can distinguish between the survival of high- and low-risk patients. ROC analysis corroborated this finding and revealed that the signature had higher prediction accuracy than known biomarkers. Kaplan-Meier analysis of the clinical subgroup showed that the signature's prognostic ability was independent of clinicopathological factors. The four-methylated lncRNA signature is an independent prognostic biomarker of OS.

Use of the Illumina EPIC methylation array for epigenomic research in the crab-eating macaque (Macaca fascicularis).

BackgroundCommercially available Illumina DNA methylation arrays (HumanMethylation 27K, HumanMethylation450, and MethylationEPIC BeadChip) can be used for comprehensive DNA methylation analyses of not only the human genome but also other mammalian genomes, ranging from those of nonhuman primates to those of rodents. However, practical application of the EPIC array to the crab‐eating macaque has not been reported.MethodsThrough bioinformatic analyses involving cross‐species comparison and consideration of probe performance, we selected array probes that can be reliably used for the crab‐eating macaque genome. A DNA methylation assay using an EPIC array was performed on genomic DNA extracted from the brains of five crab‐eating macaques. The obtained DNA methylation data were compared with a publicly available dataset.ResultsAmong the 865 918 probes in the EPIC array, a total of 183 509 probes (21.2%) were selected as high‐confidence array probes in the crab‐eating macaque. Subsequent comparisons revealed that the data from these probes showed good concordance with other DNA methylation datasets of the crab‐eating macaque.ConclusionThe selected high‐confidence array probes would be useful for high‐throughput DNA methylation assays of the crab‐eating macaque.

Comprehensive spatiotemporal DNA methylation analysis of mouse tissue and organ progression through fetal development to adulthood†.

Comprehensive spatiotemporal DNA methylation analysis of mouse tissue and organ progression through fetal development to adulthood†.

A novel epigenetic signature to predict recurrence-free survival in patients with colon cancer

BackgroundDNA methylation plays an important role in the initiation and progression of colon cancer. The aim of the present study was to perform a comprehensive analysis of DNA methylation and gene expression profiles in order to develop a signature to predict recurrence-free survival (RFS) of colon cancer. MethodsDNA methylation and mRNA expression data were obtained from TCGA database, and were analyzed using an R package MethylMix. Functional enrichment analysis was performed on statistically significant genes identified by MethylMix criteria. The epigenetic signature and nomogram associated with the RFS of colon cancer were established by the Least Absolute Shrinkage and Selection Operator (LASSO) Cox model. Additionally, a joint survival analysis of gene expression and methylation was performed to identify potential prognostic factors for patients with colon cancer. ResultsA total of 179 differentially methylated genes were obtained using MethylMix algorithm. An epigenetic signature for RFS was developed using LASSO. Patients with high-risk had significantly worse RFS than those with low-risk. The signature is independent of clinicopathological variables and indicated better predictive power than other clinicopathological variables in patients with colon cancer. Moreover, joint survival analysis of gene expression and methylation revealed that seven methylated genes could be independent prognostic factors for RFS in colon cancer. ConclusionsOur proposed epigenetic signature presents potential prognostic significance in assessing recurrence risk stratification for patients with colon cancer.

Regulation of Gene Expression through Gut Microbiota-Dependent DNA Methylation in Colonic Epithelial Cells.

A huge number of commensal bacteria inhabit the intestine, which is equipped with the largest immune system in the body. Recently, the regulation of various physiological functions of the host by these bacteria has attracted attention. In this study, the effects of commensal bacteria on gene expression in colonic epithelial cells (CoECs) were investigated with focus on regulation of DNA methylation. RNA sequencing analyses of CoECs from conventional, germ-free, and MyD88-/- mice indicated that, out of the genes affected by commensal bacteria, those downregulated in a MyD88-independent manner were most frequently observed. Furthermore, when the 5' regions of genes downregulated by commensal bacteria in CoECs were captured using a customized array and immunoprecipitated with the anti-methyl cytosine Ab, a certain population of these genes was found to be highly methylated. Comprehensive analysis of DNA methylation in the 5' regions of genes in CoECs from conventional and germ-free mice upon pull-down assay with methyl-CpG-binding domain protein 2 directly demonstrated that DNA methylation in these regions was influenced by commensal bacteria. Actually, commensal bacteria were shown to control expression of Aldh1a1, which encodes a retinoic acid-producing enzyme and plays an important role in the maintenance of intestinal homeostasis via DNA methylation in the overlapping 5' region of Tmem267 and 3110070M22Rik genes in CoECs. Collectively, it can be concluded that regulation of DNA methylation in the 5' regions of a specific population of genes in CoECs acts as a mechanism by which commensal bacteria have physiological effects on the host.

Identification of DNA methylation-driven genes by integrative analysis of DNA methylation and transcriptome data in pancreatic adenocarcinoma.

Pancreatic adenocarcinoma (PAAD) is a painful and fatal disease that undoubtedly remains a health care priority and offers significant therapeutic challenges. The significance of epigenetic modifications, including DNA methylation in tumor development, has gained the attention of researchers. Identifying DNA methylation-driven genes and investigating the mechanisms underlying the tumorigenesis of PAAD are of substantial importance for developing methods of physiological evaluation, treatment planning and prognostic prediction for PAAD. In the present study, a comprehensive analysis of DNA methylation and gene expression data from 188 clinical samples was performed to identify DNA methylation-driven genes in PAAD. In addition, the diagnostic and prognostic value of DNA methylation-driven genes was evaluated using receiver operating characteristic curve, survival and recurrence analyses. A total of 7 DNA methylation-driven genes, namely zinc finger protein 208 (ZNF208), eomesodermin (EOMES), prostaglandin D2 receptor (PTGDR), chromosome 12 open reading frame 42 (C12orf42), integrin subunit α 4 (ITGA4), dedicator of cytokinesis 8 and protein phosphatase 1 regulatory inhibitor subunit 14D (PPP1R14D), were identified. All of them may be used to diagnose PAAD with excellent specificity and sensitivity (area under curve, >0.8). Of the 7 DNA methylation-driven genes, 6 were significantly associated with overall survival (OS) and recurrence-free survival (RFS) P<0.05). Among them, ZNF208, EOMES, PTGDR, C12orf42 and ITGA4 were significantly negatively associated with the OS rate and positively associated with the recurrence rate, while PPP1R14D was significantly positively associated with the OS rate and negatively associated with the recurrence rate. The present study provides novel insight into the epigenetic alterations associated with the occurrence and progression of PAAD, thereby increasing the mechanistic understanding of this disease, offering potential novel molecular biomarkers and contributing to the development of therapeutic targets for PAAD.

Comprehensive analysis of DNA methylation and gene expression in orally tolerized T cells

T cell anergy is known to be a crucial mechanism for various types of immune tolerance, including oral tolerance. The expression of several anergy-specific genes was reportedly up-regulated in anergic T cells, and played important roles in the cells. However, how the genes were up-regulated has not been understood. In this study, we comprehensively analyzed the altered gene expression and DNA methylation status in T cells tolerized by oral antigen in vivo. Our results showed that many genes were significantly up-regulated in the orally tolerized T cells, and most of the genes found in this study have not been reported previously as anergy related genes; for example, ribosomal protein L41 (FC = 3.54E06, p = 3.70E-09: Fisher's exact test; the same applies hereinafter) and CD52 (FC = 2.18E05, p = 3.44E-06). Furthermore, we showed that the DNA methylation statuses of many genes; for example, enoyl-coenzyme A delta isomerase 3 (FC = 3.62E-01, p = 3.01E-02) and leucine zipper protein 1 (FC = 4.80E-01, p = 3.25E-02), including the ones distinctly expressed in tolerized T cells; for example, latexin (FC = 3.85E03, p = 4.06E-02 for expression; FC = 7.75E-01, p = 4.13E-01 for DNA methylation) and small nuclear ribonucleoprotein polypeptide F (FC = 3.12E04, p = 4.46E-04 for expression; FC = 8.56E-01, p = 5.15E-01 for DNA methylation), changed during tolerization, suggesting that the distinct expression of some genes was epigenetically regulated in the tolerized T cells. This study would contribute to providing a novel clue to the fine understanding of the mechanism for T cell anergy and oral tolerance.

DNA methylation-based prognostic biomarkers of acute myeloid leukemia patients.

Acute myeloid leukemia (AML) is a heterogeneous clonal disease that prevents normal myeloid differentiation with its common features. Its incidence increases with age and has a poor prognosis. Studies have shown that DNA methylation and abnormal gene expression are closely related to AML. The methylation array data and mRNA array data are from the Gene Expression Omnibus (GEO) database. Through the GEO data, we identified differential genes from tumors and normal samples. Then we performed Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses on these differential genes. Protein-protein interaction (PPI) network construction and module analysis were performed to screen the highest-scoring modules. Next, we used SurvExpress software to analyze the genes in the highest-scoring module and selected potential prognostic genes by univariate and multivariate Cox analysis. Finally, the three genes screened by SurvExpress software were analyzed using the methylation analysis site MethSurv to explore AML associated methylation biomarkers. We found three genes that can be used as independent prognostic factors for AML. These three genes are the low expression/methylation genes ATP11A and ITGAM, and the high expression/low methylation gene ZNRF2. In this study, we performed a comprehensive analysis of DNA methylation and gene expression to identify key epigenetic genes in AML.

Comprehensive analysis of DNA methylation and gene expression profiles in cholangiocarcinoma

BackgroundThe incidence of cholangiocarcinoma (CCA) has risen in recent years, and it has become a significant health burden worldwide. However, the mechanisms underlying tumorigenesis and progression of this disease remain largely unknown. An increasing number of studies have demonstrated crucial biological functions of epigenetic modifications, especially DNA methylation, in CCA. The present study aimed to identify and analyze methylation-regulated differentially expressed genes (MeDEGs) involved in CCA tumorigenesis and progression by bioinformatics analysis.MethodsThe gene expression profiling dataset (GSE119336) and gene methylation profiling dataset (GSE38860) were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) and differentially methylated genes (DMGs) were identified using the limma packages of R and GEO2R, respectively. The MeDEGs were obtained by overlapping the DEGs and DMGs. Functional enrichment analyses of these genes were then carried out. Protein–protein interaction (PPI) networks were constructed using STRING and visualized in Cytoscape to determine hub genes. Finally, the results were verified based on The Cancer Genome Atlas (TCGA) database.ResultsWe identified 98 hypermethylated, downregulated genes and 93 hypomethylated, upregulated genes after overlapping the DEGs and DMGs. These genes were mainly enriched in the biological processes of the cell cycle, nuclear division, xenobiotic metabolism, drug catabolism, and negative regulation of proteolysis. The top nine hub genes of the PPI network were F2, AHSG, RRM2, AURKB, CCNA2, TOP2A, BIRC5, PLK1, and ASPM. Moreover, the expression and methylation status of the hub genes were significantly altered in TCGA.ConclusionsOur study identified novel methylation-regulated differentially expressed genes (MeDEGs) and explored their related pathways and functions in CCA, which may provide novel insights into a further understanding of methylation-mediated regulatory mechanisms in CCA.

The SMART App: an interactive web application for comprehensive DNA methylation analysis and visualization

BackgroundData mining of The Cancer Genome Atlas (TCGA) data has significantly facilitated cancer genome research and provided unprecedented opportunities for cancer researchers. However, existing web applications for DNA methylation analysis does not adequately address the need of experimental biologists, and many additional functions are often required.ResultsTo facilitate DNA methylation analysis, we present the SMART (Shiny Methylation Analysis Resource Tool) App, a user-friendly and easy-to-use web application for comprehensively analyzing the DNA methylation data of TCGA project. The SMART App integrates multi-omics and clinical data with DNA methylation and provides key interactive and customized functions including CpG visualization, pan-cancer methylation profile, differential methylation analysis, correlation analysis and survival analysis for users to analyze the DNA methylation in diverse cancer types in a multi-dimensional manner.ConclusionThe SMART App serves as a new approach for users, especially wet-bench scientists with no programming background, to analyze the scientific big data and facilitate data mining. The SMART App is available at http://www.bioinfo-zs.com/smartapp.