ArticleJournal of translational medicine2026
Methylation entropy as a novel dimension in liquid biopsy: enhanced multimodal framework for cancer detection and tissue-of-origin classification.
Article in Journal of translational medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
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1 citing paper in PubMed, 1 synthesis or guideline pooled it.
- IBD-related inflammatory memory and colorectal cancer: epigenetic mechanisms and microbiome interventions.Frontiers in immunology · 2026Pooled it
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19 authors.
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Abstract
backgroundCirculating cell-free DNA (cfDNA) is a promising biomarker for non-invasive early cancer detection. We integrated methylation entropy, a measure of DNA methylation heterogeneity, with other cfDNA features into a multimodal model to improve multi-cancer detection.
methodsA customized panel named HYGEIA, containing abundant informative methylation CpG sites, was first developed. The panel’s performance was validated, and a method for calculating fragment-level methylation entropy was established. Subsequently, this panel was utilized to perform deep panel-targeted bisulfite sequencing on 521 plasma samples from 288 cancer patients and 233 non-cancer individuals. Participants were randomly allocated into training (197 cancer, 157 non-cancer) and testing (91 cancer, 76 non-cancer) sets for constructing the Enhanced Multi-Cancer Early Detection (EMCED) model. Upon validation of the EMCED model, a Tissue-of-Origin (TOO) model was further developed and evaluated across seven cancer types.
resultsA classifier based solely on methylation entropy achieved an AUC of 0.938 (sensitivity 78.0%, specificity 94.7%) in the testing set for multi-cancer detection. Building on this, two additional characteristics, DNA methylation levels and fragmentation features, were integrated into the model, resulting in the EMCED model, which achieved an AUC of 0.979 (sensitivity 93.4%, specificity 93.4%) in the testing set. Analysis of the EMCED model’s sensitivity by cancer type showed detection rates of 95.5% for colorectal cancer, 98.0% for lung cancer, 93.5% for gastric cancer, 83.8% for ovarian cancer, and 100% for liver, esophageal, and thyroid cancers. The TOO model, which incorporated all three feature types, methylation entropy, methylation levels, and fragmentation features, achieved an accuracy of 92.3% for classifying the tissue of origin among cancer cases in the testing set.
conclusionsMethylation entropy is a robust cfDNA biomarker for distinguishing cancer from non-cancer. The multimodal model enables enhanced multi-cancer detection and accurate classification, supporting the integration of methylation entropy for improved non-invasive early cancer detection.
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