Histone deacetylase (HDAC) 9: versatile biological functions and emerging roles in human cancer
Bibliographic record
Abstract
BACKGROUND: HDAC9 (histone deacetylase 9) belongs to the class IIa family of histone deacetylases. This enzyme can shuttle freely between the nucleus and cytoplasm and promotes tissue-specific transcriptional regulation by interacting with histone and non-histone substrates. HDAC9 plays an essential role in diverse physiological processes including cardiac muscle development, bone formation, adipocyte differentiation and innate immunity. HDAC9 inhibition or activation is therefore a promising avenue for therapeutic intervention in several diseases. HDAC9 overexpression is also common in cancer cells, where HDAC9 alters the expression and activity of numerous relevant proteins involved in carcinogenesis. CONCLUSIONS: This review summarizes the most recent discoveries regarding HDAC9 as a crucial regulator of specific physiological systems and, more importantly, highlights the diverse spectrum of HDAC9-mediated posttranslational modifications and their contributions to cancer pathogenesis. HDAC9 is a potential novel therapeutic target, and the restoration of aberrant expression patterns observed among HDAC9 target genes and their related signaling pathways may provide opportunities to the design of novel anticancer therapeutic strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".