DNA Methylation Signatures Reveal the Diversity of Processes Remodeling Hepatocellular Carcinoma Methylomes
Bibliographic record
Abstract
BACKGROUND AND AIMS: DNA methylation patterns are highly rearranged in HCCs. However, diverse sources of variation are intermingled in cancer methylomes, precluding the precise characterization of underlying molecular mechanisms. We developed a computational framework (methylation signature analysis with independent component analysis [MethICA]) leveraging independent component analysis to disentangle the diverse processes contributing to DNA methylation changes in tumors. APPROACH AND RESULTS: Applied to a collection of 738 HCCs, MethICA unraveled 13 stable methylation components preferentially active in specific chromatin states, sequence contexts, and replication timings. These included signatures of general processes associated with sex and age but also signatures related to specific driver events and molecular subgroups. Catenin beta 1 mutations were major modulators of methylation patterns in HCC, characterized by a targeted hypomethylation of transcription factor 7-bound enhancers in the vicinity of Wnt target genes as well as a widespread hypomethylation of late-replicated partially methylated domains. By contrast, demethylation of early replicated highly methylated domains was a signature of replication stress, leading to an extensive hypomethylator phenotype in cyclin-activated HCC. Inactivating mutations of the chromatin remodeler AT-rich interactive domain-containing protein 1A were associated with epigenetic silencing of differentiation-promoting transcriptional networks, also detectable in cirrhotic liver. Finally, a hypermethylation signature targeting polycomb-repressed chromatin domains was identified in the G1 molecular subgroup with progenitor features. CONCLUSIONS: This study elucidates the diversity of processes remodeling HCC methylomes and reveals the epigenetic and transcriptional impact of driver alterations.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".