ReviewEpigenetics2025
Advancements in DNA methylation technologies and their application in cancer diagnosis.
Review in Epigenetics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- Epigenetic Alterations in Meningiomas-A Review.Biomedicines · 2026Review
- Integrative Epigenomics: Bioinformatics Strategies for Multi-Omics Data Analysis in Health and Disease.Epigenomes · 2026Review
- Artificial Intelligence in Oncology: A Comprehensive Cross-Cancer Translational Readiness Analysis Across 18 Malignancies.Cancers · 2026Review
- The crosstalk between epigenetics and metabolism in the malignant cell.Discover oncology · 2026Review
- DNA Methylation and Its Role in Personalized Nutrition: Mechanisms, Clinical Insights, and Future Perspectives.International journal of molecular sciences · 2026Review
- Mechanical forces orchestrate the epigenetic landscape of oral mesenchymal stem/progenitor cell fate in dental and periodontal tissues.Frontiers in cell and developmental biology · 2026Review
- Circulating methylated promoters of HK2 and EGFR as biomarkers in the early detection of cancer.Frontiers in oncology · 2026Article
- Review
- DNA methylation and machine learning: challenges and perspective toward enhanced clinical diagnostics.Clinical epigenetics · 2025Review
- Methylation profiling in neuropathological tumors diagnosis: a comprehensive review.Frontiers in oncology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
DNA methylation is a common epigenetic modification that maintains the integrity of the DNA sequence while profoundly influencing gene expression and phenotypic variation. Aberrant DNA methylation has been associated with the onset and progression of diseases, including cancer, metabolic disorders, and neurodevelopmental disorders. Recent advancements in detection technology led to a gradual increase in the exploration of DNA methylation as a valuable biomarker for cancer diagnosis and therapy. Single-base resolution has been achieved for whole-genome methylation analyses through second-generation sequencing technology, significantly enhancing detection efficiency. Additionally, PCR-based methods offer simple and feasible solutions for methylation analysis. In this review, we discuss various methods for detecting DNA methylation, focusing on bisulfite conversion-based techniques, methylation-sensitive restriction enzyme methods, enzyme conversion-based methods, third-generation sequencing approaches, and artificial intelligence. Furthermore, we briefly summarize the methylation biomarkers used for tumor diagnosis and the corresponding sample types employed. We believe that this information provides valuable insights for selecting and optimizing DNA methylation analysis tools.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.