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PubMed2026

Non-parametric differential methylation analysis characterizes histotype-specific promoter regions in epithelial ovarian cancer.

Epithelial ovarian cancer (EOC) is a heterogenous disease with frequent late-stage diagnosis and high mortality rates, for which no reliable screening tests exist. In recent years, epigenetic biomarkers in the form of DNA methylation in CpG-rich regions have gained increased attention in the scientific community due to their robust nature and accessibility, allowing for diagnosis without the need for invasive surgery. In this study, we investigated the aberrant methylation of promoter regions in early stage EOC through non-parametric methods, with the purpose of characterizing candidate epigenetic biomarkers. The approach was used on a cohort of early stage EOC samples, and results were compared to existing programs for differential methylation. Significant regions were then used to construct a CpG panel for stratifying EOC histotypes through predictive classification in external data. Identified promoter regions were highly reproducible across cohorts, and the constructed CpG model stratified histotypes in external cohorts through predictive classification. Comparisons against other DMP and DMR callers showed a degree of homogeneity between results but also revealed promoter regions that were overlooked despite clear signs of aberrant methylation. Finally, EOC histotypes were found to differ in their methylation distribution types, and results indicate that methods sensitive to non-normally distributed data may be poorly suited to compare groups with different distribution types. The non-parametric approach identified aberrantly methylated promoter regions that were highly reproducible across cohorts. Results from predictive classification indicate that these regions may be useful for the purpose of EOC histotype stratification.

Humans↗

PubMed2026

Low-Cost Nucleic-Acid-Based Radial Flow Assay for the Detection of GSTP1 Promoter DNA Methylation in Prostate Cancer.

DNA methylation of the glutathione S-transferase pi 1 (GSTP1) promoter is a widely studied epigenetic biomarker for prostate cancer; however, its direct detection in genomic DNA remains analytically challenging without complex chemical or amplification-based workflows. Here, we report a nucleic acid-based radial flow assay (NABRFA) that enables visual and pattern-based detection of gene-specific DNA methylation using gold nanoparticle (AuNP)-conjugated oligonucleotide probes. Thiol-modified single-stranded DNA probes targeting the GSTP1 CpG island (5'ThG) were conjugated to AuNPs to form stable probe-nanoparticle constructs that retain colloidal stability under high ionic strength conditions (0.5 M NaCl). Upon hybridization with methylation-protected GSTP1 DNA, the resulting AuNP-DNA complexes exhibit hybridization-dependent modulation of transport and retention on a porous nylon membrane, generating characteristic concentric radial patterns. These patterns arise from spatial separation between retained hybridized complexes and outwardly transported unbound probe-functionalized nanoparticles, enabling direct visual discrimination of target presence. The assay demonstrated concentration-dependent pattern evolution, with visual detection achievable down to 1 ng of target DNA and an analytically determined limit of detection of approximately 32 ng, based on image-derived gray value analysis. The human prostate cancer cell line LNCaP, known for GSTP1 promoter hypermethylation, was used as the test model for assay validation. Comparative analysis using methyl-sensitive restriction enzyme-treated native genomic DNA from the human osteosarcoma MG-63 cell line (non-prostate cancer, GSTP1 methylation-negative control) and the human lung fibroblast WI-38 cell line (non-cancerous, GSTP1 methylation-negative control) confirmed assay specificity. By coupling sequence-specific hybridization with transport-mediated nanoparticle pattern formation, NABRFA provides a label-free and conversion-free analytical strategy for detection of methylation-protected GSTP1 DNA using minimal instrumentation. This work establishes a proof-of-concept membrane-based, transport-driven sensing approach for epigenetic biomarker detection and highlights its potential for integration into simplified molecular diagnostic workflows.

Humans↗

PubMed2025

Epigenetic regulation of HOXA2 expression affects tumor progression and predicts breast cancer patient survival.

Accumulating evidence suggests that genetic and epigenetic biomarkers hold potential for enhancing the early detection and monitoring of breast cancer (BC). Epigenetic alterations of the Homeobox A2 (HOXA2) gene have recently garnered significant attention in the clinical management of various malignancies. However, the precise role of HOXA2 in breast tumorigenesis has remained elusive. To address this point, we conducted high-throughput RNA sequencing and DNA methylation array studies on laser-microdissected human BC samples, paired with normal tissue samples. Additionally, we performed comprehensive in silico analyses using large public datasets: TCGA and METABRIC. The diagnostic performance of HOXA2 was calculated by means of receiver operator characteristic curves. Its prognostic significance was assessed through immunohistochemical studies and Kaplan-Meier Plotter database interrogation. Moreover, we explored the function of HOXA2 and its role in breast carcinogenesis through in silico, in vitro, and in vivo investigations. Our work revealed significant hypermethylation and downregulation of HOXA2 in human BC tissues. Low HOXA2 expression correlated with increased BC aggressiveness and unfavorable patient survival outcomes. Suppression of HOXA2 expression significantly heightened cell proliferation, migration, and invasion in BC cells, and promoted tumor growth in mice. Conversely, transgenic HOXA2 overexpression suppressed these cellular processes and promoted apoptosis of cancer cells. Interestingly, a strategy of pharmacological demethylation successfully restored HOXA2 expression in malignant cells, reducing their neoplastic characteristics. Bioinformatics analyses, corroborated by in vitro experimentations, unveiled a novel implication of HOXA2 in the lipid metabolism of BC. Specifically, depletion of HOXA2 leaded to a concomitantly decreased expression of PPARγ and its target CIDEC, a master regulator of lipid droplet (LD) accumulation, thereby resulting in reduced LD abundance in BC cells. In summary, our study identifies HOXA2 as a novel prognosis-relevant tumor suppressor in the mammary gland.

Humans↗

PubMed2026

Machine Learning-Based Identification of Survival-Associated CpG Biomarkers in Pancreatic Ductal Adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is an exceptionally aggressive cancer with a 5-year survival rate of less than 10%, driven by late-stage diagnosis, limited treatment options, and a lack of reliable biomarkers for early detection and prognosis. In this study, we integrated DNA methylation data from TCGA and ICGC cohorts, categorizing samples based on survival time, and identified 688 differentially methylated CpG sites, along with 224 CpG biomarkers significantly associated with patient survival through statistical and machine learning-based analyses. We developed a random forest model to predict patient survival, achieving 85.2% accuracy for short-survival patients and 70.0% for long-survival patients in the validation set. External dataset validation further confirmed the model's robustness and accuracy. De novo motif analysis of genomic regions surrounding the 224 CpG biomarkers identified TWIST1 and FOXA2 as key transcriptional regulators enriched in survival-associated CpG sites, linking their activity to patient survival outcomes. Collectively, our findings highlight valuable epigenetic biomarkers and provide a predictive model to assess PDAC risk levels post-surgery, offering the potential for improved patient stratification and personalized therapeutic strategies.

DNA methylation↗

PubMed2025

Machine Learning-Based Identification of Survival-Associated CpG Biomarkers in Pancreatic Ductal Adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is an exceptionally aggressive cancer with a 5-year survival rate of less than 10%, driven by late-stage diagnosis, limited treatment options, and a lack of reliable biomarkers for early detection and prognosis. In this study, we integrated DNA methylation data from TCGA and ICGC cohorts, categorizing samples based on survival time, and identified 684 differentially methylated CpG sites, along with 224 CpG biomarkers significantly associated with patient survival through statistical and machine learning-based analyses. We developed a random forest model to predict patient survival, achieving 85.2% accuracy for short-survival patients and 70.0% for long-survival patients in the validation set. External dataset validation further confirmed the model's robustness and accuracy. De novo motif analysis of genomic regions surrounding the 224 CpG biomarkers identified TWIST1 and FOXA2 as key transcriptional regulators enriched in survival-associated CpG sites, linking their activity to patient survival outcomes. Collectively, our findings highlight valuable epigenetic biomarkers and provide a predictive model to assess PDAC risk levels post-surgery, offering the potential for improved patient stratification and personalized therapeutic strategies.

Journal Article↗

PubMed2025

Stochastic epigenetic mutation profiles as biomarkers of clinical activity in juvenile idiopathic arthritis: a multi-omic machine learning approach for gene prioritization.

BACKGROUND: Juvenile idiopathic arthritis (JIA) is a rare autoimmune disease arising from a complex interplay between genetic and environmental factors. Epigenetic modifications such as DNA methylation (DNAm) have been described as potential mediators in gene-environment interactions, contributing to immune system dysregulation. Emerging evidence suggests that DNAm profiles also predict therapeutic responses in autoimmune diseases. This study aims to identify epigenetic biomarkers and epigenetic-driven gene expression changes associated with JIA clinical activity. METHODS: We reanalyzed a publicly available dataset of 44 JIA patients, with whole-genome DNAm and gene expression from CD4 + T cells measured at two points: at anti-TNF therapy withdrawal (T0) and eight months later (Tend). At Tend, 30 patients maintained inactive disease (ID) while 14 did not (NO ID). We investigated differences between ID and NO ID patients in the epigenetic mutation load and various epigenetic clocks through linear regression models, and prioritized genomic regions with significantly higher number of epimutations in NO ID patients through machine learning. RESULTS: We found a higher mutation load in NO ID than ID patients, both at T0 and at Tend, with the differences at Tend reaching statistical significance (p = 0.02). In contrast, we found no evidence of association between epigenetic clocks and JIA clinical activity. Using a multi-omic approach, we identified a List of candidate epigenetically-driven differentially expressed genes, 80 up-regulated and 77 down-regulated, in NO ID patients. Finally, comparing our candidate gene list with the Connectivity Map database, we identified new candidate potential therapeutic targets. Key findings were validated in independent datasets: DNAm profiles from CD4 + T cells (56 JIA patients, 57 controls) and transcriptomic data from PBMCs of JIA patients with active or inactive disease, confirming dysregulation of pathways such as TNF-α signaling via NF-kB and TGF-β signaling among others. CONCLUSIONS: We described a significant association of epigenetic mutations with JIA clinical activity, indicating that epigenetic changes might precede clinical symptoms and may serve as biomarkers for early disease monitoring. Further, our results shed light on biomolecular mechanisms of JIA, supporting the development of more effective treatments.

Humans↗

PubMed2025

Developmental timing of index trauma exposure and accelerated epigenetic aging in United States military veterans.

Trauma exposure has been linked to accelerated GrimAge, an epigenetic biomarker of premature morbidity and mortality. Building on this evidence, the present study examined whether the type and timing of index trauma exposure are differentially associated with accelerated GrimAge. Participants were 873 European American male United States military Veterans from the National Health and Resilience in Veterans Study. We investigated associations between self-reported age at index trauma, index trauma type (interpersonal violence, non-interpersonal trauma, or loss/instability/other), and accelerated GrimAge, operationalized as GrimAge exceeding chronological age by five or more years. Results revealed that interpersonal violence was associated with three-fold greater odds of accelerated GrimAge compared to other trauma types. Age at index trauma was not independently associated with accelerated GrimAge. However, we observed a significant interaction between trauma type and its developmental timing, even after adjusting for index trauma recency, cumulative trauma burden, and other potential confounders. Specifically, Veterans who were older at the time of exposure to interpersonal violence or trauma involving loss or instability had higher odds of accelerated GrimAge. In contrast, exposure to non-interpersonal trauma was more strongly associated with accelerated GrimAge when it occurred at younger ages. These results indicate that trauma type and timing jointly influence epigenetic aging in Veterans, highlighting the need for tailored interventions that address specific trauma characteristics to reduce associated long-term health risks in this population.

Humans↗

PubMed2026

DNA hypomethylation of the OLFM1 gene in patients with depression.

OBJECTIVE: Depression is a heterogeneous psychiatric disorder and a growing public health concern, characterized by its high prevalence, recurrence rate, and association with suicide. There is evidence suggesting that both genetic susceptibility and environmental factors can regulate gene expression through DNA methylation, thereby influencing the occurrence and development of depression. The olfactory sensory neuropeptide 1 (OLFM1) protein is a risk factor for mental disorders. However, there are no reports yet regarding the correlation between the OLFM1 gene and depression, nor have there been any studies on the association between OLFM1 gene DNA methylation and depression. METHODS: Genomic DNA was extracted from peripheral blood samples of patients with depression (n = 100) and healthy controls (n = 100) using the QIAamp DNA Blood Mini Kit. Subsequently, the extracted genomic DNA was subjected to bisulfite treatment using the EZ DNA Methylation-Gold™ kit. DNA methylation levels of 107 CpG sites in six fragments of OLFM1 exon 1 and its downstream were detected by the Illumina HiSeq platform using MethylTarget™ technology. RESULTS: Methylation levels across the overall OLFM1 CpG island and its six fragments (OLFM1-1 to OLFM1-6) were significantly reduced in the depression group relative to controls. Analysis of the OLFM1 gene fragments revealed that 84 of 107 CpG sites were significantly hypomethylated in depressed individuals. When patients were divided by sex, male patients displayed hypomethylation at 65 CpG sites, substantially more than the 37 sites found in females. CONCLUSION: OLFM1 hypomethylation is associated with depression and may serve as a potential epigenetic biomarker.

Humans↗

PubMed2026

Beyond genes: EpiSwitch® and Orion platform-powered 3D genome architecture biomarkers reveal shared biology across ME/CFS, long COVID, PTSD, rheumatoid arthritis, and multiple sclerosis.

BACKGROUND: Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), Long COVID (LC19), post-traumatic stress disorder (PTSD), rheumatoid arthritis (RA), and multiple sclerosis (MS) are clinically distinct disorders that share substantial symptom overlap, including persistent fatigue, cognitive impairment, autonomic dysfunction, and immune dysregulation. Although these conditions differ in diagnosis and clinical presentation, their underlying biological mechanisms remain poorly understood and may involve convergent regulatory pathways. METHODS: The EpiSwitch® 3D genomics platform and Orion knowledgebase were used to integrate chromosome conformation signatures with genome-wide association study (GWAS)-derived datasets across ME/CFS, LC19, PTSD, RA, and MS. Three-dimensional genomic anchors were mapped to coding genes and analysed using STRING protein-protein interaction networks and Cytoscape-based systems biology approaches. Disease-specific anchor datasets were generated and compared at both gene and network levels to identify shared biological processes and regulatory mechanisms. RESULTS: Analysis of the ME/CFS dataset identified 552 unique 3D genomic anchors mapped to 567 genes, with analogous disease-specific anchor sets generated for LC19, PTSD, RA, and MS. Direct overlap between disease-associated genes was limited; however, higher-order network analyses revealed substantial interconnectivity and convergence across conditions. Shared biological pathways included immune and cytokine signalling, interferon responses, mitochondrial function, metabolic regulation, and neuroendocrine processes. Highly connected hub genes included immune regulatory nodes such as LAG3 and components of the mTOR signalling pathway, implicating T-cell exhaustion, chronic immune activation, and immunometabolic dysregulation as common mechanisms underlying these disorders. CONCLUSIONS: These findings support a systems-level model in which clinically overlapping fatigue-associated syndromes arise from perturbations of interconnected regulatory networks rather than discrete disease-specific pathways. Despite limited genetic overlap, substantial convergence at the network level suggests shared biological architecture across ME/CFS, LC19, PTSD, RA, and MS. The identification of common regulatory pathways provides a mechanistic framework for the development of cross-disease diagnostic and therapeutic strategies. By capturing dynamic regulatory states, 3D genomic biomarkers offer significant potential for objective blood-based diagnostics, patient stratification, and the identification of shared therapeutic targets across complex chronic disorders. These findings support the application of precision medicine approaches and may accelerate the development of novel interventions for fatigue-associated multisystem diseases.

Humans↗

PubMed2026

Transcriptome-based epigenetic screening identifies DNA hypermethylation signatures as prognostic biomarkers in oral squamous cell carcinoma.

Promoter DNA hypermethylation is a key epigenetic mechanism of gene silencing in cancer, yet the DNA hypermethylome of oral squamous cell carcinoma (OSCC) and its prognostic relevance remain poorly characterized. Here, we systematically identified and validated novel hypermethylated genes with prognostic significance in OSCC using a genome-wide discovery and multi-platform validation strategy. Candidate genes were first identified by pharmacologic demethylation combined with RNA sequencing across OSCC cell lines, then validated by quantitative RT-PCR, methylation-specific PCR, and bisulfite sequencing in OSCC cell lines, normal oral mucosa, and primary OSCC tumors, with independent confirmation in the TCGA-HNSC dataset. Immunohistochemistry confirmed protein-level silencing, and Kaplan-Meier survival analysis assessed prognostic significance across both cohorts. This pipeline identified five candidate genes, GPX3, ANG, CTGF, GPRC5B, and BAMBI, exhibiting cancer-specific promoter hypermethylation associated with transcriptional and protein silencing in OSCC. Validation in oral cavity tumor samples extracted from the TCGA-HNSC dataset confirmed tumor-specific hypermethylation and revealed significant inverse correlations between methylation and expression for GPX3, GPRC5B, and CTGF. Notably, CTGF hypermethylation was independently associated with poor overall survival in both cohorts (institutional cohort, p=0.03; oral tumor subset from TCGA-HNSC, p=0.01), and a combined ANG+CTGF methylation signature showed superior and reproducible prognostic performance across both platforms. Pathway analysis linked these genes to epithelial-mesenchymal transition and interferon response signaling. This study establishes the first validated DNA methylation biomarker panel for OSCC prognosis, identifying CTGF hypermethylation as a robust prognostic driver with translational potential for clinical risk stratification.

Humans↗

PubMed2026

Understanding and making sense of epigenetic age misalignment across different aging clocks.

The output of an epigenetic aging clock can vary depending on the training method utilized, cell type composition, the nature of the training dataset, the technology used to generate the methylomic data, acute stressors, and other factors. On an individual level, epigenetic age can fluctuate across different clocks purely due to differences in model training. Among aging clock researchers, it is well-known that the epigenetic age of a single sample can vary across different models. Based on our observations and conversations with longevity scientists and stakeholders, however, this fact is often unappreciated among non-aging clock experts. To help bring more awareness to this important topic, we highlight key literature and, as an illustrative example, use eight blood-trained clocks to show that epigenetic age is frequently misaligned in a publicly available whole blood dataset. Our simple analysis revealed that the average sample difference between the youngest and oldest predicted ages across these clocks was 17 years. The smallest and largest individual-level differences observed were 4 and 45 years, respectively. Clock misalignment has implications for choosing which clock to utilize, interpreting the impact of an intervention on epigenetic age, personalized tracking, and relating epigenetic age to the abstract concept of biological age.

Humans↗

PubMed2025

ANXA3 hypomethylation as a prognostic biomarker in hepatitis B virus-related acute-on-chronic liver failure.

BACKGROUND: Hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF) is associated with a poor prognosis. This research aimed to characterize the expression pattern and clinical value of Annexin A3 (ANXA3) in HBV-ACLF patients. METHODS: First of all, ACLF-related datasets were downloaded from the Gene Expression Omnibus (GEO) database to carry out bioinformatics analyses. RT-qPCR, ELISA, and Methylight were used to measure ANXA3 gene expression and promoter methylation levels. A validation cohort was leveraged to further validate the results. RESULTS: Transcriptome analysis showed that ANXA3 was among the most differentially expressed genes when comparing dead patients with HBV-ACLF to those with survivors. The mRNA and serum levels of ANXA3 were elevated, and methylation levels were decreased in HBV-ACLF patients. The PMR value of ANXA3 in patients with HBV-ACLF was negatively correlated with inflammation-related cytokines IL-6, TNF-α, and IL-1β, as well as quantitative clinical parameters AST, TBIL, PT, INR, NEUT%, and MELD score, and positively correlated with PTA (all p < 0.05). In HBV-ACLF patients, ANXA3 was considered to be an independent influence factor for the 90-day mortality. It was also found that ANXA3, especially hypomethylation, was associated with 28- and 90-day overall survival in patients with HBV-ACLF based on receiver operating characteristic (ROC) analysis, decision curve analysis (DCA), and Kaplan-Meier curves. CONCLUSIONS: ANXA3 hypomethylation has a prominent predictive value for short-term mortality in patients with HBV-ACLF and may serve as a promising biomarker of HBV-ACLF prognosis.

Humans↗

PubMed2026

KLK6 is Associated with a Neutrophil-Dominant Immunosuppressive Microenvironment and Epigenetic Deregulation in Lung Adenocarcinoma.

INTRODUCTION: Lung adenocarcinoma (LUAD) is the most prevalent histological subtype of lung cancer and is associated with poor survival despite advances in targeted therapies. Kallikrein-related peptidase 6 (KLK6) has been implicated in several malignancies, but its expression pattern, clinical relevance, and biological function in LUAD remain incompletely characterized. This study aimed to evaluate KLK6 expression and its associations with prognosis, epigenetic regulation, immune infiltration, and migratory phenotypes in LUAD. METHODS: RNA-seq expression and clinical data were obtained from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Gene Expression Omnibus (GEO) databases. KLK6 expression was analyzed in relation to clinicopathological parameters, survival outcomes, promoter methylation status (via UALCAN), and tumor-infiltrating immune cell abundance (via TIMER2.0). In vitro, KLK6 was knocked down using shRNA in A549 and H1299 LUAD cell lines. Cell migration was assessed by transwell assays, and the expression of Epithelial-Mesenchymal Transition (EMT)- and Wnt signaling-related markers was examined by qRT-PCR and Western blotting. RESULTS: KLK6 expression was significantly upregulated in LUAD tissues compared with normal lung tissues. High KLK6 expression was associated with poorer overall survival (HR = 1.52, P = 0.009) and disease-specific survival (HR = 1.55, P = 0.03). ROC analysis showed that KLK6 discriminated stage I LUAD from normal tissues with an AUC of 0.73. Promoter hypomethylation was observed in LUAD tumors and correlated with increased KLK6 expression. Immune infiltration analysis revealed that KLK6-high tumors exhibited reduced B-cell infiltration and increased neutrophil infiltration. Functional experiments demonstrated that KLK6 knockdown significantly suppressed cell migration, accompanied by increased E-cadherin and decreased N-cadherin, Vimentin, Wnt5a, and β-catenin expression. DISCUSSION: These findings suggest that KLK6 overexpression in LUAD is driven in part by promoter hypomethylation and is closely linked to a neutrophil-dominant immunosuppressive microenvironment. Furthermore, KLK6 appears to promote LUAD cell migration through EMT- and Wnt-related signaling pathways. Collectively, these multi-layered data position KLK6 as a potential driver of aggressive tumor behavior and a candidate biomarker for risk stratification. CONCLUSION: KLK6 is aberrantly overexpressed in LUAD and is associated with poor prognosis and enhanced migratory capacity. It may serve as a promising prognostic biomarker and a potential therapeutic target for LUAD.

KLK6↗

PubMed2025

Altered mitochondrial DNA methylation in blood in individuals with mild cognitive impairment.

BACKGROUND: Previous studies reported that altered mitochondrial methylation in Alzheimer's disease (AD), however, whether epigenetic modifications in mitochondrial genomes contribute to preclinical AD remains unclear. This study aimed to investigate mitochondrial methylation changes in individuals with cognitive decline. RESEARCH DESIGN AND METHODS: We examined whole mitochondrial genome methylation in 50 individuals with mild cognitive impairment (MCI) and 50 individuals without MCI, using bisulfite amplicon sequencing, assessing methylation at 366 Cytosine-guanine oligodeoxynucleotide (CpG) sites. RESULTS: We found the overall methylation level of mitochondrial DNA (mtDNA) in each subject was relatively low, ranging from 0% to 15%. Global methylation was significantly higher in individuals with cognitive decline compared to controls (3.86% vs. 3.46%, p = 0.037), with 34 differentially methylated CpG sites identified. Methylation differences (MD) between cognitive decline individuals and controls were 22.93 ± 5.60% at chrM6465 (Q = 0.013), 12.55 ± 3.02% at chrM9612 (Q = 0.013), 11.45 ± 3.88% at chrM11762 (Q = 0.159) and 11.03 ± 3.88% at chrM11766 (Q = 0.172), respectively, while the level of MD at chrM15812 was -13.11 ± 4.31% (Q = 0.159) after Benjamini-Hochberg FDR adjusted. Furthermore, Methylation at specific sites were significantly correlated with Mini-Mental State Examination scores, distinguishing individuals with cognitive decline from controls. CONCLUSIONS: Our study provides an mtDNA methylation map and suggests a role for these sites in preclinical AD pathogenesis.

Humans↗

PubMed2026

Osteoarthritis phenotypes: advancing precision medicine through clinical, structural, and molecular stratification.

PURPOSE: Osteoarthritis (OA) is now understood as a heterogeneous syndrome driven by diverse biological, biomechanical, metabolic, genetic, and molecular mechanisms. This variability explains differences in disease progression and treatment response, challenging the traditional "one-size-fits-all" approach. This review highlights OA phenotyping as a key step toward precision medicine, focusing on clinical, structural, and molecular classifications that inform individualized care. METHODS: A narrative review was conducted using a non-systematic search of major databases and Osteoarthritis Research Society International sources (2010-2026). Evidence was thematically synthesized across clinical, imaging, and molecular domains to characterize OA phenotypes and their potential relevance to precision medicine. RESULTS: Multiple OA phenotypes were identified: inflammatory, metabolic, biomechanical, cartilage-subchondral, pain-sensitization, and aging/senescence. These exhibit distinct clinical features, risk factors, and therapeutic responses. Imaging-based phenotypes (e.g., inflammatory, meniscus-cartilage, subchondral bone, atrophic, hypertrophic) and molecular endotypes (low turnover, structural damage, systemic inflammation) further refine stratification. Pain-structure discordance is notable in sensitization phenotypes and may predict poorer surgical outcomes. Joint-specific variations and emerging genomic and epigenetic insights underscore disease complexity. Advances in imaging, biomarkers, and machine learning may enable earlier detection and patient clustering, though clinical application remains limited. CONCLUSION: Phenotype- and endotype-based classification represents a critical advancement toward precision OA management. Tailored interventions based on stratification hold promise for improving outcomes; however, clinical translation remains limited by overlapping phenotypes, lack of validated biomarkers, and inconsistent results from phenotype-driven trials. Wider clinical adoption requires standardized definitions, validation across joints, and integration of multimodal diagnostic tools into routine practice.

Humans↗

PubMed2025

Blood-based DNA methylation markers for autism spectrum disorder identification using machine learning.

BACKGROUND: Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder lacking objective biomarkers for early diagnosis. DNA methylation is a promising epigenetic marker, and machine learning offers a data-driven classification approach. However, few studies have examined whole-blood, genome-wide DNA methylation profiles for ASD diagnosis in school-aged children. METHODS: We analyzed genome-wide DNA methylation data from GEO dataset GSE113967, including 52 children with ASD and 48 typically developing (TD) controls. Differentially methylated positions (DMPs) were identified, and feature selection was performed using support vector machine-recursive feature elimination with cross-validation (SVM-RFECV). Classification models were developed using random forest (RF), extreme gradient boosting (XGBoost), and decision tree (DT) classifiers. A nomogram visualized feature contributions. RESULTS: A total of 138 DMPs differentiated ASD from TD children. Eleven CpG sites selected by SVM-RFECV formed the basis for model construction. RF and XGBoost achieved the highest accuracy (75%), with DT reaching 70%. Functional annotation indicated enrichment in cell adhesion and immune-related pathways. CONCLUSIONS: This exploratory study demonstrates the feasibility of integrating peripheral blood DNA methylation data with machine learning to distinguish children with ASD. While limited by sample size and moderate accuracy, this study provides methodological insights into the feasibility of integrating epigenetic and computational approaches for ASD-related biomarker exploration.

Humans↗

PubMed2026

DNA methylation biomarkers for early detection of ovarian cancer.

Ovarian cancer (OC) remains difficult to detect at an early stage, and current screening approaches using CA125 and transvaginal ultrasonography have not demonstrated sufficient benefit for population screening. DNA methylation is a promising biomarker class because epigenetic alterations may arise early in tumourigenesis, can be detected in circulating cell-free DNA (cfDNA), and may provide tissue-of-origin information. This review critically evaluates recent evidence on DNA methylation biomarkers for early OC detection. PubMed/MEDLINE, Web of Science, and Scopus were searched for studies published between January 2020 and September 2025, supplemented by selected earlier studies of biological or methodological relevance. Evidence was synthesised across single-gene biomarkers, multi-locus panels, genome-wide signatures, assay platforms, and machine-learning classifiers, with emphasis on early-stage performance, histological representation, comparator populations, analytical methodology, and validation design. Single-gene markers such as BRCA1, RASSF1A, OPCML, HOXA9, and HIC1 show variable performance, while multi-gene and classifier-based approaches generally provide stronger discrimination. However, many studies remain limited by retrospective case-control designs, small FIGO stage I-II subsets, predominance of serous disease, and insufficient prospective validation. Integration with CA125 may improve sensitivity but can reduce specificity, which is critical in low-prevalence screening. Clinical translation will therefore require minimal and reproducible methylation signatures, standardised low-input cfDNA workflows, rigorous external validation, and prospective longitudinal evaluation in intended-use populations.

Humans↗

PubMed2025

DNA Methylation Analysis by Bisulfite Pyrosequencing of Mouse Embryonic Fibroblasts with Reprogramming Enhanced by Thyroid Hormones.

DNA methylation is a widely studied epigenetic mark which in mammals involves the incorporation of a methyl group to the fifth carbon of cytosines, mainly those belonging to CpG dinucleotides. It has been linked to context-dependent regulatory functions ranging from gene and repetitive DNA silencing to gene body transcriptional activity. Because of its important roles during embryonic development and cell differentiation, DNA methylation can be used to track cell reprogramming by measuring the methylation levels of pluripotency-associated factors. In this scenario, bisulfite pyrosequencing is a simple, robust, and widely used technique which allows for the quantification of DNA methylation levels at small, specific regions of the genome. It involves the amplification and biotin tagging of bisulfite-converted DNA. Single amplified strands are then purified using streptavidin and finally pyrosequenced using a sequencing primer. Thus, it is an ideal method for the quantitative profiling of specific genomic regions, with applications ranging from biomarker discovery and epigenetic clock tracking to omic validation studies.

Animals↗