PubMed Health⌕ Search

SEARCH · PubMed Health

Results for “DNA methylation biomarker”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 recordsLinked to original sources
PubMed2006

Prognostic DNA methylation biomarkers in ovarian cancer.

PURPOSE: Aberrant DNA methylation, now recognized as a contributing factor to neoplasia, often shows definitive gene/sequence preferences unique to specific cancer types. Correspondingly, distinct combinations of methylated loci can function as biomarkers for numerous clinical correlates of ovarian and other cancers. EXPERIMENTAL DESIGN: We used a microarray approach to identify methylated loci prognostic for reduced progression-free survival (PFS) in advanced ovarian cancer patients. Two data set classification algorithms, Significance Analysis of Microarray and Prediction Analysis of Microarray, successfully identified 220 candidate PFS-discriminatory methylated loci. Of those, 112 were found capable of predicting PFS with 95% accuracy, by Prediction Analysis of Microarray, using an independent set of 40 advanced ovarian tumors (from 20 short-PFS and 20 long-PFS patients, respectively). Additionally, we showed the use of these predictive loci using two bioinformatics machine-learning algorithms, Support Vector Machine and Multilayer Perceptron. CONCLUSION: In this report, we show that highly prognostic DNA methylation biomarkers can be successfully identified and characterized, using previously unused, rigorous classifying algorithms. Such ovarian cancer biomarkers represent a promising approach for the assessment and management of this devastating disease.

Adenocarcinoma↗
PubMed2005

Identifying DNA methylation biomarkers of cancer drug response.

In the last few years, DNA methylation has become one of the most studied gene regulation mechanisms in carcinogenesis as a result of the cumulative evidence produced by the scientific community. Moreover, advances in the technologies that allow detection of DNA methylation in a variety of analytes have opened the possibility of developing methylation-based tests. A number of studies have provided evidence that specific methylation changes can alter the response to different therapeutic agents in cancer and, therefore, be useful biomarkers. For example, the association of the methylation status of DNA repair genes such as MGMT and MLH1 illustrate the two main mechanisms of response to DNA damaging agents. Loss of methylation of MGMT, and the subsequent increase in gene expression, leads to a reduction in response to alkylating agents as a result of enhanced repair of drug-induced DNA damage. Conversely, the increase in methylation of MLH1 and its resulting loss of expression has been consistently observed in drug-resistant tumor cells. MLH1 encodes a mismatch repair enzyme activated in response to DNA damage; activation of MLH1 also induces apoptosis of tumor cells, and thus loss of its expression leads to resistance to DNA-damaging agents. Other methylation-regulated genes that could serve as biomarkers in cancer therapy include drug transporters, genes involved in microtubule formation and stability, and genes related to hormonal therapy response. These methylation markers have potential applications for disease prognosis, treatment response prediction, and the development of novel treatment strategies.

Antineoplastic Agents↗
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↗
PubMed2004

DNA methylation biomarkers of cancer: moving toward clinical application.

While different markers for cancer diagnosis have been known for at least a decade, the systematic search for biomarkers emerged only several years ago. In this article, I will concentrate on DNA methylation as a dynamic and robust platform for the development of cancer-specific biomarkers. Simultaneous analysis of a growing number of independent methylation events can create increasingly more precise and individualized diagnostics. The differential detection of methylated and unmethylated DNA can be accomplished through either chemical modification or digestion with methylation-sensitive restriction enzyme(s). The benefits and potential pitfalls of both these approaches for clinical sample analysis will be addressed.

Biomarkers, Tumor↗
PubMed2026

Cell-free DNA methylation biomarkers for the early detection and tumor burden monitoring of gastric cancer.

Development of sensitive biomarkers is required to achieve early detection and tumor burden monitoring in gastric cancer (GC). We performed genome-wide methylation sequencing on 78 tissue and 241 plasma samples from 171 GC patients and 114 healthy controls from two independent clinical centers. Differentially methylated regions (DMRs) were screened using paired GC and normal tissues, and refined through cfDNA profiles with LASSO regression to construct a cfDNA-based biomarker, the GCML-score. The GCML-score, consisting of 13 DMRs, demonstrated excellent diagnostic performance (AUC: 0.95/0.99/0.95 overall and 0.96/0.99/0.82 in early GC for training/internal validation/external validation cohorts). In 12 patients receiving neoadjuvant chemotherapy, dynamic changes in GCML-score were consistent with radiological tumor burden, highlighting its monitoring potential. The GCML-score, derived from genome-wide cfDNA methylation profiling, provides a robust tool for early GC detection and real-time tumor burden monitoring, facilitating improved prognosis and personalized therapeutic strategies.

Journal Article↗
PubMed2003

DNA methylation, a biomarker for colorectal cancer: implications for screening and pathological utility.

Currently up to one-third of colorectal cancer patients present with locally advanced or metastatic disease that precludes a surgical cure. Performance limitations and low uptake of current screening tools have fueled research to develop minimally invasive approaches that can detect early-stage neoplasms. The observation that altered DNA can be amplified from the stool or circulation has stimulated research on its use as a biomarker of occult neoplasia. De novo methylation of CpG islands 5' to certain tumor suppressor genes has been associated with epigenetic silencing. At certain loci this phenomenon is specific for neoplastic populations, and it is frequently detected at early stages in colorectal tumorigenesis. Accordingly, hypermethylation events have been proposed by researchers as ideal targets for the basis of a screening panel to detect peripheral tumor DNA. This critique reviews research findings on the use of epigenetic biomarkers in screening for occult neoplasia. In addition, the authors consider the pathological utility of epigenetic testing in refining tumor staging and predicting disease recurrence.

Biomarkers, Tumor↗
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↗
PubMed2022

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.

Adult↗
PubMed2003

DNA methylation as a cancer-specific biomarker: from molecules to populations.

Cancer contributes to a large proportion of the mortality and morbidity in the United States and worldwide. Despite advances in diagnosis and treatment of various cancers, early detection and treatment of cancer remain a challenge. Diagnosis of cancer often occurs once the disease has progressed to a point where currently available intervention options provide limited success. Therefore, techniques that enable early detection followed by targeted interventions would influence stage at diagnosis and, in turn, mortality associated with cancer. Identification of molecular biomarkers, especially those that are associated with cancer initiation and progression, shows promise as an effective strategy in this regard. One potential early detection biomarker is DNA methylation of the promoter region of certain cancer-associated genes, which results in gene inactivation. Examination of serum for circulating tumor DNA with abnormal methylation patterns offers a possible method for early detection of several cancers and serves as a point for early intervention and prevention strategies. Additionally, it is imperative to consider how such a screening mechanism can be implemented in populations at risk, especially in resource-poor settings. Thus, the challenge is to validate DNA methylation as a cancer-specific biomarker, with the ultimate goal of designing a research plan that integrates the current knowledge base regarding cancer detection and diagnosis into specific prevention and intervention strategies that can be applied at a population level.

Biomarkers, Tumor↗
PubMed2025

Discovery and performance of DNA methylation panels for cancer detection and classification in blood.

Examining DNA in a liquid biopsy for non-invasive cancer detection relies on identifying dilute signal in a high background. This study aims to identify DNA methylation biomarkers for multi-cancer detection. Utilizing large tissue datasets, we apply novel search algorithms to discover confined biomarker panels capable of distinguishing tumor from normal and determining the tissue of origin. We explore the applicability to blood-based testing using targeted methylation sequencing followed by machine learning classification. We present an 8-marker panel, which successfully predicts tumors across 14 types with a 91% average sensitivity, maintaining a low false positive rate (< 0.04%). Additionally, a panel of 39 CpG sites exhibits accuracies ranging from 69% to 98% for identifying tissue of origin. When tested on 114 patient plasma samples (colon, liver, pancreatic, prostate, and stomach cancer), the 8-marker panel obtains an AUC of 0.78 with a 78% sensitivity among 32 early-stage patients (stage I-II), and 60% overall. Using the 39-marker panel in a multi-class classification model selecting only the best match, 54% of tumor samples were on average correctly assigned to the tissue of origin, and up to 80% when allowing more inclusive criteria. Using a limited set of biomarkers, our work contributes to advancing non-invasive cancer diagnostics.

DNA methylation↗
PubMed2022

Significant variation in the performance of DNA methylation predictors across data preprocessing and normalization strategies.

BACKGROUND: DNA methylation (DNAm)-based predictors hold great promise to serve as clinical tools for health interventions and disease management. While these algorithms often have high prediction accuracy, the consistency of their performance remains to be determined. We therefore conduct a systematic evaluation across 101 different DNAm data preprocessing and normalization strategies and assess how each analytical strategy affects the consistency of 41 DNAm-based predictors. RESULTS: Our analyses are conducted in a large EPIC DNAm array dataset from the Jackson Heart Study (N&#x2009;=&#x2009;2053) that included 146 pairs of technical replicate samples. By estimating the average absolute agreement between replicate pairs, we show that 32 out of 41 predictors (78%) demonstrate excellent consistency when appropriate data processing and normalization steps are implemented. Across all pairs of predictors, we find a moderate correlation in performance across analytical strategies (mean rho&#x2009;=&#x2009;0.40, SD&#x2009;=&#x2009;0.27), highlighting significant heterogeneity in performance across algorithms. Successful or unsuccessful removal of technical variation furthermore significantly impacts downstream phenotypic association analysis, such as all-cause mortality risk associations. CONCLUSIONS: We show that DNAm-based algorithms are sensitive to technical variation. The right choice of data processing strategy is important to achieve reproducible estimates and improve prediction accuracy in downstream phenotypic association analyses. For each of the 41 DNAm predictors, we report its degree of consistency and provide the best performing analytical strategy as a guideline for the research community. As DNAm-based predictors become more and more widely used, our work helps improve their performance and standardize their implementation.

DNA Methylation↗
PubMed2005

Chemoprevention: mouse colon and lung tumor bioassay and modulation of DNA methylation as a biomarker.

Lung and colon tumors were induced in A/J, C3H, and A/J X C3H (AC3) mice by administering 16 mg/kg vinyl carbamate followed by 6 weekly doses of 12 mg/kg azoxymethane (AOM). Beginning 1 week after carcinogen treatment, the mice received chemopreventive agents, dexamethasone or piroxicam, at 0.1 and 75 mg/kg in the diet, respectively. Both AOM and vinyl carbamate induces lung tumors, but only AOM induced colon tumors. The strain sensitivity for both colon and lung tumors was A/J > AC3 > C3H mice. Dexamethasone and piroxicam reduced the multiplicity of colon and lung tumors in A/J and AC3 mice, demonstrating the advantage of a combined colon and lung bioassay. The ability of budesonide, a drug that prevents mouse lung tumors, to modulate DNA methylation in vinyl carbamate-induced lung tumors was also determined. Budesonide administered for only 7 days prior to sacrifice caused a dose-dependent (0.6 to 2.4 mg/kg diet) reversal in tumors of DNA hypomethylation and hypomethylation of the insulin-like growth factor (IGF)-II gene in the differentially methylated region (DMR) 2 region of exons 4 to 5. Longer treatment with budesonide reversed hypomethylation when administered up to the time of sacrifice. These results indicate that reversal of the hypomethylation of DNA and of specific genes in lung tumors may be applicable as a surrogate end-point biomarker for chemoprevention.

Animals↗
PubMed2026

cfMethDB: A Comprehensive cfDNA Methylation Data Resource for Cancer Biomarkers.

Cancer is a major global health threat, and early detection is crucial for improving patient outcomes. DNA methylation in circulating cell-free DNA (cfDNA) has emerged as a promising biomarker for non-invasive cancer diagnosis. However, the integration and utilization of existing cfDNA methylation data have been limited, hindering comprehensive research efforts, particularly in the discovery of cfDNA methylation biomarkers. To address this challenge, we introduced cfMethDB, a comprehensive database dedicated to cfDNA methylation in cancer that encompasses 4828 publicly available datasets. Through standardized analysis, we identified 1,048,770 differentially methylated cytosines (DMCs) as candidate biomarkers across seven cancer types. With cfMethDB, we not only identified known cfDNA methylation biomarkers, but also discovered several genes, such as ZIC4, that could be novel biomarkers. Moreover, cfMethDB offers a suite of user-friendly tools, including biomarker evaluation, pan-cancer search, and end motif analysis. We hope that cfMethDB will serve as a valuable platform for the discovery of novel cancer cfDNA methylation biomarkers and facilitate cancer research and clinical applications. cfMethDB is publicly available at https://cfmethdb.hzau.edu.cn/home.

Humans↗
PubMed2025

Discovery of a DNA methylation episignature as a molecular biomarker for fetal alcohol syndrome.

PURPOSE: Fetal alcohol spectrum disorder (FASD) encompasses a range of clinical features and neurodevelopmental disorders in children exposed to alcohol in utero. Despite its global public health significance, FASD diagnosis remains challenging because of nonspecific clinical findings and the lack of an accurate molecular diagnostic biomarker. This study aimed to evaluate peripheral blood DNA methylation (DNAm) profiles as a potential diagnostic biomarker for fetal alcohol syndrome. METHODS: Genomic DNAm profiles from 93 individuals with suspected or confirmed FAS, including a clinically diagnosed FAS subgroup, were analyzed and compared with a large database of control and patient cohorts with previously reported DNAm episignatures. Functional analysis of these DNAm profiles was performed to identify episignatures and assess their potential diagnostic utility. RESULTS: A relatively sensitive and specific DNAm episignature for FAS was identified. Comparative epigenomic analysis revealed functional correlations between FAS and other rare genetic disorders, supporting the robustness of the identified DNAm profiles as a diagnostic tool. CONCLUSION: This study demonstrates that unique DNAm profiles provide a robust episignature biomarker for FAS. These findings contribute to the molecular understanding of FAS and hold promise for improving diagnostic accuracy for this complex disorder.

Humans↗
PubMed1994

DNA methylation as an intermediate biomarker in colorectal cancer: modulation by folic acid supplementation.

Several studies have suggested that DNA hypomethylation is an early step in colorectal carcinogenesis. However, it is not clear at which stage in carcinogenesis this hypomethylation occurs, what promotes it, the extent to which it can be reversed and the consequences of such reversal in affecting tumour development. In an attempt to address some of these questions, we studied three groups of subjects with similar age and gender distributions: a group of 12 patients with colorectal carcinomas; a group of 12 patients with colorectal adenomas; and a group of eight healthy control subjects. Two experimental protocols were employed. In the first protocol, intrinsic DNA methylation was evaluated in neoplastic and in normal-appearing rectal mucosa of patients with colonic carcinomas or adenomas, compared with a group of healthy controls. In the second protocol, we examined, in a prospective and controlled fashion, the effect of folic acid supplementation (10 mg/day) on the degree of DNA methylation of rectal mucosa from those same patients after removal of the neoplasms. The degree of intrinsic DNA methylation was assessed on the basis of the capacity of the DNA isolates to serve as methyl acceptors in in vitro incubations that contained DNA methylase and [3H-methyl] S-adenosylmethionine. Intrinsic DNA methylation was significantly lower in carcinomas than in adenomas (P < 0.005). In addition, normal-appearing rectal mucosa from patients with carcinomas was significantly less methylated than in healthy controls (P < 0.005); the mean value found in the latter was also greater than the value observed in patients with adenomas, but not significantly so (P > 0.05).(ABSTRACT TRUNCATED AT 250 WORDS)

Adenoma↗
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↗
PubMed2005

Intracellular S-adenosylhomocysteine increased levels are associated with DNA hypomethylation in HUVEC.

Hyperhomocysteinemia is a risk factor for atherosclerosis and vascular disease; however, the mechanism underlying this association remains poorly understood. Increased levels of intracellular S-adenosylhomocysteine (AdoHcy), secondary to homocysteine-mediated reversal of the AdoHcy hydrolase reaction, have been associated with reduced DNA methylation patterns and pointed as responsible for the hyperhomocysteinemia-related endothelial dysfunction. Methylation is an epigenetic feature of genomic DNA, which leads to alterations in gene expression. So far, the effect of intracellular AdoHcy accumulation on DNA methylation patterns has not yet been fully substantiated by experimental evidence. The present study was designed to evaluate, in cultured endothelial cells, the effect of AdoHcy accumulation on genomic global DNA methylation status. Experimental intracellular accumulation of AdoHcy was induced by adenosine-2,3-dialdehyde (ADA), an inhibitor of AdoHcy hydrolase. Increased concentrations of inhibitor were tested, and unsupplemented medium incubations were used as controls. Cytosolic and nuclear fractions were obtained from trypsinized cells after 72 h of incubation. Total homocysteine concentration was quantified (culture medium and cytosolic fractions) by high-performance liquid chromatography (HPLC). S-Adenosylmethionine and AdoHcy concentrations were measured (cytosolic fractions) by stable-isotope dilution LC-tandem mass spectrometry method. Genomic DNA was obtained from the nuclear fraction, and global DNA methylation status was evaluated by the cytosine extension assay. The results showed that supplementation of the culture medium with ADA had no cytotoxic effect and increased the intracellular AdoHcy concentration in a dose-dependent manner. A significant negative correlation was observed between intracellular AdoHcy and genomic DNA methylation status. These findings strongly point to the importance of AdoHcy as a pivotal biomarker of genomic DNA methylation status.

Adenosine↗