Histone tail modifications and noncanonical functions of histones: perspectives in cancer epigenetics
Bibliographic record
Abstract
Over the past few years, the histone deacetylase (HDAC) inhibitors have occupied an important place in the effort to develop novel, but less toxic, anticancer therapy. HDAC inhibitors block HDACs, which are the enzymes responsible for histone deacetylation, and therefore they modulate gene expression. The cellular effects of HDAC inhibitors include growth arrest and the induction of differentiation. Early successes in cancer therapeutics obtained using these drugs alone or in combination with other anticancer drugs emphasize the important place of posttranslational modifications of histones in cancer therapy. Histone tail modifications along with DNA methylation are the most studied epigenetic events related to cancer progression. Moreover, extranuclear functions of histones have also been described. Because HDAC inhibitors block HDACs and thereby increase histone acetylation, we propose a model wherein exogenous acetylated histones or other related acetylated proteins that are introduced into the nucleus become HDAC substrates and thereby compete with endogenous histones for HDACs. This competition may lead to the increased acetylation of the endogenous histones, as in the case of HDAC inhibitor therapy. Moreover, other mechanisms of action, such as binding to chromatin and modulating gene expression, are also possible for exogenously introduced histones.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".