Clinical Epigenetics最新文献

Background: Early screening and detection of hepatocellular carcinoma (HCC) can efficiently improve patient prognosis. We aimed to identify a series of hypermethylated DNA markers and develop a blood-based HCC diagnosis panel containing DNA methylation sites and protein markers with improved sensitivity for early-stage HCC detection.

Results: Overall, 850K methylation arrays were performed using paired tissue DNA samples from 60 HCC patients. Ten candidate hypermethylated CpG sites were selected for further evaluation by quantitative methylation-specific PCR with 60 pairs of tissue samples. Six methylated CpG sites, along with α-fetoprotein (AFP) and des-gamma-carboxyprothrombin (DCP), were assayed in 150 plasma samples. Finally, an HCC diagnosis panel, named HepaClear, was developed in a cohort consisting of 296 plasma samples and validated in an independent cohort consisting of 198 plasma samples. The HepaClear panel, containing 3 hypermethylated CpG sites (cg14263942, cg12701184, and cg14570307) and 2 protein markers (AFP and DCP), yielded a sensitivity of 82.6% and a specificity of 96.2% in the training set and a sensitivity of 84.7% and a specificity of 92.0% in the validation set. The HepaClear panel had higher sensitivity (72.0%) for early-stage HCC than AFP (≥ 20 ng/mL, 48.0%) and DCP (≥ 40 mAU/mL, 62.0%) and detected 67.5% of AFP-negative HCC patients (AFP ≤ 20 ng/mL).

Conclusions: We developed a multimarker HCC detection panel (HepaClear) that shows high sensitivity for early-stage HCC. The HepaClear panel exhibits high potential for HCC screening and diagnosis from an at-risk population.

Background: Therapeutic replacement of pancreatic endocrine β-cells is key to improving hyperglycaemia caused by insulin-dependent diabetes . Whilst the pool of ductal progenitors, which give rise to the endocrine cells, are active during development, neogenesis of islets is repressed in the human adult. Recent human donor studies have demonstrated the role of EZH2 inhibition in surgically isolated exocrine cells showing reactivation of insulin expression and the influence on the H3K27me3 barrier to β-cell regeneration. However, those studies fall short on defining the cell type active in transcriptional reactivation events. This study examines the role of the regenerative capacity of human pancreatic ductal cells when stimulated with pharmacological inhibitors of the EZH2 methyltransferase.

Results: Human pancreatic ductal epithelial cells were stimulated with the EZH2 inhibitors GSK-126, EPZ6438, and triptolide using a 2- and 7-day protocol to determine their influence on the expression of core endocrine development marker NGN3, as well as β-cell markers insulin, MAFA, and PDX1. Chromatin immunoprecipitation studies show a close correspondence of pharmacological EZH2 inhibition with reduced H3K27me3 content of the core genes, NGN3, MAFA and PDX1. Consistent with the reduction of H3K27me3 by pharmacological inhibition of EZH2, we observe measurable immunofluorescence staining of insulin protein and glucose-sensitive insulin response.

Conclusion: The results of this study serve as a proof of concept for a probable source of β-cell induction from pancreatic ductal cells that are capable of influencing insulin expression. Whilst pharmacological inhibition of EZH2 can stimulate secretion of detectable insulin from ductal progenitor cells, further studies are required to address mechanism and the identity of ductal progenitor cell targets to improve likely methods designed to reduce the burden of insulin-dependent diabetes.

Background & aims: The effects of SARS-CoV-2 infection can be more complex and severe in patients with hepatocellular carcinoma (HCC) as compared to other cancers. This is due to several factors, including pre-existing conditions such as viral hepatitis and cirrhosis, which are commonly associated with HCC.

Methods: We conducted an analysis of epigenomics in SARS-CoV-2 infection and HCC patients, and identified common pathogenic mechanisms using weighted gene co-expression network analysis (WGCNA) and other analyses. Hub genes were identified and analyzed using LASSO regression. Additionally, drug candidates and their binding modes to key macromolecular targets of COVID-19 were identified using molecular docking.

Results: The epigenomic analysis of the relationship between SARS-CoV-2 infection and HCC patients revealed that the co-pathogenesis was closely linked to immune response, particularly T cell differentiation, regulation of T cell activation and monocyte differentiation. Further analysis indicated that CD4⁺ T cells and monocytes play essential roles in the immunoreaction triggered by both conditions. The expression levels of hub genes MYLK2, FAM83D, STC2, CCDC112, EPHX4 and MMP1 were strongly correlated with SARS-CoV-2 infection and the prognosis of HCC patients. In our study, mefloquine and thioridazine were identified as potential therapeutic agents for COVID-19 in combined with HCC.

Conclusions: In this research, we conducted an epigenomics analysis to identify common pathogenetic processes between SARS-CoV-2 infection and HCC patients, providing new insights into the pathogenesis and treatment of HCC patients infected with SARS-CoV-2.

Background: Epigenetic alterations are a near-universal feature of human malignancy and have been detected in malignant cells as well as in easily accessible specimens such as blood and urine. These findings offer promising applications in cancer detection, subtyping, and treatment monitoring. However, much of the current evidence is based on findings in retrospective studies and may reflect epigenetic patterns that have already been influenced by the onset of the disease.

Methods: Studying breast cancer, we established genome-scale DNA methylation profiles of prospectively collected buffy coat samples (n = 702) from a case-control study nested within the EPIC-Heidelberg cohort using reduced representation bisulphite sequencing (RRBS).

Results: We observed cancer-specific DNA methylation events in buffy coat samples. Increased DNA methylation in genomic regions associated with SURF6 and REXO1/CTB31O20.3 was linked to the length of time to diagnosis in the prospectively collected buffy coat DNA from individuals who subsequently developed breast cancer. Using machine learning methods, we piloted a DNA methylation-based classifier that predicted case-control status in a held-out validation set with 76.5% accuracy, in some cases up to 15 years before clinical diagnosis of the disease.

Conclusions: Taken together, our findings suggest a model of gradual accumulation of cancer-associated DNA methylation patterns in peripheral blood, which may be detected long before clinical manifestation of cancer. Such changes may provide useful markers for risk stratification and, ultimately, personalized cancer prevention.

Background: DNA methylation changes, frequent early events in cancer, can modulate the binding of transcription factors. RE1-silencing transcription factor (REST) plays a fundamental role in regulating the expression of neuronal genes, and in particular their silencing in non-neuronal tissues, by inducing chromatin modifications, including DNA methylation changes, not only in the proximity of its binding sites but also in the flanking regions. REST has been found aberrantly expressed in brain cancer and other cancer types. In this work, we investigated DNA methylation alterations at REST binding sites and their flanking regions in a brain cancer (pilocytic astrocytoma), two gastrointestinal tumours (colorectal cancer and biliary tract cancer) and a blood cancer (chronic lymphocytic leukemia).

Results: Differential methylation analyses focused on REST binding sites and their flanking regions were conducted between tumour and normal samples from our experimental datasets analysed by Illumina microarrays and the identified alterations were validated using publicly available datasets. We discovered distinct DNA methylation patterns between pilocytic astrocytoma and the other cancer types in agreement with the opposite oncogenic and tumour suppressive role of REST in glioma and non-brain tumours.

Conclusions: Our results suggest that these DNA methylation alterations in cancer may be associated with REST dysfunction opening the enthusiastic possibility to develop novel therapeutic interventions based on the modulation of this master regulator in order to restore the aberrant methylation of its target regions into a normal status.

The majority of these existing prognostic models of head and neck squamous cell carcinoma (HNSCC) have unsatisfactory prediction accuracy since they solely utilize demographic and clinical information. Leveraged by autophagy-related epigenetic biomarkers, we aim to develop a better prognostic prediction model of HNSCC incorporating CpG probes with either main effects or gene-gene interactions. Based on DNA methylation data from three independent cohorts, we applied a 3-D analysis strategy to develop An independently validated auTophagy-related epigenetic prognostic prediction model of HEad and Neck squamous cell carcinomA (ATHENA). Compared to prediction models with only demographic and clinical information, ATHENA has substantially improved discriminative ability, prediction accuracy and more clinical net benefits, and shows robustness in different subpopulations, as well as external populations. Besides, epigenetic score of ATHENA is significantly associated with tumor immune microenvironment, tumor-infiltrating immune cell abundances, immune checkpoints, somatic mutation and immunity-related drugs. Taken together these results, ATHENA has the demonstrated feasibility and utility of predicting HNSCC survival ( http://bigdata.njmu.edu.cn/ATHENA/ ).

Background: MLH1 epimutation is characterised by constitutional monoallelic MLH1 promoter hypermethylation, which can cause colorectal cancer (CRC). Tumour molecular profiles of MLH1 epimutation CRCs were used to classify germline MLH1 promoter variants of uncertain significance and MLH1 methylated early-onset CRCs (EOCRCs). Genome-wide DNA methylation and somatic mutational profiles of tumours from two germline MLH1: c.-11C > T and one MLH1: c.-[28A > G; 7C > T] carriers and three MLH1 methylated EOCRCs (< 45 years) were compared with 38 reference CRCs. Methylation-sensitive droplet digital PCR (ddPCR) was used to detect mosaic MLH1 methylation in blood, normal mucosa and buccal DNA.

Results: Genome-wide methylation-based Consensus Clustering identified four clusters where the tumour methylation profiles of germline MLH1: c.-11C > T carriers and MLH1 methylated EOCRCs clustered with the constitutional MLH1 epimutation CRCs but not with the sporadic MLH1 methylated CRCs. Furthermore, monoallelic MLH1 methylation and APC promoter hypermethylation in tumour were observed in both MLH1 epimutation and germline MLH1: c.-11C > T carriers and MLH1 methylated EOCRCs. Mosaic constitutional MLH1 methylation in MLH1: c.-11C > T carriers and 1 of 3 MLH1 methylated EOCRCs was identified by methylation-sensitive ddPCR.

Conclusions: Mosaic MLH1 epimutation underlies the CRC aetiology in MLH1: c.-11C > T germline carriers and a subset of MLH1 methylated EOCRCs. Tumour profiling and ultra-sensitive ddPCR methylation testing can be used to identify mosaic MLH1 epimutation carriers.

Background: Smoking can lead to the deterioration of lung function and susceptibility to diabetes. Recently, smoking was found to induce DNA methylation (DNAm) changes in some cytosine-phosphate-guanine sites (CpGs). As linear combinations of DNAm levels of aging-related CpGs, five measures of epigenetic age acceleration (EAA) have received extensive attention: HannumEAA, IEAA, PhenoEAA, GrimEAA, and DunedinPACE. It is of interest to explore whether some measures of EAA can mediate the associations of smoking with diabetes-related outcomes and indices of ventilatory lung function.

Methods and results: In this study, we included self-reported smoking variables (smoking status, the number of pack-years, and years since smoking cessation), seven DNAm markers (HannumEAA, IEAA, PhenoEAA, GrimEAA, DNAm-based smoking pack-years, DNAm plasminogen activator inhibitor 1 [PAI-1] levels, and DunedinPACE), and four health outcomes (fasting glucose, hemoglobin A1C, forced expiratory volume in 1.0 s [FEV1], and forced vital capacity [FVC]) from 2474 Taiwan Biobank participants. Mediation analyses were conducted while adjusting for chronological age, sex, body mass index, drinking status, regular exercise status, educational attainment, and five cell-type proportions. We demonstrated that GrimEAA, DNAm-based smoking pack-years, DNAm PAI-1 levels, DunedinPACE, and PhenoEAA mediated smoking associations with diabetes-related outcomes. Moreover, current and former smoking both had an adverse indirect effect on FVC through DNAm PAI-1 levels. For former smokers, a long time since smoking cessation had a positive indirect impact on FVC through GrimEAA and on FEV1 through PhenoEAA.

Conclusions: This is one of the first studies to comprehensively investigate the role of five measures of EAA in mediating the associations of smoking with the health outcomes of an Asian population. The results showed that the second-generation epigenetic clocks (GrimEAA, DunedinPACE, and PhenoEAA) significantly mediated the associations between smoking and diabetes-related outcomes. In contrast, the first-generation epigenetic clocks (HannumEAA and IEAA) did not significantly mediate any associations of smoking variables with the four health outcomes. Cigarette smoking can, directly and indirectly, deteriorate human health through DNAm changes in aging-related CpG sites.

Background: Abnormal DNA methylation is thought to contribute to the onset and progression of systemic sclerosis. Currently, the most comprehensive assay for profiling DNA methylation is whole-genome bisulfite sequencing (WGBS), but its precision depends on read depth and it may be subject to sequencing errors. SOMNiBUS, a method for regional analysis, attempts to overcome some of these limitations. Using SOMNiBUS, we re-analyzed WGBS data previously analyzed using bumphunter, an approach that initially fits single CpG associations, to contrast DNA methylation estimates by both methods.

Methods: Purified CD4+ T lymphocytes of 9 SSc and 4 control females were sequenced using WGBS. We separated the resulting sequencing data into regions with dense CpG data, and differentially methylated regions (DMRs) were inferred with the SOMNiBUS region-level test, adjusted for age. Pathway enrichment analysis was performed with ingenuity pathway analysis (IPA). We compared the results obtained by SOMNiBUS and bumphunter.

Results: Of 8268 CpG regions of ≥ 60 CpGs eligible for analysis with SOMNiBUS, we identified 131 DMRs and 125 differentially methylated genes (DMGs; p-values less than Bonferroni-corrected threshold of 6.05-06 controlling family-wise error rate at 0.05; 1.6% of the regions). In comparison, bumphunter identified 821,929 CpG regions, 599 DMRs (of which none had ≥ 60 CpGs) and 340 DMGs (q-value of 0.05; 0.04% of all regions). The top ranked gene identified by SOMNiBUS was FLT4, a lymphangiogenic orchestrator, and the top ranked gene on chromosome X was CHST7, known to catalyze the sulfation of glycosaminoglycans in the extracellular matrix. The top networks identified by IPA included connective tissue disorders.

Conclusions: SOMNiBUS is a complementary method of analyzing WGBS data that enhances biological insights into SSc and provides novel avenues of investigation into its pathogenesis.

Background: Gingivobuccal complex oral squamous cell carcinoma (GBC-OSCC) is an aggressive malignancy with high mortality often preceded by premalignant lesions, including leukoplakia. Previous studies have reported genomic drivers in OSCC, but much remains to be elucidated about DNA methylation patterns across different stages of oral carcinogenesis.

Results: There is a serious lack of biomarkers and clinical application of biomarkers for early detection and prognosis of gingivobuccal complex cancers. Hence, in search of novel biomarkers, we measured genome-wide DNA methylation in 22 normal oral tissues, 22 leukoplakia, and 74 GBC-OSCC tissue samples. Both leukoplakia and GBC-OSCC had distinct methylation profiles as compared to normal oral tissue samples. Aberrant DNA methylation increases during the different stages of oral carcinogenesis, from premalignant lesions to carcinoma. We identified 846 and 5111 differentially methylated promoters in leukoplakia and GBC-OSCC, respectively, with a sizable fraction shared between the two sets. Further, we identified potential biomarkers from integrative analysis in gingivobuccal complex cancers and validated them in an independent cohort. Integration of genome, epigenome, and transcriptome data revealed candidate genes with gene expression synergistically regulated by copy number and DNA methylation changes. Regularised Cox regression identified 32 genes associated with patient survival. In an independent set of samples, we validated eight genes (FAT1, GLDC, HOXB13, CST7, CYB5A, MLLT11, GHR, LY75) from the integrative analysis and 30 genes from previously published reports. Bisulfite pyrosequencing validated GLDC (P = 0.036), HOXB13 (P < 0.0001) promoter hypermethylation, and FAT1 (P < 0.0001) hypomethylation in GBC-OSCC compared to normal controls.

Conclusions: Our findings identified methylation signatures associated with leukoplakia and gingivobuccal complex cancers. The integrative analysis in GBC-OSCC identified putative biomarkers that enhance existing knowledge of oral carcinogenesis and may potentially help in risk stratification and prognosis of GBC-OSCC.