DNA methylation as a universal biomarker

Methylation Biomarkers of Lung Cancer Risk: A Systematic Review and Meta-Analysis

Background: Lung cancer (LC) is the leading cause of cancer deaths worldwide among both men and women, and represents a major public health challenge. DNA methylation (DNAm) has shown potential in identifying individuals at higher risk of LC, but the overall evidence has not been systematically evaluated. This review and meta-analysis aims to evaluate and summarize existing research on the association between blood DNAm levels and LC risk. Methods: Searches were conducted in PubMed, Web of Science, and Scopus for studies published until February 2024, following PRISMA and MOOSE guidelines. Eleven studies met the eligibility criteria. Results: Using a random effects model, our pooled analysis showed a significant association between increased DNAm levels and LC risk (OR 1.24, 95% CI 1.10-1.39; I2 = 93.90%, p = 0.0001). Stratifying the results by study design showed a stronger association in two prospective cohort studies (OR 1.61; 95% CI 1.36-1.90; I2 = 14.42%, p = 0.32), while case-control studies showed a weaker association (OR 1.05; 95% CI 0.99-1.11; I2 = 70.57%, p = 0.0001). Sensitivity analyses indicated that omitting individual studies did not significantly alter the LC risk estimates. Conclusions: These findings suggest that higher blood DNAm levels are associated with an increased risk of LC, especially in long-term cohort studies. Further research is recommended to explore the potential of DNAm as a screening biomarker for LC and to clarify the role of other influencing factors.

Revolutionizing DNA: advanced modification techniques for next-gen nanotechnology

The comprehensive advancement in DNA modification and coupling is driving DNA nanotechnology to new heights, paving the way for groundbreaking innovations in healthcare, materials science, and beyond. The ability to engineer DNA with tailored properties and functionalities underscores its immense potential in creating novel materials and devices. Utilizing a spectrum of techniques-such as amino handles, thiol groups, alkynes, azides, Diels-Alder reactions, hydrazides, and aminooxy functions-enables diverse coupling strategies, including Palladium-Catalyzed Couplings, to construct intricate DNA nanostructures. Further coupling modifications encompass hydrophobic alterations, redox-active moieties, chemical crosslinking agents, and Biotinylation. These modifications significantly broaden DNA's functional repertoire, offering precise control over interactions, structures, and features. By leveraging these advanced techniques, alongside next-generation sequencing (NGS)-based DNA modifications, researchers can design and implement DNA nanostructures with specific capabilities and applications, showcasing DNA's versatility as a programmable biomaterial. Through meticulous design and strategic implementation, DNA nanotechnology achieves unprecedented levels of precision and functionality, ushering in a new era of technological advancements and applications. These advanced DNA modification techniques hold great potential for transformative applications in nanotechnology, paving the way for innovations in drug delivery, diagnostics, and bioengineering.

Considering Biomarkers of Neurodegeneration in Alzheimer's Disease: The Potential of Circulating Cell-Free DNA in Precision Neurology

Neurodegenerative diseases, such as Alzheimer's disease (AD), are a growing public health crisis, exacerbated by an aging global population and the lack of effective early disease-modifying therapies. Early detection of neurodegenerative disorders is critical to delaying symptom onset and mitigating disease progression, but current diagnostic tools often rely on detecting pathology once clinical symptoms have emerged and significant neuronal damage has already occurred. While disease-specific biomarkers, such as amyloid-beta and tau in AD, offer precise insights, they are too limited in scope for broader neurodegeneration screening for these conditions. Conversely, general biomarkers like neurofilament light chain (NfL) provide valuable staging information but lack targeted insights. Circulating cell-free DNA (cfDNA), released during cell death, is emerging as a promising biomarker for early detection. Derived from dying cells, cfDNA can capture both general neurodegenerative signals and disease-specific insights, offering multi-layered genomic and epigenomic information. Though its clinical potential remains under investigation, advances in cfDNA detection sensitivity, standardized protocols, and reference ranges could establish cfDNA as a valuable tool for early screening. cfDNA methylation signatures, in particular, show great promise for identifying tissue-of-origin and disease-specific changes, offering a minimally invasive biomarker that could transform precision neurology. However, further research is required to address technological challenges and validate cfDNA's utility in clinical settings. Here, we review recent work assessing cfDNA as a potential early biomarker in AD. With continued advances, cfDNA could play a pivotal role in shifting care from reactive to proactive, improving diagnostic timelines and patient outcomes.

Network-based hub biomarker discovery for glaucoma

OBJECTIVE

Glaucoma is an optic neuropathy and the leading cause of irreversible blindness worldwide. However, the early detection of glaucoma remains challenging, as chronic forms of glaucoma remain largely asymptomatic until considerable irreversible visual field deficits have ensued. Thus, biomarkers that facilitate early diagnosis and treatment for glaucoma patients with a high risk of progression are pressing.

METHODS AND ANALYSIS

Human disease-biomarker interactions network and human disease-target-drug interactions network were first constructed based on multiomics data. The greedy search algorithm was used to search for the hub biomarkers and drug targets for glaucoma. Genome-wide association studies and epidemiological data from the UK Biobank were used to verify our results. Biological network and functional analysis was conducted to find common network features and pathways.

RESULTS

We identified 10 hub biomarkers/drug targets for the diagnosis, treatment and prognosis for glaucoma. These results were verified by text mining and genomic/epidemiology data. We also predicted the new application of BMP1 and MMP9 to diagnose glaucoma and confirm the theory of hub biomarkers with multiple clinical applications. Further, relevant pivotal pathways for these hub biomolecules were discovered, which may serve as foundations for future biomarker and drug target prediction for glaucoma.

CONCLUSION

We have used a network-based approach to identify hub diagnostic and therapeutic biomarkers for glaucoma and detected relationships between glaucoma and associated diseases. Several hub biomarkers were identified and verified, which may play more important roles in the diagnosis and treatment of glaucoma.

MethylGPT: a foundation model for the DNA methylome

DNA methylation serves as a powerful biomarker for disease diagnosis and biological age assessment. However, current analytical approaches often rely on linear models that cannot capture the complex, context-dependent nature of methylation regulation. Here we present MethylGPT, a transformer-based foundation model trained on 226,555 (154,063 after QC and deduplication) human methylation profiles spanning diverse tissue types from 5,281 datasets, curated 49,156 CpG sites, and 7.6 billion training tokens. MethylGPT learns biologically meaningful representations of CpG sites, capturing both local genomic context and higher-order chromosomal features without external supervision. The model demonstrates robust methylation value prediction (Pearson R=0.929) and maintains stable performance in downstream tasks with up to 70% missing data. Applied to age prediction across multiple tissue types, MethylGPT achieves superior accuracy compared to existing methods. Analysis of the model's attention patterns reveals distinct methylation signatures between young and old samples, with differential enrichment of developmental and aging-associated pathways. When finetuned to mortality and disease prediction across 60 major conditions using 18,859 samples from Generation Scotland, MethylGPT achieves robust predictive performance and enables systematic evaluation of intervention effects on disease risks, demonstrating potential for clinical applications. Our results demonstrate that transformer architectures can effectively model DNA methylation patterns while preserving biological interpretability, suggesting broad utility for epigenetic analysis and clinical applications.

DNA methylation in cardiovascular disease and heart failure: novel prediction models?

BACKGROUND

Cardiovascular diseases (CVD) affect over half a billion people worldwide and are the leading cause of global deaths. In particular, due to population aging and worldwide spreading of risk factors, the prevalence of heart failure (HF) is also increasing. HF accounts for approximately 36% of all CVD-related deaths and stands as the foremost cause of hospitalization. Patients affected by CVD or HF experience a substantial decrease in health-related quality of life compared to healthy subjects or affected by other diffused chronic diseases.

MAIN BODY

For both CVD and HF, prediction models have been developed, which utilize patient data, routine laboratory and further diagnostic tests. While some of these scores are currently used in clinical practice, there still is a need for innovative approaches to optimize CVD and HF prediction and to reduce the impact of these conditions on the global population. Epigenetic biomarkers, particularly DNA methylation (DNAm) changes, offer valuable insight for predicting risk, disease diagnosis and prognosis, and for monitoring treatment. The present work reviews current information relating DNAm, CVD and HF and discusses the use of DNAm in improving clinical risk prediction of CVD and HF as well as that of DNAm age as a proxy for cardiac aging.

CONCLUSION

DNAm biomarkers offer a valuable contribution to improving the accuracy of CV risk models. Many CpG sites have been adopted to develop specific prediction scores for CVD and HF with similar or enhanced performance on the top of existing risk measures. In the near future, integrating data from DNA methylome and other sources and advancements in new machine learning algorithms will help develop more precise and personalized risk prediction methods for CVD and HF.

Liquid biopsy for improving diagnosis and monitoring of CNS lymphomas: A RANO review

BACKGROUND

The utility of liquid biopsies is well documented in several extracranial and intracranial (brain/leptomeningeal metastases, gliomas) tumors.

METHODS

The RANO (Response Assessment in Neuro-Oncology) group has set up a multidisciplinary Task Force to critically review the role of blood and cerebrospinal fluid (CSF)-liquid biopsy in CNS lymphomas, with a main focus on primary central nervous system lymphomas (PCNSL).

RESULTS

Several clinical applications are suggested: diagnosis of PCNSL in critical settings (elderly or frail patients, deep locations, and steroid responsiveness), definition of minimal residual disease, early indication of tumor response or relapse following treatments, and prediction of outcome.

CONCLUSIONS

Thus far, no clinically validated circulating biomarkers for managing both primary and secondary CNS lymphomas exist. There is need of standardization of biofluid collection, choice of analytes, and type of technique to perform the molecular analysis. The various assays should be evaluated through well-organized central testing within clinical trials.

New advances in signal amplification strategies for DNA methylation detection in vitro

5-methylcytosine (5 mC) DNA methylation is a prominent epigenetic modification ubiquitous in the genome. It plays a critical role in the regulation of gene expression, maintenance of genome stability, and disease control. The potential of 5 mC DNA methylation for disease detection, prognostic information, and prediction of response to therapy is enormous. However, the quantification of DNA methylation from clinical samples remains a considerable challenge due to its low abundance (only 1% of total bases). To overcome this challenge, scientists have recently developed various signal amplification strategies to enhance the sensitivity of DNA methylation biosensors. These strategies include isothermal nucleic acid amplification and enzyme-assisted target cycling amplification, among others. This review summarizes the applications, advantages, and limitations of these signal amplification strategies over the past six years (2018-2023). Our goal is to provide new insights into the selection and establishment of DNA methylation analysis. We hope that this review will offer valuable insights to researchers in the field and facilitate further advancements in this area.

BCAT1, IKZF1 and SEPT9: methylated DNA biomarkers for detection of pan-gastrointestinal adenocarcinomas

INTRODUCTION

Methylated circulating tumour DNA (ctDNA) blood tests for BCAT1/IKZF1 (COLVERA) and SEPT9 (Epi proColon) are used to detect colorectal cancer (CRC). However, there are no ctDNA assays approved for other gastrointestinal adenocarcinomas. We aimed to characterize BCAT1, IKZF1 and SEPT9 methylation in different gastrointestinal adenocarcinoma and non-gastrointestinal tumours to determine if these validated CRC biomarkers might be useful for pan-gastrointestinal adenocarcinoma detection.

METHODS

Tissue DNA methylation data from colorectal (COAD, READ), gastroesophageal (ESCA, STAD), pancreatic (PAAD) and cholangiocarcinoma (CHOL) adenocarcinoma cohorts within The Cancer Genome Atlas were used for differential methylation analyses. Clinicodemographic predictors of BCAT1, IKZF1 and SEPT9 methylation, and the selectivity of hypermethylated BCAT1, IKZF1 and SEPT9 for colorectal adenocarcinomas in comparison to other cancers were each explored with beta regression.

RESULTS

Hypermethylated BCAT1, IKZF1 and SEPT9 were each differentially methylated in colorectal and gastroesophageal adenocarcinomas. IKZF1 was differentially methylated in pancreatic adenocarcinoma. Hypermethylated DNA biomarkers BCAT1, IKZF1 and SEPT9 were largely stable across different stages of disease and were highly selective for gastrointestinal adenocarcinomas relative to other cancer types.

DISCUSSION

Existing CRC methylated ctDNA blood tests for BCAT1/IKZF1 and SEPT9 might be usefully repurposed for use in other gastrointestinal adenocarcinomas and warrant further prospective ctDNA studies.

Promising predictive molecular biomarkers for cervical cancer (Review)

Cervical cancer (CC) constitutes a serious public health problem. Vaccination and screening programs have notably reduced the incidence of CC worldwide by >80%; however, the mortality rate in low‑income countries remains high. The staging of CC is a determining factor in therapeutic strategies: The clinical management of early stages of CC includes surgery and/or radiotherapy, whereas radiotherapy and/or concurrent chemotherapy are the recommended therapeutic strategies for locally advanced CC. The histopathological characteristics of tumors can effectively serve as prognostic markers of radiotherapy response; however, the efficacy rate of radiotherapy may significantly differ among cancer patients. Failure of radiotherapy is commonly associated with a higher risk of recurrence, persistence and metastasis; therefore, radioresistance remains the most important and unresolved clinical problem. This condition highlights the importance of precision medicine in searching for possible predictive biomarkers to timely identify patients at risk of treatment response failure and provide tailored therapeutic strategies according to genetic and epigenetic characteristics. The present review aimed to summarize the evidence that supports the role of several proteins, methylation markers and non‑coding RNAs as potential predictive biomarkers for CC.

Identification of Protein Biomarkers for Differentiating Listeria monocytogenes Genetic Lineage III

Listeria monocytogenes is the causative agent of listeriosis, a severe foodborne illness characterized by septicemia, meningitis, encephalitis, abortions, and occasional death in infants and immunocompromised individuals. L. monocytogenes is composed of four genetic lineages (I, II, III, and IV) and fourteen serotypes. The aim of the current study was to identify proteins that can serve as biomarkers for detection of genetic lineage III strains based on simple antibody-based methods. Liquid chromatography (LC) with electrospray ionization tandem mass spectrometry (ESI MS/MS) followed by bioinformatics and computational analysis were performed on three L. monocytogenes strains (NRRL B-33007, NRRL B-33014, and NRRL B-33077), which were used as reference strains for lineages I, II, and III, respectively. Results from ESI MS/MS revealed 42 unique proteins present in NRRL B-33077 and absent in NRRL B-33007 and NRRL B-33014 strains. BLAST analysis of the 42 proteins against a broader panel of >80 sequenced strains from lineages I and II revealed four proteins [TM2 domain-containing protein (NRRL B-33077_2770), DUF3916 domain-containing protein (NRRL B-33077_1897), DNA adenine methylase (NRRL B-33077_1926), and protein RhsA (NRRL B-33077_1129)] that have no homology with any sequenced strains in lineages I and II. The four genes that encode these proteins were expressed in Escherichia coli strain DE3 and purified. Polyclonal antibodies were prepared against purified recombinant proteins. ELISA using the polyclonal antibodies against 12 L. monocytogenes lineage I, II, and III isolates indicated that TM2 protein and DNA adenine methylase (Dam) detected all lineage III strains with no reaction to lineage I and II strains. In conclusion, two proteins including TM2 protein and Dam are potentially useful biomarkers for detection and differentiation of L. monocytogenes lineage III strains in clinical, environmental, and food processing facilities. Furthermore, these results validate the approach of using a combination of proteomics and bioinformatics to identify useful protein biomarkers.

Large-Scale Machine Learning Analysis Reveals DNA Methylation and Gene Expression Response Signatures for Gemcitabine-Treated Pancreatic Cancer

Background: Gemcitabine is a first-line chemotherapy for pancreatic adenocarcinoma (PAAD), but many PAAD patients do not respond to gemcitabine-containing treatments. Being able to predict such nonresponders would hence permit the undelayed administration of more promising treatments while sparing gemcitabine life-threatening side effects for those patients. Unfortunately, the few predictors of PAAD patient response to this drug are weak, none of them exploiting yet the power of machine learning (ML). Methods: Here, we applied ML to predict the response of PAAD patients to gemcitabine from the molecular profiles of their tumors. More concretely, we collected diverse molecular profiles of PAAD patient tumors along with the corresponding clinical data (gemcitabine responses and clinical features) from the Genomic Data Commons resource. From systematically combining 8 tumor profiles with 16 classification algorithms, each of the resulting 128 ML models was evaluated by multiple 10-fold cross-validations. Results: Only 7 of these 128 models were predictive, which underlines the importance of carrying out such a large-scale analysis to avoid missing the most predictive models. These were here random forest using 4 selected mRNAs [0.44 Matthews correlation coefficient (MCC), 0.785 receiver operating characteristic-area under the curve (ROC-AUC)] and XGBoost combining 12 DNA methylation probes (0.32 MCC, 0.697 ROC-AUC). By contrast, the hENT1 marker obtained much worse random-level performance (practically 0 MCC, 0.5 ROC-AUC). Despite not being trained to predict prognosis (overall and progression-free survival), these ML models were also able to anticipate this patient outcome. Conclusions: We release these promising ML models so that they can be evaluated prospectively on other gemcitabine-treated PAAD patients.

OvaPrint-A Cell-free DNA Methylation Liquid Biopsy for the Risk Assessment of High-grade Serous Ovarian Cancer

PURPOSE

High-grade serous ovarian carcinoma (HGSOC) is the most lethal epithelial ovarian cancer (EOC) and is often diagnosed at late stage. In women with a known pelvic mass, surgery followed by pathologic assessment is the most reliable way to diagnose EOC and there are still no effective screening tools in asymptomatic women. In the current study, we developed a cell-free DNA (cfDNA) methylation liquid biopsy for the risk assessment of early-stage HGSOC.

EXPERIMENTAL DESIGN

We performed reduced representation bisulfite sequencing to identify differentially methylated regions (DMR) between HGSOC and normal ovarian and fallopian tube tissue. Next, we performed hybridization probe capture for 1,677 DMRs and constructed a classifier (OvaPrint) on an independent set of cfDNA samples to discriminate HGSOC from benign masses. We also analyzed a series of non-HGSOC EOC, including low-grade and borderline samples to assess the generalizability of OvaPrint. A total of 372 samples (tissue n = 59, plasma n = 313) were analyzed in this study.

RESULTS

OvaPrint achieved a positive predictive value of 95% and a negative predictive value of 88% for discriminating HGSOC from benign masses, surpassing other commercial tests. OvaPrint was less sensitive for non-HGSOC EOC, albeit it may have potential utility for identifying low-grade and borderline tumors with higher malignant potential.

CONCLUSIONS

OvaPrint is a highly sensitive and specific test that can be used for the risk assessment of HGSOC in symptomatic women. Prospective studies are warranted to validate OvaPrint for HGSOC and further develop it for non-HGSOC EOC histotypes in both symptomatic and asymptomatic women with adnexal masses.

Brain Tumor Classification by Methylation Profile

The goal of the methylation classifier in brain tumor classification is to accurately classify tumors based on their methylation profiles. Accurate brain tumor diagnosis is the first step for healthcare professionals to predict tumor prognosis and establish personalized treatment plans for patients. The methylation classifier can be used to perform classification on tumor samples with diagnostic difficulties due to ambiguous histology or mismatch between histopathology and molecular signatures, i.e., not otherwise specified (NOS) cases or not elsewhere classified (NEC) cases, aiding in pathological decision-making. Here, the authors elucidate upon the application of a methylation classifier as a tool to mitigate the inherent complexities associated with the pathological evaluation of brain tumors, even when pathologists are experts in histopathological diagnosis and have access to enough molecular genetic information. Also, it should be emphasized that methylome cannot classify all types of brain tumors, and it often produces erroneous matches even with high matching scores, so, excessive trust is prohibited. The primary issue is the considerable difficulty in obtaining reference data regarding the methylation profile of each type of brain tumor. This challenge is further amplified when dealing with recently identified novel types or subtypes of brain tumors, as such data are not readily accessible through open databases or authors of publications. An additional obstacle arises from the fact that methylation classifiers are primarily research-based, leading to the unavailability of charging patients. It is important to note that the application of methylation classifiers may require specialized laboratory techniques and expertise in DNA methylation analysis.

Degradation of methylation signals in cryopreserved DNA

BACKGROUND

Blood-based DNA methylation has shown great promise as a biomarker in a wide variety of diseases. Studies of DNA methylation in blood often utilize samples which have been cryopreserved for years or even decades. Therefore, changes in DNA methylation associated with long-term cryopreservation can introduce biases or otherwise mislead methylation analyses of cryopreserved DNA. However, previous studies have presented conflicting results with studies reporting hypomethylation, no effect, or even hypermethylation of DNA following long-term cryopreservation. These studies may have been limited by insufficient sample sizes, or by their profiling of methylation only on an aggregate global scale, or profiling of only a few CpGs.

RESULTS

We analyzed two large prospective cohorts: a discovery (n = 126) and a validation (n = 136) cohort, where DNA was cryopreserved for up to four years. In both cohorts there was no detectable change in mean global methylation across increasing storage durations as DNA. However, when analysis was performed on the level of individual CpG methylation both cohorts exhibited a greater number of hypomethylated than hypermethylated CpGs at q-value < 0.05 (4049 hypomethylated but only 50 hypermethylated CpGs in discovery, and 63 hypomethylated but only 6 hypermethylated CpGs in validation). The results were the same even after controlling for age, storage duration as buffy coat prior to DNA extraction, and estimated cell type composition. Furthermore, we find that in both cohorts, CpGs have a greater likelihood to be hypomethylated the closer they are to a CpG island; except for CpGs at the CpG islands themselves which are less likely to be hypomethylated.

CONCLUSION

Cryopreservation of DNA after a few years results in a detectable bias toward hypomethylation at the level of individual CpG methylation, though when analyzed in aggregate there is no detectable change in mean global methylation. Studies profiling methylation in cryopreserved DNA should be mindful of this hypomethylation bias, and more attention should be directed at developing more stable methods of DNA cryopreservation for biomedical research or clinical use.

Implication of DNA methylation during lifestyle mediated weight loss

Over the past 50 years, the number of overweight/obese people increased significantly, making obesity a global public health challenge. Apart from rare monogenic forms, obesity is a multifactorial disease, most likely resulting from a concerted interaction of genetic, epigenetic and environmental factors. Although recent studies opened new avenues in elucidating the complex genetics behind obesity, the biological mechanisms contributing to individual's risk to become obese are not yet fully understood. Non-genetic factors such as eating behaviour or physical activity are strong contributing factors for the onset of obesity. These factors may interact with genetic predispositions most likely via epigenetic mechanisms. Epigenome-wide association studies or methylome-wide association studies are measuring DNA methylation at single CpGs across thousands of genes and capture associations to obesity phenotypes such as BMI. However, they only represent a snapshot in the complex biological network and cannot distinguish between causes and consequences. Intervention studies are therefore a suitable method to control for confounding factors and to avoid possible sources of bias. In particular, intervention studies documenting changes in obesity-associated epigenetic markers during lifestyle driven weight loss, make an important contribution to a better understanding of epigenetic reprogramming in obesity. To investigate the impact of lifestyle in obesity state specific DNA methylation, especially concerning the development of new strategies for prevention and individual therapy, we reviewed 19 most recent human intervention studies. In summary, this review highlights the huge potential of targeted interventions to alter disease-associated epigenetic patterns. However, there is an urgent need for further robust and larger studies to identify the specific DNA methylation biomarkers which influence obesity.

A DNA Methylation-based Epigenetic Signature for the Identification of Lymph Node Metastasis in T1 Colorectal Cancer

OBJECTIVE

This study aimed to unravel the lymph node metastasis (LNM)-related methylated DNA (mDNA) landscape and develop a mDNA signature to identify LNM in patients with T1 colorectal cancers (T1 CRC).

BACKGROUND

Considering the invasiveness of T1 CRC, current guidelines recommend endoscopic resection in patients with LNM-negative, and radical surgical resection only for high-risk LNM-positive patients. Unfortunately, the clinicopathological criteria for LNM risk stratification are imperfect, resulting in frequent misdiagnosis leading to unnecessary radical surgeries and postsurgical complications.

METHODS

We conducted genome-wide methylation profiling of 39 T1 CRC specimens to identify differentially methylated CpGs between LNM-positive and LNM-negative, and performed quantitative pyrosequencing analysis in 235 specimens from 3 independent patient cohorts, including 195 resected tissues (training cohort: n=128, validation cohort: n=67) and 40 pretreatment biopsies.

RESULTS

Using logistic regression analysis, we developed a 9-CpG signature to distinguish LNM-positive versus LNM-negative surgical specimens in the training cohort [area under the curve (AUC)=0.831, 95% confidence interval (CI)=0.755-0.892; P <0.0001], which was subsequently validated in additional surgical specimens (AUC=0.825; 95% CI=0.696-0.955; P =0.003) and pretreatment biopsies (AUC=0.836; 95% CI=0.640-1.000, P =0.0036). This diagnostic power was further improved by combining the signature with conventional clinicopathological features.

CONCLUSIONS

We established a novel epigenetic signature that can robustly identify LNM in surgical specimens and even pretreatment biopsies from patients with T1 CRC. Our signature has strong translational potential to improve the selection of high-risk patients who require radical surgery while sparing others from its complications and expense.

Beadchip technology to detect DNA methylation in mouse faithfully recapitulates whole-genome bisulfite sequencing

Aim: To facilitate wide-scale implementation of Illumina Mouse Methylation BeadChip (MMB) technology, array-based measurement of cytosine methylation was compared with the gold-standard assessment of DNA methylation by whole-genome bisulfite sequencing (WGBS). Methods: DNA methylation across two mouse strains (C57B6 and C3H) and both sexes was assessed using the MMB and compared with previously existing deep-coverage WGBS of mice of the same strain and sex. Results & conclusion: The findings demonstrated that 93.3-99.2% of sites had similar measurements of methylation across technologies and that differentially methylated cytosines and regions identified by each technology overlap and enrich for similar biological functions, suggesting that the MMB faithfully recapitulates the findings of WGBS.

Locus-Specific Bisulfate NGS Sequencing of GSTP1, RNF219, and KIAA1539 Genes in the Total Pool of Cell-Free and Cell-Surface-Bound DNA in Prostate Cancer: A Novel Approach for Prostate Cancer Diagnostics

The locus-specific methylation of three genes (GSTP1, RNF219, and KIAA1539, also known as FAM214B) in the total pool of blood cell-free DNA, including cell-free DNA from plasma and cell-surface-bound DNA, of patients with prostate cancer and healthy donors was studied on the MiSeq platform. Our study found a higher methylation index of loci for total cell-free DNA compared with cell-free DNA. For total cell-free DNA, the methylation of GSTP1 in each of the 11 positions provided a complete separation of cancer patients from healthy donors, whereas for cell-free DNA, there were no positions in the three genes allowing for such separation. Among the prostate cancer patients, the minimum proportion of GSTP1 genes methylated in any of the 17 positions was 12.1% of the total circulated DNA fragments, and the minimum proportion of GSTP1 genes methylated in any of the 11 diagnostically specific positions was 8.4%. Total cell-free DNA was shown to be more convenient and informative as a source of methylated DNA molecules circulating in the blood than cell-free DNA.

Evaluation of cross-platform compatibility of a DNA methylation-based glucocorticoid response biomarker

Background

Identifying blood-based DNA methylation patterns is a minimally invasive way to detect biomarkers in predicting age, characteristics of certain diseases and conditions, as well as responses to immunotherapies. As microarray platforms continue to evolve and increase the scope of CpGs measured, new discoveries based on the most recent platform version and how they compare to available data from the previous versions of the platform are unknown. The neutrophil dexamethasone methylation index (NDMI 850) is a blood-based DNA methylation biomarker built on the Illumina MethylationEPIC (850K) array that measures epigenetic responses to dexamethasone (DEX), a synthetic glucocorticoid often administered for inflammation. Here, we compare the NDMI 850 to one we built using data from the Illumina Methylation 450K (NDMI 450).

Results

The NDMI 450 consisted of 22 loci, 15 of which were present on the NDMI 850. In adult whole blood samples, the linear composite scores from NDMI 450 and NDMI 850 were highly correlated and had equivalent predictive accuracy for detecting DEX exposure among adult glioma patients and non-glioma adult controls. However, the NDMI 450 scores of newborn cord blood were significantly lower than NDMI 850 in samples measured with both assays.

Conclusions

We developed an algorithm that reproduces the DNA methylation glucocorticoid response score using 450K data, increasing the accessibility for researchers to assess this biomarker in archived or publicly available datasets that use the 450K version of the Illumina BeadChip array. However, the NDMI850 and NDMI450 do not give similar results in cord blood, and due to data availability limitations, results from sample types of newborn cord blood should be interpreted with care.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13148-022-01352-1.

Genomic Effect of DNA Methylation on Gene Expression in Colorectal Cancer

The aberrant expression of cancer-related genes can lead to colorectal cancer (CRC) carcinogenesis, and DNA methylation is one of the causes of abnormal expression. Although many studies have been conducted to reveal how DNA methylation affects transcription regulation, the ways in which it modulates gene expression and the regions that significantly affect DNA methylation-mediated gene regulation remain unclear. In this study, we investigated how DNA methylation in specific genomic areas can influence gene expression. Several regression models were constructed for gene expression prediction based on DNA methylation. Among these models, ElasticNet, which had the best performance, was chosen for further analysis. DNA methylation near transcription start sites (TSS), especially from 2 kb upstream to 7 kb downstream of TSS, had an essential regulatory role in gene expression. Moreover, methylation-affected and survival-associated genes were compiled and found to be mainly enriched in immune-related pathways. This study investigated genomic regions in which methylation changes can affect gene expression. In addition, this study proposed that aberrantly expressed genes due to DNA methylation can lead to CRC pathogenesis by the immune system.

Methylation risk scores are associated with a collection of phenotypes within electronic health record systems

Inference of clinical phenotypes is a fundamental task in precision medicine, and has therefore been heavily investigated in recent years in the context of electronic health records (EHR) using a large arsenal of machine learning techniques, as well as in the context of genetics using polygenic risk scores (PRS). In this work, we considered the epigenetic analog of PRS, methylation risk scores (MRS), a linear combination of methylation states. We measured methylation across a large cohort (n = 831) of diverse samples in the UCLA Health biobank, for which both genetic and complete EHR data are available. We constructed MRS for 607 phenotypes spanning diagnoses, clinical lab tests, and medication prescriptions. When added to a baseline set of predictive features, MRS significantly improved the imputation of 139 outcomes, whereas the PRS improved only 22 (median improvement for methylation 10.74%, 141.52%, and 15.46% in medications, labs, and diagnosis codes, respectively, whereas genotypes only improved the labs at a median increase of 18.42%). We added significant MRS to state-of-the-art EHR imputation methods that leverage the entire set of medical records, and found that including MRS as a medical feature in the algorithm significantly improves EHR imputation in 37% of lab tests examined (median R2 increase 47.6%). Finally, we replicated several MRS in multiple external studies of methylation (minimum p-value of 2.72 × 10-7) and replicated 22 of 30 tested MRS internally in two separate cohorts of different ethnicity. Our publicly available results and weights show promise for methylation risk scores as clinical and scientific tools.

Chromatin Methylation Abnormalities in Autosomal Dominant Polycystic Kidney Disease

Autosomal dominant polycystic kidney disease (ADPKD) is the most common inherited kidney disease worldwide and is one of the major causes of end-stage renal disease. PKD1 and PKD2 are two genes that mainly contribute to the development and progression of ADPKD. The precise mechanism is not fully understood. In recent years, epigenetic modification has drawn increasing attention. Chromatin methylation is a very important category of PKD epigenetic changes and mostly involves DNA, histone, and RNA methylation. Genome hypomethylation and regional gene hypermethylation coexist in ADPKD. We found that the genomic DNA of ADPKD kidney tissues showed extensive demethylation by whole-genome bisulphite sequencing, while some regional DNA methylation from body fluids, such as blood and urine, can be used as diagnostic or prognostic biomarkers to predict PKD progression. Histone modifications construct the histone code mediated by histone methyltransferases and contribute to aberrant methylation changes in PKD. Considering the complexity of methylation abnormalities occurring in different regions and genes on the PKD epigenome, more specific therapy aiming to restore to the normal genome should lead to the development of epigenetic treatment.

Alpha-synuclein overexpression induces epigenomic dysregulation of glutamate signaling and locomotor pathways

Parkinson’s disease (PD) is a neurological disorder with complex interindividual etiology that is becoming increasingly prevalent worldwide. Elevated alpha-synuclein levels can increase risk of PD and may influence epigenetic regulation of PD pathways. Here, we report genome-wide DNA methylation and hydroxymethylation alterations associated with overexpression of two PD-linked alpha-synuclein variants (wild-type and A30P) in LUHMES cells differentiated to dopaminergic neurons. Alpha-synuclein altered DNA methylation at thousands of CpGs and DNA hydroxymethylation at hundreds of CpGs in both genotypes, primarily in locomotor behavior and glutamate signaling pathway genes. In some cases, epigenetic changes were associated with transcription. SMITE network analysis incorporating H3K4me1 ChIP-seq to score DNA methylation and hydroxymethylation changes across promoters, enhancers, and gene bodies confirmed epigenetic and transcriptional deregulation of glutamate signaling modules in both genotypes. Our results identify distinct and shared impacts of alpha-synuclein variants on the epigenome, and associate alpha-synuclein with the epigenetic etiology of PD.

Correlation between RASSF1A Methylation in Cell-Free DNA and the Prognosis of Cancer Patients: A Systematic Review and Meta-Analysis

Background

Although the effects of methylation of the Ras association domain-containing protein 1 isoform A (RASSF1A) gene in cell-free DNA on the outcomes of patients with different types of cancer have been reported, the results are inconsistent.

Objective

: To explore the relationships between RASSF1A methylation in cell-free DNA and the outcomes of cancer patients.

Methods

The PubMed, Embase, and Web of Science databases were searched for papers related to this topic on December 8, 2021. The retrieved articles were screened by two independent researchers, following which the methodological quality of the selected studies was evaluated using the Newcastle-Ottawa Scale. Additionally, hazard ratios were calculated, and publication bias of the studies was determined using Egger's test.

Results

Nine relevant publications involving a combined total of 1254 patients with different types of cancer were included in this study. The combined results of the random effects models yielded a hazard ratio of 1.73 (95% confidence interval: 1.31, 2.29; P < 0.001), which suggested there was a significant association between RASSF1A methylation and overall survival, and patients with an RASSF1A methylation status had a significantly increased risk of total death. Moreover, the Egger test result suggested there was no significant publication bias among the included studies.

Conclusions

The methylation of RASSF1A in cell-free DNA in cancer patients was observably associated with an increased risk of poor overall survival.

Toxicological effects of atenolol and venlafaxine on zebrafish tissues: Bioaccumulation, DNA hypomethylation, and molecular mechanism

The beta-blocker atenolol (ATE), and the selective serotonin and norepinephrine reuptake inhibitor, venlafaxine (VEN) are frequently detected in municipal wastewater effluents, but little is known about their ecotoxicological effect on aquatic animals. Herein, ATE and VEN were selected to explore their accumulation and global DNA methylation (GDM) in zebrafish tissues after a 30-day exposure. Molecular dynamics (MD) stimulation was used to investigate the toxic mechanism of ATE and VEN exposure. The results demonstrated that ATE and VEN could reduce the condition factor of zebrafish. The bioaccumulation capacity for ATE and VEN was in the order of liver > gut > gill > brain and liver > gut > brain > gill, respectively. After a 30-day recovery, ATE and VEN could still be detected in zebrafish tissues when exposure concentrations were ≥10 μg/L. Moreover, ATE and VEN induced global DNA hypomethylation in different tissues with a dose-dependent manner and their main target tissues were liver and brain. When the exposure concentrations of ATE and VEN were increased to 100 μg/L, the global DNA hypomethylation of liver and brain were reduced to 27% and 18%, respectively. In the same tissue exposed to the same concentration, DNA hypomethylation induced by VEN was more serious than that of ATE. After a 30-day recovery, the global DNA hypomethylations caused by the two drugs were still persistent, and the recovery of VEN was slower than that of ATE. The MD simulation results showed that both ATE and VEN could reduce the catalytic activity of DNA Methyltransferase 1 (DNMT1), while the effect of VEN on the 3D conformational changes of the DNMT1 domain was more significant, resulting in a lower DNA methylation rate. The current study shed new light on the toxic mechanism and potential adverse impacts of ATE and VEN on zebrafish, providing essential information to the further ecotoxicological risk assessment of these drugs in the aquatic environment.

Experimental development of the epigenomic library construction method to elucidate the epigenetic diversity and causal relationship between epigenome and transcriptome at a single-cell level

The method of single-cell RNA sequencing has been rapidly developed, and numerous experiments have been conducted over the past decade. Their results allow us to recognize various subpopulations and rare cell states in tissues, tumors, and immune systems that are previously unidentified, and guide us to understand fundamental biological processes that determine cell identity based on single-cell gene expression profiles. However, it is still challenging to understand the principle of comprehensive gene regulation that determines the cell fate only with transcriptome, a consequential output of the gene expression program. To elucidate the mechanisms related to the origin and maintenance of comprehensive single-cell transcriptome, we require a corresponding single-cell epigenome, which is a differentiated information of each cell with an identical genome. This review deals with the current development of single-cell epigenomic library construction methods, including multi-omics tools with crucial factors and additional requirements in the future focusing on DNA methylation, chromatin accessibility, and histone post-translational modifications. The study of cellular differentiation and the disease occurrence at a single-cell level has taken the first step with single-cell transcriptome and is now taking the next step with single-cell epigenome.

Locus-Specific Methylation of GSTP1, RNF219, and KIAA1539 Genes with Single Molecule Resolution in Cell-Free DNA from Healthy Donors and Prostate Tumor Patients: Application in Diagnostics

Simple Summary

Prostate cancer (PCa) is the second most commonly diagnosed cancer in men, which is constantly accompanied by benign prostate hyperplasia (BPH). To reach a 100% 5-year survival rate in PCa, which is characteristic for PCa if it is diagnosed in early stages, efficient PCa diagnostics against the background of BPH are demanded. The article describes a liquid biopsy approach to differential PCa diagnostics based on the data on locus-specific methylation of the three genes (GSTP1, RNF219, and KIAA1539) obtained with NGS of cell-free DNA from blood plasma of PCa, BPH, and healthy individuals. We offered a diagnostic approach including the analysis of simultaneous methylation status of two CpGs in one cell-free DNA molecule, allowing the discrimination of all patients with absolute sensitivity and specificity.

Abstract

The locus-specific methylation of three genes (GSTP1, RNF219, and KIAA1539 (also known as FAM214B)) in the blood plasma cell-free DNA (cfDNA) of 20 patients with prostate cancer (PCa), 18 healthy donors (HDs), and 17 patients with benign prostatic hyperplasia (BPH) was studied via the MiSeq platform. The methylation status of two CpGs within the same loci were used as the diagnostic feature for discriminating the patient groups. Many variables had good diagnostic characteristics, e.g., each of the variables GSTP1.C3.C9, GSTP1.C9, and GSTP1.C9.T17 demonstrated an 80% sensitivity at a 100% specificity for PCa patients vs. the others comparison. The analysis of RNF219 gene loci methylation allowed discriminating BPH patients with absolute sensitivity and specificity. The data on the methylation of the genes GSTP1 and RNF219 allowed discriminating PCa patients, as well as HDs, with absolute sensitivity and specificity. Thus, the data on the locus-specific methylation of cfDNA (with single-molecule resolution) combined with a diagnostic approach considering the simultaneous methylation of several CpGs in one locus enabled the discrimination of HD, BPH, and PCa patients.

Novel Methylation Biomarkers for Colorectal Cancer Prognosis

Colorectal cancer (CRC) comprises the third most common cancer worldwide and the second regarding number of deaths. In order to make a correct and early diagnosis to predict metastasis formation, biomarkers are an important tool. Although there are multiple signaling pathways associated with cancer progression, the most recognized are the MAPK pathway, p53 pathway, and TGF-β pathway. These pathways regulate many important functions in the cell, such as cell cycle regulation, proliferation, differentiation, and metastasis formation, among others. Changes in expression in genes belonging to these pathways are drivers of carcinogenesis. Often these expression changes are caused by mutations; however, epigenetic changes, such as DNA methylation, are increasingly acknowledged to play a role in the deregulation of oncogenic genes. This makes DNA methylation changes an interesting biomarkers in cancer. Among the newly identified biomarkers for CRC metastasis INHBB, SMOC2, BDNF, and TBRG4 are included, all of which are highly deregulated by methylation and closely associated with metastasis. The identification of such biomarkers in metastasis of CRC may allow a better treatment and early identification of cancer formation in order to perform better diagnostics and improve the life expectancy.

Detection of MSH2 Gene Methylation in Extramammary Paget's Disease by Methylation-Sensitive High-Resolution Melting Analysis

Background

Extramammary Paget's disease (EMPD) is a rare skin tumor. Hypermethylation in the MSH2 promoter resulting in the downregulation of its protein expression shows a high detection rate in EMPD tumor tissue, which indicates that the methylation of MSH2 may play an important role in the pathogenesis of EMPD.

Objective

This study aims to establish a rapid analysis strategy based on the methylation-sensitive high-resolution melting curve (MS-HRM) to detect the methylation level of the MSH2 promoter.

Methods

With the use of universal methylated human DNA products, we established the MS-HRM standard curve to quantitatively detect the methylation level of the MSH2 promoter. Then, all 57 EMPD tumor DNA samples were analyzed. Pyrosequencing assay was also carried out to test the accuracy and efficacy of MS-HRM. Besides, a total of 54 human normal and other cancerous tissues were included in this study to test the reliability and versatility of the MS-HRM standard curve.

Results

In this study, by using the established MS-HRM, we found that 96.5% (55/57) EMPD tumor samples had varying methylation levels in the MSH2 promoter ranging from 0% to 30%. Then, the methylation data were compared to the results obtained from pyrosequencing, which showed a high correlation between these two techniques by Pearson's correlation (r = 0.9425) and Bland–Altman plots (mean difference = −0.1069) indicating that the methylation levels analyzed by MS-HRM were consistent with DNA pyrosequencing. Furthermore, in 23 normal and 31 other cancerous tissue samples, there were two colorectal cancer (CRC) tissues that tested MSH2 methylation positive (1% and 5%) which confirmed that our established MS-HRM can be widely applied to various types of samples.

Conclusion

MS-HRM standard curve can be used for the detection of the methylation level of MSH2 in EMPD tumor samples and other cancerous tissues potentially, which presents a promising candidate as a quantitative assay to analyze MSH2 promoter methylation in routine pathological procedure.

A review on interplay between small RNAs and oxidative stress in cancer progression

Oxidative stress has been known to be the underlying cause in many instances of cancer development. The new aspect of cancer genesis that has caught the attention of many researchers worldwide is its connection to non-coding RNAs (ncRNAs). ncRNAs may not be protein coding, but in light of the more recent discovery of their wide range of functions, the term 'dark matter of the genome' has been rendered inapplicable. There is an extensive mention of colon cancer as an example, where some of these ncRNAs and their manipulations have seen significant progress. As of now, the focus is on discovering a non-invasive, cost-effective method for diagnosis that is easier to monitor and can be conducted before visible symptoms indicate cancer in a patient, by which time it may already be too late. The concept of liquid biopsies has revolutionized recent diagnostic measures. It has been possible to detect circulating parts of the cancer genome or other biomarkers in the patients' bodily fluids, resulting in the effective management of the disease. This has led these ncRNAs to be considered effective therapeutic targets and extrinsic modifications in several tumor types, proven to be effective as therapy. However, there is a vast scope for further understanding and pertinent application of our acquired knowledge and expanding it in enhancing the utilization of ncRNAs for a better prognosis, quicker diagnosis, and improved management of cancer. This review explores the prognosis of cancer and related mutations by scrutinizing small ncRNAs in the disease.

An Optimized Method for the Construction of a DNA Methylome from Small Quantities of Tissue or Purified DNA from Arabidopsis Embryo

DNA methylation is an important epigenetic mechanism affecting genome structure, gene regulation, and the silencing of transposable elements. Cell- and tissue-specific methylation patterns are critical for differentiation and development in eukaryotes. Dynamic spatiotemporal methylation data in these cells or tissues is, therefore, of great interest. However, the construction of bisulfite sequencing libraries can be challenging if the starting material is limited or the genome size is small, such as in Arabidopsis. Here, we describe detailed methods for the purification of Arabidopsis embryos at all stages, and the construction of comprehensive bisulfite libraries from small quantities of input. We constructed bisulfite libraries by releasing embryos from intact seeds, using a different approach for each developmental stage, and manually picking single-embryo with microcapillaries. From these libraries, reliable Arabidopsis methylome data were collected allowing, on average, 11-fold coverage of the genome using as few as five globular, heart, and torpedo embryos as raw input material without the need for DNA purification step. On the other hand, purified DNA from as few as eight bending torpedo embryos or a single mature embryo is sufficient for library construction when RNase A is treated before DNA extraction. This method can be broadly applied to cells from different tissues or cells from other model organisms. Methylome construction can be achieved using a minimal amount of input material using our method; thereby, it has the potential to increase our understanding of dynamic spatiotemporal methylation patterns in model organisms.

Maternal biological age assessed in early pregnancy is associated with gestational age at birth

Maternal age is an established predictor of preterm birth independent of other recognized risk factors. The use of chronological age makes the assumption that individuals age at a similar rate. Therefore, it does not capture interindividual differences that may exist due to genetic background and environmental exposures. As a result, there is a need to identify biomarkers that more closely index the rate of cellular aging. One potential candidate is biological age (BA) estimated by the DNA methylome. This study investigated whether maternal BA, estimated in either early and/or late pregnancy, predicts gestational age at birth. BA was estimated from a genome-wide DNA methylation platform using the Horvath algorithm. Linear regression methods assessed the relationship between BA and pregnancy outcomes, including gestational age at birth and prenatal perceived stress, in a primary and replication cohort. Prenatal BA estimates from early pregnancy explained variance in gestational age at birth above and beyond the influence of other recognized preterm birth risk factors. Sensitivity analyses indicated that this signal was driven primarily by self-identified African American participants. This predictive relationship was sensitive to small variations in the BA estimation algorithm. Benefits and limitations of using BA in translational research and clinical applications for preterm birth are considered.

Variability of multi-omics profiles in a population-based child cohort

BACKGROUND

Multiple omics technologies are increasingly applied to detect early, subtle molecular responses to environmental stressors for future disease risk prevention. However, there is an urgent need for further evaluation of stability and variability of omics profiles in healthy individuals, especially during childhood.

METHODS

We aimed to estimate intra-, inter-individual and cohort variability of multi-omics profiles (blood DNA methylation, gene expression, miRNA, proteins and serum and urine metabolites) measured 6 months apart in 156 healthy children from five European countries. We further performed a multi-omics network analysis to establish clusters of co-varying omics features and assessed the contribution of key variables (including biological traits and sample collection parameters) to omics variability.

RESULTS

All omics displayed a large range of intra- and inter-individual variability depending on each omics feature, although all presented a highest median intra-individual variability. DNA methylation was the most stable profile (median 37.6% inter-individual variability) while gene expression was the least stable (6.6%). Among the least stable features, we identified 1% cross-omics co-variation between CpGs and metabolites (e.g. glucose and CpGs related to obesity and type 2 diabetes). Explanatory variables, including age and body mass index (BMI), explained up to 9% of serum metabolite variability.

CONCLUSIONS

Methylation and targeted serum metabolomics are the most reliable omics to implement in single time-point measurements in large cross-sectional studies. In the case of metabolomics, sample collection and individual traits (e.g. BMI) are important parameters to control for improved comparability, at the study design or analysis stage. This study will be valuable for the design and interpretation of epidemiological studies that aim to link omics signatures to disease, environmental exposures, or both.

Prognostic Role of RASSF1A, SOX17 and Wif-1 Promoter Methylation Status in Cell-Free DNA of Advanced Gastric Cancer Patients

Epigenetic modification of several genes is a key component in the development of gastric cancer. The methylation status of RASSF1A, SOX17 and Wif-1 genes was evaluated in the cell free circulating DNA of 70 patients with advanced gastric cancer, using methylation-specific PCR. Patients with higher cell-free DNA concentration seem to have lower PFS, than patients with lower cell-free DNA concentration (p = 0.001). RASSF1A was the tumor suppressor gene, most frequently methylated in metastatic gastric cancer patients, followed by SOX17 and Wif-1 (74.3%, 60.0% and 47.1%, respectively). Patients having the SOX17 promoter methylated, had lower progression free survival and overall survival, than unmethylated ones (p < 0.001). Patients having the Wif-1 promoter methylated, had lower progression free survival and overall survival, than unmethylated ones (p = 0.001). Patients having the RASSF1A promoter methylated, had lower progression free survival and overall survival, than unmethylated ones (p = 0.004). Promoter methylation of the examined genes was significantly associated with a decrease in progression free survival and overall survival, comparing to that of patients without methylation. Simultaneous methylation of the above genes was associated with even worse progression free survival and overall survival. The methylation of RASSF1A, SOX-17 and Wif-1 and genes, is a frequent epigenetic event in patients with advanced gastric cancer.

DNA Methylation and Expression Profiles of Whole Blood in Parkinson's Disease

Parkinson’s disease (PD) is the second most common age-related neurodegenerative disease. It is presently only accurately diagnosed at an advanced stage by a series of motor deficits, which are predated by a litany of non-motor symptoms manifesting over years or decades. Aberrant epigenetic modifications exist across a range of diseases and are non-invasively detectable in blood as potential markers of disease. We performed comparative analyses of the methylome and transcriptome in blood from PD patients and matched controls. Our aim was to characterize DNA methylation and gene expression patterns in whole blood from PD patients as a foundational step toward the future goal of identifying molecular markers that could predict, accurately diagnose, or track the progression of PD. We found that differentially expressed genes (DEGs) were involved in the processes of transcription and mitochondrial function and that PD methylation profiles were readily distinguishable from healthy controls, even in whole-blood DNA samples. Differentially methylated regions (DMRs) were functionally varied, including near transcription factor nuclear transcription factor Y subunit alpha (NFYA), receptor tyrosine kinase DDR1, RING finger ubiquitin ligase (RNF5), acetyltransferase AGPAT1, and vault RNA VTRNA2-1. Expression quantitative trait methylation sites were found at long non-coding RNA PAX8-AS1 and transcription regulator ZFP57 among others. Functional epigenetic modules were highlighted by IL18R1, PTPRC, and ITGB2. We identified patterns of altered disease-specific DNA methylation and associated gene expression in whole blood. Our combined analyses extended what we learned from the DEG or DMR results alone. These studies provide a foundation to support the characterization of larger sample cohorts, with the goal of building a thorough, accurate, and non-invasive molecular PD biomarker.

Differential methylation EPIC analysis discloses cisplatin-resistance related hypermethylation and tumor-specific heterogeneity within matched primary and metastatic testicular germ cell tumor patient tissue samples

Testicular germ cell tumors (TGCTs) are among the most common solid malignancies in young-adult men, and currently most mortality is due to metastatic disease and emergence of resistance to cisplatin. There is some evidence that increased methylation is one mechanism behind this resistance, stemming from individual studies, but approaches based on matched primary and metastatic patient samples are lacking. Herein, we provide an EPIC array-based study of matched primary and metastatic TGCT samples. Histology was the major determinant of overall methylation pattern, but some clustering of samples related to response to cisplatin was observed. Further differential analysis of patients with the same histological subtype (embryonal carcinoma) disclosed a remarkable increase in net methylation levels (at both promoter and CpG site level) in the patient with cisplatin-resistant disease and poor outcome compared to the patient with complete response to chemotherapy. This further confirms the recent results of another study performed on isogenic clones of sensitive and resistant TGCT cell lines. Differentially methylated promoters among groups of samples were mostly not shared, disclosing heterogeneity in patient tissue samples. Finally, gene ontology analysis of cisplatin-resistant samples indicated enrichment of differentially hypermethylated promoters on pathways related to regulation of immune microenvironment, and enrichment of differentially hypomethylated promoters on pathways related to DNA/chromatin binding and regulation. This data supports not only the use of hypomethylating agents for targeting cisplatin-resistant disease, but also their use in combination with immunotherapies and chromatin remodelers.

Circulating Cell-Free DNA Methylation Profiles in the Early Detection of Ovarian Cancer: A Scoping Review of the Literature

Epithelial ovarian cancer is the most lethal gynecologic malignancy and has few reliable non-invasive tests for early detection or diagnosis. Recent advances in genomic techniques have bolstered the utility of cell-free DNA (cfDNA) evaluation from peripheral blood as a viable cancer biomarker. For multiple reasons, comparing alterations in DNA methylation is particularly advantageous over other molecular assays. We performed a literature review for studies exploring cfDNA methylation in serum and plasma for the early diagnosis of ovarian cancer. The data suggest that serum/plasma cfDNA methylation tests have strong diagnostic accuracies for ovarian cancer (median 85%, range 40-91%). Moreover, there is improved diagnostic performance if multiple genes are used and if the assays are designed to compare detection of ovarian cancer with benign pelvic masses. We further highlight the vast array of possible gene targets and techniques, and a need to include more earlier-stage ovarian cancer samples in test development. Overall, we show the promise of cfDNA methylation analysis in the development of a viable diagnostic biomarker for ovarian cancer.

Ultra performance liquid chromatography-tandem mass spectrometry assay for the quantification of RNA and DNA methylation

Previous studies have reported that nucleic acid methylation is a critical element in cardiovascular disease, and most studies mainly focused on sequencing and biochemical research. Here we developed an Ultra performance liquid chromatography-tandem mass spectrometry (UPLC-MS/ MS) method for the quantification analysis of the dissociative epigenetic modified nucleosides (5mdC, 5mrC, m⁶A) in Myocardial Infarction (MI) SD rats from different periods (1 week, 4 weeks, 8 weeks) after the surgery. The samples for analysis were obtained from heart tissue and blood of the rats. All the quantification results are compared with the sham-operated group. Total RNA and DNA were isolated by enzymatic hydrolytic methods before the UPLC-MS/MS analysis. The statistical analysis demonstrates the dynamic changes of modified nucleosides in MI rats, and it showed good specificity, accuracy, stability and less samples were needed in the method. In this paper, we discovered that the concentration of 5mdC, 5mrC, m⁶A from heart tissue significantly increased at 8 weeks after the surgery. Furthermore, UPLC-MS/MS helps us observe the similar change of the concentration of those 3 methylated biomarkers in peripheral blood after 8 weeks. The result shows that the dynamic process of those 3 methylated biomarkers in peripheral blood is related to the content of methylated biomarkers from the heart tissue. Based on the scientific evidence available, we proved that the methylation of genetic materials in peripheral blood is similar to myocardial infarction tissue. The relation between them indicates that peripheral blood could be a promising alternative to the heart tissue which monitor the level of methylation and MI diagnosis-aided.

Fluorimetric detection of methylated DNA of Sept9 promoter by silver nanoclusters at intrastrand 6C-loop

Methylation of DNA at carbon 5 of cytosines is the most common epigenetic modification of human genome. Due to its critical role in many normal cell processes such as growth and development, any aberrant methylation pattern in a particular locus may lead to abnormal functions and diseases such as cancer. Development of methods to detect methylation state of DNA which may eliminate labor-intensive chemical or enzymatic treatments has received considerable attention in recent years. Herein, we report a DNA methylation detection procedure based on fluorescence turn-on strategy. Target sequence was selected from Sept9 promoter region that has been reported as one of the most frequently methylated sites in colorectal cancer. Probe DNA was designed to be complementary to this sequence with an additional six cytosines in the middle to form an internal loop to host silver nanoclusters. The fluorescence intensity of the synthesized silver nanoclusters with the duplexes of probe-non-methylated target was significantly different from that of probe-methylated target. The fluorescence enhanced with increasing the methylated DNA concentration with a linear relation in the range of 1.0 × 10^-8 M to 5.0 × 10^-7 M with the detection limit of 8.2 × 10^-9 M, and quenched with non-methylated ones. The method was very specific in the presence of non-complementary sequences with maximum similarity of 40%. Circular dichroism spectra indicated that silver ions significantly affected the structure of methylated and non-methylated DNA into different extents which could further influence the nanocluster fluorescence. Finally, a method was introduced to meet the concerns in the applicability of the proposed method in real situation.

Cell Types in Environmental Epigenetic Studies: Biological and Epidemiological Frameworks

PURPOSE OF REVIEW

This article introduces the roles of perinatal DNA methylation in human health and disease, highlights the challenges of tissue and cellular heterogeneity to studying DNA methylation, summarizes approaches to overcome these challenges, and offers recommendations in conducting research in environmental epigenetics.

RECENT FINDINGS

Epigenetic modifications are essential for human development and are labile to environmental influences, especially during gestation. Epigenetic dysregulation is also a hallmark of multiple diseases. Environmental epigenetic studies routinely measure DNA methylation in readily available tissues. However, tissues and cell types exhibit specific epigenetic patterning and heterogeneity between samples complicates epigenetic studies. Failure to account for cell-type heterogeneity limits identification of biological mechanisms and biases study results. Tissue-level epigenetic measures represent a convolution of epigenetic signals from individual cell types. Tissue-specific epigenetics is an evolving field and the use of disease-affected target, surrogate, or multiple tissues has inherent trade-offs and affects inference. Likewise, experimental and bioinformatic approaches to accommodate cell-type heterogeneity have varying assumptions and inherent trade-offs that affect inference. The relationships between exposure, disease, tissue-level DNA methylation, cell type-specific DNA methylation, and cell-type heterogeneity must be carefully considered in study design and analysis. Causal diagrams can inform study design and analytic strategies. Properly addressing cell-type heterogeneity limits sources of potential bias, avoids misinterpretation of study results, and allows investigators to distinguish shifts in cell-type proportions from direct changes to cellular epigenetic programming, both of which provide insights into environmental disease etiology and aid development of novel methods for prevention and treatment.

Circulating tumor DNA detection and its application status in gastric cancer: a narrative review

Circulating tumor DNA (ctDNA) is the small genomic fragment released by tumor cells into the circulating system, which carries the gene variation features, such as mutation, insertion, deletion, rearrangement, copy number variation (CNV) and methylation, rendering it an important biomarker. It can be used not only to diagnose certain types of solid tumors, but also to monitor the therapeutic response and explore the minimal residual disease (MRD) and resistant mutation of targeted therapy. Therefore, ctDNA detection may become the preferred non-invasive tumor screening method. For patients who cannot receive further gene detection due to insufficient or restricted sample collection with the defined pathological diagnosis, ctDNA detection can be carried out to determine the gene mutation type, with no need for repeated sampling. Gastric cancer (GC) is a malignancy with extremely high morbidity and mortality, and its genesis and development are the consequence of interactions of multiple factors, including environment, diet, heredity, helicobacter pylori infection, chronic inflammatory infiltration, and precancerous lesion. As the research on GC moves forward, the existing research mainly focuses on genetic and epigenetic changes, including DNA methylation, histone modification, non-coding RNA changes, gene mutation, gene heterozygosity loss and microsatellite instability. This paper aimed to summarize the contents of ctDNA detection, its application status in GC and clinical significance.

Longitudinal data in peripheral blood confirm that PM20D1 is a quantitative trait locus (QTL) for Alzheimer's disease and implicate its dynamic role in disease progression

BACKGROUND

While Alzheimer's disease (AD) remains one of the most challenging diseases to tackle, genome-wide genetic/epigenetic studies reveal many disease-associated risk loci, which sheds new light onto disease heritability, provides novel insights to understand its underlying mechanism and potentially offers easily measurable biomarkers for early diagnosis and intervention.

METHODS

We analyzed whole-genome DNA methylation data collected from peripheral blood in a cohort (n = 649) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and compared the DNA methylation level at baseline among participants diagnosed with AD (n = 87), mild cognitive impairment (MCI, n = 175) and normal controls (n = 162), to identify differentially methylated regions (DMRs). We also leveraged up to 4 years of longitudinal DNA methylation data, sampled at approximately 1 year intervals to model alterations in methylation levels at DMRs to delineate methylation changes associated with aging and disease progression, by linear mixed-effects (LME) modeling for the unchanged diagnosis groups (AD, MCI and control, respectively) and U-shape testing for those with changed diagnosis (converters).

RESULTS

When compared with controls, patients with MCI consistently displayed promoter hypomethylation at methylation QTL (mQTL) gene locus PM20D1. This promoter hypomethylation was even more prominent in patients with mild to moderate AD. This is in stark contrast with previously reported hypermethylation in hippocampal and frontal cortex brain tissues in patients with advanced-stage AD at this locus. From longitudinal data, we show that initial promoter hypomethylation of PM20D1 during MCI and early stage AD is reversed to eventual promoter hypermethylation in late stage AD, which helps to complete a fuller picture of methylation dynamics. We also confirm this observation in an independent cohort from the Religious Orders Study and Memory and Aging Project (ROSMAP) Study using DNA methylation and gene expression data from brain tissues as neuropathological staging (Braak score) advances.

CONCLUSIONS

Our results confirm that PM20D1 is an mQTL in AD and demonstrate that it plays a dynamic role at different stages of the disease. Further in-depth study is thus warranted to fully decipher its role in the evolution of AD and potentially explore its utility as a blood-based biomarker for AD.

Circulating methylated Septin 9 and ring finger protein 180 for noninvasive diagnosis of early gastric cancer

Background

Gastric cancer (GC) has a poor prognosis due to patients often being diagnosed at an advanced stage, when metastasis has already occurred. To improve the 5-year survival rate and reduce the number of cancer-related deaths in patients with GC, noninvasive methods for early detection need to be developed. This study aimed to evaluate the value of circulating methylated Septin 9 (SEPT9) and ring finger protein 180 (RNF180) for the early diagnosis of GC.

Methods

Seventy-four patients with early GC, 99 patients with benign gastric diseases (BGD) (inflammation, polyps, intestinal metaplasia, ulcers, and erosion), and 57 cases with no evidence of disease (NED) were enrolled. Methylated SEPT9 and RNF180 in circulating cell-free DNA in blood samples from each group were detected, and the positivity rates were calculated. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), confidence interval (CI), and area under the curve (AUC) were determined for methylated SEPT9 and RNF180 in relation to early GC.

Results

As a diagnostic target, methylated SEPT9 had a sensitivity of 28.3% (95% CI: 18.5–40.0%), specificity of 94.2% (95% CI: 89.3–97.3%), and AUC value of 0.616 (95% CI: 52.0–71.1%). Methylated RNF180 had a sensitivity of 32.4% (95% CI: 22.0–44.3%), specificity of 89.7% (95% CI: 83.9–94.0%), and AUC value of 0.636 (95% CI: 54.2–73.0%). A combination of the two yielded a sensitivity of 40.5% (95% CI: 29.3–52.6%), specificity of 85.3% (95% CI: 78.7–90.4%), and AUC value of 0.65 (95% CI: 55.7–74.4%).

Conclusions

Methylated SEPT9 and RNF180 could be used as diagnostic biomarkers for early gastric cancer (EGC).

Histone modifications, DNA methylation, and the epigenetic code of alcohol use disorder

Alcohol use disorder (AUD) is a leading cause of morbidity and mortality. Despite AUD's substantial contributions to lost economic productivity and quality of life, there are only a limited number of approved drugs for treatment of AUD in the United States. This chapter will update progress made on the epigenetic basis of AUD, with particular focus on histone post-translational modifications and DNA methylation and how these two epigenetic mechanisms interact to contribute to neuroadaptive processes leading to initiation, maintenance and progression of AUD pathophysiology. We will also evaluate epigenetic therapeutic strategies that have arisen from preclinical models of AUD and epigenetic biomarkers that have been discovered in human populations with AUD.

DNA Methylation Markers in Lung Cancer

Lung cancer is the most common cancer and the leading cause of cancer-related morbidity and mortality worldwide. As early symptoms of lung cancer are minimal and non-specific, many patients are diagnosed at an advanced stage. Despite a concerted effort to diagnose lung cancer early, no biomarkers that can be used for lung cancer screening and prognosis prediction have been established so far. As global DNA demethylation and gene-specific promoter DNA methylation are present in lung cancer, DNA methylation biomarkers have become a major area of research as potential alternative diagnostic methods to detect lung cancer at an early stage. This review summarizes the emerging DNA methylation changes in lung cancer tumorigenesis, focusing on biomarkers for early detection and their potential clinical applications in lung cancer.

Identification of differentially methylated HpaII sites by NGS analysis of HpaII-digested libraries

Differentially methylated regions (DMRs) have been widely explored as epigenetic biomarkers. Here, we developed a novel approach combining methylation-sensitive restriction enzyme (MSRE) and next-generation sequencing (NGS) to identify DMRs between chorionic villi (CV) and maternal blood cells (MBC). During NGS library preparation, adapter-ligated genomic DNA of CV and MBC were digested with the MSRE, HpaII, and PCR-amplified. As unmethylated HpaII sites were cleaved, the resulted library should contain only methylated HpaII sites. By sequencing both HpaII-digested CV and MBC libraries, 9 differentially methylated-HpaII sites on chromosome 21 which exhibited more than 50% methylation increase in CV were identified. These DMRs are epigenetic biomarkers to tell the difference between CV and MBC. Our approach will also be applicable to screen various tissue-specific epigenetic biomarkers.

Tumor DNA hypomethylation of LINE-1 is associated with low tumor grade of breast cancer in Tunisian patients

DNA hypomethylation of long interspersed repetitive DNA retrotransposon (LINE-1) and Alu repeats elements of short interspersed elements family (SINEs) is an early event in carcinogenesis that causes transcriptional activation and leads to chromosomal instability. In the current study, DNA methylation levels of LINE-1 and Alu repeats were analyzed in tumoral tissues of invasive breast cancer in a Tunisian cohort and its association with the clinicopathological features of patients was defined. DNA methylation of LINE-1 and Alu repeats were analyzed using pyrosequencing in 61 invasive breast cancers. Median values observed for DNA methylation of LINE-1 and Alu repeats were considered as the cut-off (59.81 and 18.49%, respectively). The results of the current study demonstrated a positive correlation between DNA methylation levels of LINE-1 and Alu repeats (rho=0.284; P<0.03). DNA hypomethylation of LINE-1 was also indicated to be associated with low grade (P=0.023). To the best of our knowledge, the current study is the first study regarding DNA methylation of LINE-1 and Alu repeats element in breast cancer of the Tunisian population. The results of the current study suggest that, since hypomethylation of LINE-1 is associated with low grade, it could be used as a biomarker for prognosis for patients with breast cancer.