Causes and consequences of DNA hypomethylation in human cancer
Bibliographic record
Abstract
While specific genes are hypermethylated in the genome of cancer cells, overall methylcytosine content is often decreased as a consequence of hypomethylation affecting many repetitive sequences. Hypomethylation is also observed at a number of single-copy genes. While global hypomethylation is highly prevalent across all cancer types, it often displays considerable specificity with regard to tumor type, tumor stage, and sequences affected. Following an overview of hypomethylation alterations in various cancers, this review focuses on 3 hypotheses. First, hypomethylation at a single-copy gene may occur as a 2-step process, in which selection for gene function follows upon random hypo methylation. In this fashion, hypomethylation facilitates the adaptation of cancer cells to the ever-changing tumor tissue microenvironment, particularly during metastasis. Second, the development of global hypomethylation is intimately linked to chromatin restructuring and nuclear disorganization in cancer cells, reflected in a large number of changes in histone-modifying enzymes and other chromatin regulators. Third, DNA hypomethylation may occur at least partly as a consequence of cell cycle deregulation disturbing the coordination between DNA replication and activity of DNA methyltransferases. Finally, because of their relation to tumor progression and metastasis, DNA hypomethylation markers may be particularly useful to classify cancer and predict their clinical course.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".