ArticleClinical epigenetics2024
Prediction of methylation status using WGS data of plasma cfDNA for multi-cancer early detection (MCED).
Article in Clinical epigenetics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Development, characterization, and inter-laboratory validation of methylated human cell free DNA candidate reference materials.Clinical epigenetics · 2026Article
- Detection of primary cancer types via fragment size selection in circulating cell-free extrachromosomal circular DNA.Genome medicine · 2026Article
- Methylated CfDNA may distinguish between high- and intermediate-risk uveal melanoma: a pilot study.Cancer cell international · 2025Article
- Article
- Improvement of the sensitivity of circulating tumor DNA-based liquid biopsy: current approaches and future perspectives.Exploration of targeted anti-tumor therapy · 2025Review
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Authors and funding
11 authors.
Funding
Abstract
backgroundCell-free DNA (cfDNA) contains a large amount of molecular information that can be used for multi-cancer early detection (MCED), including changes in epigenetic status of cfDNA, such as cfDNA fragmentation profile. The fragmentation of cfDNA is non-random and may be related to cfDNA methylation. This study provides clinical evidence for the feasibility of inferring cfDNA methylation levels based on cfDNA fragmentation patterns. We performed whole-genome bisulfite sequencing and whole-genome sequencing (WGS) on both healthy individuals and cancer patients. Using the information of whole-genome methylation levels, we investigated cytosine-phosphate-guanine (CpG) cleavage profile and validated the method of predicting the methylation level of individual CpG sites using WGS data.
resultsWe conducted CpG cleavage profile biomarker analysis on data from both healthy individuals and cancer patients. We obtained unique or shared potential biomarkers for each group and built models accordingly. The modeling results proved the feasibility to predict the methylation status of single CpG sites in cfDNA using cleavage profile model from WGS data.
conclusionBy combining cfDNA cleavage profile of CpG sites with machine learning algorithms, we have identified specific CpG cleavage profile as biomarkers to predict the methylation status of individual CpG sites. Therefore, methylation profile, a widely used epigenetic biomarker, can be obtained from a single WGS assay for MCED.
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