Expression of Class I Histone Deacetylases Indicates Poor Prognosis in Endometrioid Subtypes of Ovarian and Endometrial Carcinomas
Bibliographic record
Abstract
Histone deacetylase (HDAC) inhibitors are an emerging class of targeted cancer therapeutics, and little is known about HDAC expression in gynecologic malignancies. Therefore, we tested the hypothesis whether high-level expression of class 1 HDACs (HDAC1, 2, and 3) is associated with clinically distinct subsets of ovarian and endometrial carcinomas. Expression was assessed by immunohistochemistry in a population-based cohort of 465 ovarian and 149 endometrial carcinomas and correlated with clinicopathologic parameters. Each of the HDACs was expressed at high levels in most ovarian (HDAC1, 61%; HDAC2, 93%; HDAC3, 84%) and endometrial (HDAC1, 61%; HDAC2, 95%; HDAC3, 83%) carcinomas. Further, 55% and 56% of ovarian and endometrial carcinomas, respectively, expressed all three HDACs at high levels. Such cases were less common among endometrioid subtypes of ovarian and endometrial carcinomas (36% and 52% positive cases, respectively) compared with high-grade serous subtypes (64 and 69%, respectively, P < .001). High-level expression of all three HDACs is associated with a poor prognosis in ovarian endometrioid carcinomas (hazard ratio, 6.7; 95% confidence interval, 1.9-23.3). The independent prognostic information and the overall high rate of expression for class I HDACs suggest that these targets should be explored as predictive factors in ovarian and endometrial carcinomas prospectively.
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 | 0.001 | 0.002 |
| 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) | 0.001 | 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 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".