Drug-induced inactivation or gene silencing of class I histone deacetylases suppresses ovarian cancer cell growth: Implications for therapy
Bibliographic record
Abstract
There is an urgent need to develop new strategies to treat ovarian cancer, the most deadly gynecologic malignancy. Histone deacetylase (HDAC) inhibitors are emerging as novel therapeutic drugs in the treatment of a variety of cancers, including those resistant to standard chemotherapy. Since there are multiple HDAC isoforms, determining the precise role of individual HDAC isoenzymes in the growth and progression of ovarian cancer has the potential to influence the use of selective HDAC inhibitors as strategic therapeutic agents that elicit fewer undesirable side effects. Unfortunately, there is limited information about the expression of HDAC isoforms in human ovarian tissues. This report provides evidence for the first time that Class I HDACs are expressed at significantly higher levels in ovarian cancers in comparison to normal ovarian tissues, with no significant difference in Class II HDAC expression between the two groups. Furthermore, ovarian cancer cells are far more sensitive than normal ovarian cells to the potent HDAC inhibitor romidepsin (FK228), a drug that displays greater inhibitory selectivity for Class I HDACs over Class II isoforms. Using small interfering RNA (siRNA) methodology, we demonstrate that knocking down the gene expression of HDAC3 and other members of the Class I HDAC family suppresses ovarian cancer cell growth. Taken together, the present studies offer several novel findings that have direct relevance for the strategic use of inhibitors that target Class I HDACs, particularly HDAC3, in the treatment of ovarian 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".