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Record W4410415556 · doi:10.1186/s13046-025-03412-9

Genome-Wide Methylation Sequencing to Identify DNA Methylation Markers for Early-stage Hepatocellular Carcinoma in Liver and Blood

2025· article· en· W4410415556 on OpenAlexaff
Siyu Fu, Ruben Boers, Joachim Boers, Pam Elisabeth van der Meeren, Jean Helmijr, Vanja de Weerd, Michail Doukas, Maurice Jansen, Bettina E. Hansen, Roeland F. de Wilde, Dave Sprengers, Joost Gribnau, Saskia M. Wilting, José D. Debes, André Boonstra

Bibliographic record

VenueJournal of Experimental & Clinical Cancer Research · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEpigenetics and DNA Methylation
Canadian institutionsUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsHepatocellular carcinomaDNA methylationCirrhosisMethylationInternal medicineCarcinomaLiver cancerMedicineBiologyGastroenterologyOncologyGeneGeneticsGene expression

Abstract

fetched live from OpenAlex

BACKGROUND: Hepatocellular carcinoma (HCC) is associated with a poor 5-year survival mainly due to detection at late stages. Better non-invasive surveillance methods are needed to improve early detection and maximize survival. We performed a strict assessment of DNA methylation markers (DMMs) for HCC detection. METHODS: A total of 385 samples from liver tissues and blood were analyzed. Genome-wide Methylated DNA sequencing (MeD-seq) was initially performed on 46 liver tissues, followed by the validation using quantitative methylation-specific PCR (qMSP) on 175 liver tissues. The selected DMMs with and without ASAP/GAAD score were further evaluated in 180 blood samples. Additionally, MeD-seq was performed to validate the results on blood. RESULTS: MeD-seq revealed a substantial number of differentially methylated regions (DMRs) in HCC tissues compared to non-HCC controls. By qMSP, the top 5 DMMs demonstrated strong performance in distinguishing cirrhotic HCC from cirrhosis controls in tissue (AUC 0.842 to 0.957). However, evaluation of these DMMs in blood showed lower performance in early HCC detection compared to cirrhosis in both the training (sensitivity 26.7-43.3%, 81.3% specificity) and validation cohorts (sensitivity 16.2-43.2%, 85.7% specificity). The addition of DMMs to the ASAP/GAAD score only provided an additional 5.4% sensitivity in the validation cohort compared to the ASAP/GAAD score alone. These findings were confirmed using MeD-seq analysis in blood samples, which revealed no detectable DMRs between cirrhotic HCC and cirrhosis controls. Interestingly, DNA methylation patterns in blood of healthy individuals differed strongly from both groups (cirrhosis and cirrhotic HCC). CONCLUSION: DNA methylation patterns in liver tissue were distinctly different between HCC and controls. In blood, DMMs contributed minimally to early-stage HCC detection compared to cirrhosis, whether used alone or in combination with the ASAP/GAAD score. It is likely that high baseline DNA methylation related to cirrhosis and possibly the low input of tumor-related DNA impacts the use of DMMs in early HCC detection in blood.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.069
Threshold uncertainty score0.485

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.123
GPT teacher head0.480
Teacher spread0.357 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations7
Published2025
Admission routes1
Has abstractyes

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