Dedifferentiation-the acquisition of an early developmental state-is a hallmark of cancer. However, the underlying mechanisms that lead to cancer-associated dedifferentiation are not fully understood. Transposable elements (TEs) are becoming increasingly recognised as important regulators of development and disease. The recruitment of TE sequences has played an important role in placental evolution, and TE-derived genes play critical roles in placental development. Although important biological differences exist between tumours and the placenta, the placenta shares certain features with tumours, including the capacity to invade surrounding tissue and modulate the maternal immune response. In this regard, TEs have been implicated in cancer development, and are documented to contribute to oncogenesis through multiple different mechanisms. Moreover, cancers reacquire an epigenetic landscape, which is reflective of early development, and which corresponds to increased phenotypic plasticity, including facilitating the activation of early developmental genes. Many cancers can repurpose developmental genes, including TE-associated genes, which may contribute to pathways involved in invasion and metastasis. Determining whether TE activation is a consequence of broader epigenetic reprogramming or actively contributes to dedifferentiation will be important for understanding cancer biology and may facilitate improvements in cancer diagnosis and treatment.

Epigenetic analysis, especially DNA methylation profiling of plasma cell-free DNA (cfDNA), has recently emerged as a promising clinical tool. Choosing the right analytical method is crucial for working with limited cfDNA, ensuring cost-effectiveness, and supporting clinical translation. While bisulfite-based methods have long been the standard for methylation analysis, enzymatic conversion is a potential alternative. However, their comparative performance for cfDNA remains unclear. In this study, we compared enzymatic (EM-Seq) and bisulfite-based (cfRRBS and cfMethyl-seq) methods. EM-Seq showed higher mapping efficiency, broader genomic coverage, and captured more CpGs at low coverage thresholds, while the bisulfite methods had higher conversion rates, lower costs, and better coverage of functional regions like promoters and exons. The bisulfite-based methods also demonstrated superior reproducibility. Overall, cfRRBS offered the best balance of cost, accuracy, and reproducibility. Our findings fill a key gap in cancer epigenetics, outline the strengths and limitations of each method, and provide a practical guide for selecting cfDNA methylation profiling methods in liquid biopsy applications.

Background:Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related mortality worldwide and continues to have poor survival outcomes, with most patients diagnosed at advanced stages of disease. In New Zealand, NSCLC contributes substantially to cancer inequities, with Māori communities experiencing disproportionately high incidence and mortality rates. Although low-dose computed tomography screening can improve early detection, major limitations remain, including false-positive findings, overdiagnosis, high infrastructure costs, and limited accessibility for rural and underserved populations. Liquid biopsy approaches using circulating tumor DNA (ctDNA), particularly DNA methylation profiling, have emerged as promising, minimally invasive strategies for improving cancer detection, treatment monitoring, and precision oncology. Objective:This study aims to establish integrated genomic and epigenomic predictive and prognostic biomarkers using ctDNA, tumor tissue, and transcriptomic profiling to improve early detection, risk stratification, treatment selection and response prediction, and longitudinal monitoring, with particular emphasis on identifying molecular mechanisms associated with treatment resistance and disease progression. Methods:This prospective observational translational biomarker study is being conducted through the University of Otago and associated respiratory and oncology services in New Zealand. The study will recruit participants with NSCLC (including squamous and nonsquamous subtypes), individuals referred to fast-track lung nodule assessment clinics, and nonmalignant respiratory controls. Serial peripheral blood sampling will be performed in selected participants at predefined clinical follow-up time points to evaluate treatment response and disease progression. The availability of formalin-fixed paraffin-embedded archival tissues will be recorded, but will not be mandatory for enrollment. Genome-scale DNA methylation profiling will be performed using cell-free reduced representation bisulfite sequencing (cfRRBS), while targeted genomic profiling and transcriptomic analyses will be conducted using targeted sequencing panels and RNA sequencing. Integrative bioinformatic analyses will be used to identify molecular biomarkers associated with early-stage disease, advanced disease, treatment response, and therapeutic resistance. Results:Ethics approval for the study has been obtained from the New Zealand Health and Disability Ethics Committee (2022 EXP 12566). This study commenced in 2022, and recruitment and biospecimen collection are ongoing. The study aims to recruit approximately 450 participants, including patients with NSCLC, individuals referred through respiratory diagnostic pathways, and nonmalignant controls. As of July 31, 2026, 205 participants have been recruited, with recruitment continuing until the target sample size is reached. Molecular and data analyses are ongoing, with additional publications expected as the cohort matures. Conclusions:This study will generate one of the first integrated genomic, epigenomic, and transcriptomic liquid biopsy datasets for NSCLC in New Zealand. The findings are expected to support the development of sensitive, accessible, and equitable blood-based biomarkers for NSCLC detection and treatment monitoring while also contributing to improved precision oncology approaches and reducing NSCLC inequities among Māori populations.

Arsenic is known to adversely affect the female reproductive physiology, specifically at environmentally-relevant or accidentally-high doses, mostly by generation of high amounts of reactive oxygen species (ROS). However, the exact molecular events at very low doses of arsenic exposure, leading to uterine dysfunctions haven’t yet been ascertained. This study aims to evaluate the effect and mechanism of action of oral exposure to 0.4 ppm arsenic for 28 days in adult female albino rats. Alterations in the uterine histomorphology, levels of serum estradiol, expression of the estrogen receptor and cell cycle regulating genes were analyzed. The levels of glutathione, catalase and SOD were also evaluated biochemically. The results indicated that rats exposed to 0.4 ppm sodium arsenate showed reduced circulating levels of estradiol, along with degeneration of epithelial cells of uterine lumen and endometrial glands. Concomitantly, downregulation of the estrogen receptor alpha (ERα), cell cycle regulating proteins (cyclin D1, CDK4), PI3K and Akt were also observed. However, no significant change was observed in the levels of the cellular antioxidant components. The findings thereby indicate that arsenic, at very low concentrations, leads to debilitating effects in the rat physiology by modulating estradiol production, estrogen receptor expression and uterine cell proliferation, without involving redox imbalance, eventually leading to reproductive failures.

Circulating tumour cells (CTCs) are key mediators of metastasis and exhibit marked phenotypic plasticity driven by epithelial-to-mesenchymal transition (EMT). Traditional marker-based CTC isolation approaches rely on epithelial marker expression, which may fail to capture mesenchymal and hybrid CTC subpopulations. Hybrid CTCs remain poorly characterised in colorectal cancer (CRC). This study explored transcriptionally defined CTC subpopulations in CRC to provide insight into CTC heterogeneity. Single-cell RNA sequencing (scRNA-seq) was performed on peripheral blood mononuclear cell (PBMC) fractions from four treatment-naive CRC patients (AJCC stages I–IV). Integrated analysis with healthy PBMC controls enabled immune cell exclusion and cell-type annotation. CTCs were identified using epithelial and mesenchymal transcriptional scores together with CD45 negativity. Differential expression, pathway enrichment, and pseudotime analyses were used to characterise epithelial, mesenchymal, and hybrid CTC states. Subpopulations of epithelial, mesenchymal, and hybrid cells were identified in one CRC patient. Hybrid CTCs exhibited distinct transcriptional features and enrichment of pathways related to RNA metabolism, protein trafficking, mitochondrial energy production, DNA repair, and cytoskeletal organisation. Trajectory inference suggested a continuous EMT spectrum, with hybrid CTCs occupying intermediate pseudotime states characterised by progressive loss of epithelial markers and acquisition of mesenchymal-associated features. This study explored CTC heterogeneity in CRC using single-cell transcriptomics and identified epithelial, hybrid, and mesenchymal CTC states within the analysed sample. Hybrid CTCs exhibited distinct transcriptional features, providing preliminary insight into the transcriptional diversity of CRC CTCs. Further studies in larger cohorts are required to validate these findings and determine their clinical relevance.

Transposable elements (TEs) play important roles during development and disease, including through transcriptional activation of TE-associated genes during early human development. Moreover, based on the functional and epigenetic similarities between early development and cancer, TE-associated genes contribute not only to early human development, but frequently contribute to cancer progression. In this study, we hypothesised that recruitment of TE-associated genes during cancer onset occurs through epigenetic regulatory processes, especially involving DNA hypomethylation accompanied by transcriptional upregulation of early developmental pathways, such that, when reactivated inappropriately in later life, they may drive malignancy. It is unknown, however, to what extent DNA methylation changes are critically involved in the transcriptional activation of TE-associated genes. Accordingly, to investigate this we used the RepExpress tool to identify developmentally regulated TE-associated genes in placenta and human embryonic stem cells (hESCs), which we then investigated by targeted deep bisulfite sequencing (TDBS) to determine the methylation status of the identified TE-associated genes in placenta, somatic tissues, and melanoma cell lines. Outcomes suggest that DNA methylation may be one of the regulatory factors underscoring transcriptional activation of TE-associated genes, but that methylation is not necessarily the sole factor involved in regulating the transcriptional activation of TE-associated genes during malignant transformation.

DNA methylation alterations are early and stable hallmarks of cancer and represent promising biomarkers for non-invasive detection using circulating cell-free DNA (cfDNA). However, current computational approaches often model DNA sequence and methylation features separately and struggle to capture complex read-level methylation architecture in heterogeneous, low-signal liquid biopsy data. Here, we present DNAmBERT, a Transformer-based deep learning framework designed to jointly model DNA sequence context and read-level methylation haplotype structure from cfDNA methylation sequencing data. DNAmBERT integrates k-mer-encoded DNA sequences with methylation haplotype tokens using a unified representation and masked language modelling objective, enabling context-aware learning of sequence-epigenetic dependencies through self-attention. We evaluated DNAmBERT across multiple cfDNA methylation platforms (RRBS, cfRRBS, and cfMethyl-seq) and cancer types, including colorectal cancer, lung adenocarcinoma and hepatocellular carcinoma. In binary classification tasks, the model achieved high performance across platforms (AUC up to 0.99-1.00) and outperformed conventional machine learning and existing deep learning approaches. Aggregation of read-level predictions enabled quantitative tumour probability estimation at the sample level. Beyond binary detection, DNAmBERT supported multi-cancer and stage-aware classification, including early-stage disease, with multiclass AUC values up to 0.99. The framework further demonstrated effective cross-cancer transfer learning, maintaining robust performance under limited data availability. These results indicate that integrated sequence-haplotype representation learning provides an accurate and scalable approach for cfDNA-based multi-cancer detection.

In this Journal Club, Chatterjee and Rodger highlight two studies by Guo et al. and Shen et al. that demonstrated how genome-wide DNA methylation profiling enables sensitive detection and classification of tumour-derived cell-free DNA, advancing epigenetic approaches in liquid biopsy for cancer diagnostics.

Prostate cancer is the second-highest cause of cancer-related incidence and the fifth-highest cause of cancer mortality in males. Prostate cancer is a heterogeneous disease with a wide spectrum of clinical behaviour, ranging from indolent to highly aggressive. Molecular approaches, such as genomic testing, can augment existing clinical risk stratifications and tailor management to the individual. Genomic tests that sample biopsy or surgical tissue can provide a molecular risk assessment and identify actionable therapy targets. Liquid biopsy, while still emerging, may provide a non-invasive alternative to tissue tests and enable longitudinal monitoring of tumour status. We first discuss the molecular landscape of prostate cancer, before providing a detailed overview of the molecular approaches available for early detection and prognostication. Furthermore, methodological considerations and barriers towards clinical implementation for these tests are discussed, highlighting areas of future research.

High-dimensional data expands the spatial dimension, leading to increased computational complexity and reduced generalization performance. Microarray data classification, such as diagnosing diseases like cancer, involves complex dimensions due to their genetic and biological information. To address this issue, dimension reduction is essential for these data sets. The main goal of this chapter is to provide a method for dimension reduction and classification of genetic data sets. The proposed approach comprises multiple stages. Initially, various feature ranking methods are combined to improve the robustness and stability of the feature selection process. A hybrid ranking method, which incorporates gene interactions, is integrated with a wrapper method. Subsequently, a support vector machine (SVM) is employed for classification. To address class imbalance in the training data, a solution is implemented before feeding the data into the SVM classifier. The experimental outcomes of the proposed approach, tested on five microarray databases, indicate robust feature selection with a metric ranging from 0.70 to 0.88. Additionally, the classification accuracy falls within the range of 91-96%.

Successful immune checkpoint inhibitor (ICI) therapy occurs in only a fraction of melanoma patients, and yet all patients are susceptible to potentially serious ICI-related side-effects. No current biomarkers robustly predict ICI treatment response in melanoma patients. In this study we sought to identify methylome and transcriptome markers which have the potential to predict immunotherapy response in melanoma patients ahead of treatment with anti-PD1 ICI monotherapy. Using Infinium MethylationEPIC microarrays, we analysed DNA methylation profiles of >850,000 CpG sites in pre-treatment melanoma tissues from patients administered anti-PD-1 monotherapy as first-line treatment. In addition, we analysed transcriptomes using RNA-seq. DNA methylation and gene expression data were then statistically compared to patient response to anti-PD1 therapy. We identified 2579 DNA hypomethylation and hypermethylation alterations correlating with melanoma response to anti-PD1 therapy. An integrative analysis of DNA methylomes and transcriptomes identified a subset of 35 loci, 13 of which were significantly differentially methylated in both initial discovery and external validation datasets. Functional enrichment analysis of hypomethylated sites (p-value <0.05) in non-responders was associated with "Formation of the cornified envelope", "Regulation of epithelial cell proliferation", and "Purine-containing compound metabolic process". We have identified novel integrated DNA methylation and gene expression markers, which correlate with anti-PD1 treatment response in melanoma patients. These findings suggest a relationship between tumour-associated genomic DNA methylation, gene expression patterns, and anti-PD1 ICI immunotherapy response in melanoma patients.

Post-exertional malaise (PEM) is a defining symptom of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS), yet its molecular underpinnings remain elusive. This study investigated the temporal-longitudinal DNA methylation changes associated with PEM using a structured two-day maximum repeated effort cardiopulmonary exercise testing (CPET) protocol involving pre- and two post-exercise blood samplings from five ME/CFS patients. Cardiopulmonary measurements revealed complex heterogeneous profiles among the patients compared to typical healthy controls, and VO2 peak indicated all patients had poor normative fitness. The switch to anaerobic metabolism occurred at a lower workload in some patients on Day Two of the test. Reduced Representation Bisulphite Sequencing followed by analysis with Differential Methylation Analysis Package-version 2 (DMAP2) identified differentially methylated fragments (DMFs) present in the DNA genomes of all five ME/CFS patients through the exercise test compared with 'before exercise'. With further filtering for >10% methylation differences, there were early DMFs (0-24 h after first exercise test) and late DMFs between (24-48 h after the second exercise test), as well as DMFs that changed gradually (between 0 and 48 h). Of these, 98% were ME/CFS-specific, compared with the two healthy controls accompanying the longitudinal study. Principal component analysis illustrated the three distinct clusters at the 0 h, 24 h, and 48 h timepoints, but with heterogeneity among the patients within the clusters, highlighting dynamic methylation responses to exertion in individual patients. There were 24 ME/CFS-specific DMFs at gene promoter fragments that revealed distinct patterns of temporal methylation across the timepoints. Functional enrichment of ME-specific DMFs revealed pathways involved in endothelial function, morphogenesis, inflammation, and immune regulation. These findings uncovered temporally dynamic epigenetic changes in stress/immune functions in ME/CFS during PEM and suggest molecular signatures with potential for diagnosis and of mechanistic significance.

Clinical epigenetics as a field has experienced rapid advancement over recent decades. Complexities of fundamental epigenetic regulation in health and disease continue to be uncovered, alongside developments in novel epigenetic technologies and clinical applications. The 4th Clinical Epigenetics International Conference (CLEPIC) held 11th -13th June 2025 at The University of Campania Luigi Vanvitelli in Naples, Italy, highlighted exciting progress across a range of areas. Here, we provide an overview of the broad themes and emerging concepts explored during this meeting and discuss prospects for further research and clinical translation.

Meningiomas are among the most prevalent central nervous system (CNS) tumors, with up to 20

Post-viral conditions, Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) and Long COVID (LC), share > 95% of their symptoms, but the connection between disturbances in their underlying molecular biology is unclear. This study investigates DNA methylation patterns in peripheral blood mononuclear cells (PBMC) from patients with ME/CFS, LC, and healthy controls (HC). Reduced Representation Bisulphite Sequencing (RRBS) was applied to the DNA of age- and sex-matched cohorts: ME/CFS (n = 5), LC (n = 5), and HC (n = 5). The global DNA methylomes of the three cohorts were similar and spread equally across all chromosomes, except the sex chromosomes, but there were distinct minor changes in the exons of the disease cohorts towards more hypermethylation. A principal component analysis (PCA) analysing significant methylation changes (p < 0.05) separated the ME/CFS, LC, and HC cohorts into three distinct clusters. Analysis with a limit of >10% methylation difference and at p < 0.05 identified 214 Differentially Methylated Fragments (DMF) in ME/CFS, and 429 in LC compared to HC. Of these, 118 DMFs were common to both cohorts. Those in promoters and exons were mainly hypermethylated, with a minority hypomethylated. There were rarer examples with either no change in methylation in ME/CFS but a change in LC, or a methylation change in ME/CFS but in the opposite direction in LC. The differential methylation in a number of fragments was significantly greater in the LC cohort than in the ME/CFS cohort. Our data reveal a generally shared epigenetic makeup between ME/CFS and LC but with specific, distinct changes. Differences between the two cohorts likely reflect the stage of the disease from onset (LC 1 year vs. ME/CFS 12 years), but specific changes imposed by the SARS-CoV-2 virus in the case of the LC patients cannot be discounted. These findings provide a foundation for further studies with larger cohorts at the same disease stage and for functional analyses to establish clinical relevance.

Myalgic encephalomyelitis/chronic fatigue syndrome is a post-viral/stressor syndrome that has a complex pathophysiology reflecting multiple changes in many cell transcripts and proteins. These changes imply a change in the regulation of gene expression at the level of the DNA. A significant contributor to this is the modulation of the methylation at specific sites within regulatory regions throughout the genome that can either enhance or dampen expression depending on whether methylation is reduced or increased, respectively. DNA methylation can be analyzed by array technology or by reduced representation bisulfite sequencing (RRBS) or whole genome bisulfite sequencing (WGBS). This chapter describes RRBS, which has been very effective at analyzing the methylation states of ME/CFS patients both in single time point studies and in longitudinal studies with individual patients, for example, following a relapse recovery cycle. Here, we describe the step-by-step experimental methodology of how RRBS has been applied to DNA samples from ME/CFS patients and the analytical platforms used to detect the methylation changes that are statistically significant between patients and health controls. It has the potential to provide molecular biomarkers for a diagnostic test or to follow the progression of the condition in patients or through relapse/recovery fluctuations that occur frequently through the ongoing course of the disease. When effective therapies become available it has the potential to monitor the effectiveness on individual patients under treatment.

Metastatic progression is a complex, multistep process and the leading cause of cancer mortality. There is growing evidence that emphasises the significance of epigenetic modification, specifically DNA methylation and histone modifications, in influencing colorectal (CRC) metastasis. Epigenetic modifications influence the expression of genes involved in various cellular processes, including the pathways associated with metastasis. These modifications could contribute to metastatic progression by enhancing oncogenes and silencing tumour suppressor genes. Moreover, specific epigenetic alterations enable cancer cells to acquire invasive and metastatic characteristics by altering cell adhesion, migration, and invasion-related pathways. Exploring the involvement of DNA methylation and histone modification is crucial for identifying biomarkers that impact cancer prediction for metastasis in CRC. This review provides a summary of the potential epigenetic biomarkers associated with metastasis in CRC, particularly DNA methylation and histone modifications, and examines the pathways associated with these biomarkers.

With an increase in accuracy and throughput of long-read sequencing technologies, they are rapidly being assimilated into the single-cell sequencing pipelines. For transcriptome sequencing, these techniques provide RNA isoform-level information in addition to the gene expression profiles. Long-read sequencing technologies not only help in uncovering complex patterns of cell-type specific splicing, but also offer unprecedented insights into the origin of cellular complexity and thus potentially new avenues for drug development. Additionally, single-cell long-read DNA sequencing enables high-quality assemblies, structural variant detection, haplotype phasing, resolving high-complexity regions, and characterization of epigenetic modifications. Given that significant progress has primarily occurred in single-cell RNA isoform sequencing (scRiso-seq), this review will delve into these advancements in depth and highlight the practical considerations and operational challenges, particularly pertaining to downstream analysis. We also aim to offer a concise introduction to complementary technologies for single-cell sequencing of the genome, epigenome and epitranscriptome. We conclude by identifying certain key areas of innovation that may drive these technologies further and foster more widespread application in biomedical science.

DNA methylation is well-established as a major epigenetic mechanism that can control gene expression and is involved in both normal development and disease. Analysis of high-throughput-sequencing-based DNA methylation data is a step toward understanding the relationship between disease and phenotype. Analysis of CpG methylation at single-base resolution is routinely done by bisulfite sequencing, in which methylated Cs remain as C while unmethylated Cs are converted to U, subsequently seen as T nucleotides. Sequence reads are aligned to the reference genome using mapping tools that accept the C-T ambiguity. Then, various statistical packages are used to identify differences in methylation between (groups of) samples. We have previously developed the Differential Methylation Analysis Pipeline (DMAP) as an efficient, fast, and flexible tool for this work, both for whole-genome bisulfite sequencing (WGBS) and reduced-representation bisulfite sequencing (RRBS). The protocol described here includes a series of scripts that simplify the use of DMAP tools and that can accommodate the wider range of input formats now in use to perform analysis of whole-genome-scale DNA methylation sequencing data in various biological and clinical contexts. © 2024 The Author(s). Current Protocols published by Wiley Periodicals LLC. Basic Protocol: DMAP2 workflow for whole-genome bisulfite sequencing (WGBS) and reduced-representation bisulfite sequencing (RRBS).