Roles of Histone Deacetylases and Inhibitors in Anticancer Therapy
Bibliographic record
Abstract
Histones are the main structural proteins of eukaryotic chromatin. Histone acetylation/ deacetylation are the epigenetic mechanisms of the regulation of gene expression and are catalyzed by histone acetyltransferases (HAT) and histone deacetylases (HDAC). These epigenetic alterations of DNA structure influence the action of transcription factors which can induce or repress gene transcription. The HATs catalyze acetylation and the events related to gene transcription and are also responsible for transporting newly synthesized histones from the cytoplasm to the nucleus. The activity of HDACs is mainly involved in silencing gene expression and according to their specialized functions are divided into classes I, II, III and IV. The disturbance of the expression and mutations of HDAC genes causes the aberrant transcription of key genes regulating important cancer pathways such as cell proliferation, cell-cycle regulation and apoptosis. In view of their role in cancer pathways, HDACs are considered promising therapeutic targets and the development of HDAC inhibitors is a hot topic in the search for new anticancer drugs. The present review will focus on HDACs I, II and IV, the best known inhibitors and potential alternative inhibitors derived from natural and synthetic products which can be used to influence HDAC activity and the development of new cancer therapies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".