The epigenetic basis of hepatocellular carcinoma – mechanisms and potential directions for biomarkers and therapeutics
Bibliographic record
Abstract
Hepatocellular carcinoma (HCC) is the sixth leading cancer worldwide and has complex pathogenesis due to its heterogeneity, along with poor prognoses. Diagnosis is often late as current screening methods have limited sensitivity for early HCC. Moreover, current treatment regimens for intermediate-to-advanced HCC have high resistance rates, no robust predictive biomarkers, and limited survival benefits. A deeper understanding of the molecular biology of HCC may enhance tumor characterization and targeting of key carcinogenic signatures. The epigenetic landscape of HCC includes complex hallmarks of 1) global DNA hypomethylation of oncogenes and hypermethylation of tumor suppressors; 2) histone modifications, altering chromatin accessibility to upregulate oncogene expression, and/or suppress tumor suppressor gene expression; 3) genome-wide rearrangement of chromatin loops facilitating distal enhancer-promoter oncogenic interactions; and 4) RNA regulation via translational repression by microRNAs (miRNAs) and RNA modifications. Additionally, it is useful to consider etiology-specific epigenetic aberrancies, especially in viral hepatitis and metabolic dysfunction-associated steatotic liver disease (MASLD), which are the main risk factors of HCC. This article comprehensively explores the epigenetic signatures in HCC, highlighting their potential as biomarkers and therapeutic targets. Additionally, we examine how etiology-specific epigenetic patterns and the integration of epigenetic therapies with immunotherapy could advance personalized HCC treatment strategies.
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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".