Definition of the Landscape of Promoter DNA Hypomethylation in Liver Cancer
Bibliographic record
Abstract
We use hepatic cellular carcinoma (HCC), one of the most common human cancers, as a model to delineate the landscape of promoter hypomethylation in cancer. Using a combination of methylated DNA immunoprecipitation and hybridization with comprehensive promoter arrays, we have identified approximately 3,700 promoters that are hypomethylated in tumor samples. The hypomethylated promoters appeared in clusters across the genome suggesting that a high-level organization underlies the epigenomic changes in cancer. In normal liver, most hypomethylated promoters showed an intermediate level of methylation and expression, however, high-CpG dense promoters showed the most profound increase in gene expression. The demethylated genes are mainly involved in cell growth, cell adhesion and communication, signal transduction, mobility, and invasion; functions that are essential for cancer progression and metastasis. The DNA methylation inhibitor, 5-aza-2'-deoxycytidine, activated several of the genes that are demethylated and induced in tumors, supporting a causal role for demethylation in activation of these genes. Previous studies suggested that MBD2 was involved in demethylation of specific human breast and prostate cancer genes. Whereas MBD2 depletion in normal liver cells had little or no effect, we found that its depletion in human HCC and adenocarcinoma cells resulted in suppression of cell growth, anchorage-independent growth and invasiveness as well as an increase in promoter methylation and silencing of several of the genes that are hypomethylated in tumors. Taken together, the findings define the potential functional role of hypomethylation in cancer.
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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".