HDAC5 and HDAC9 in Medulloblastoma: Novel Markers for Risk Stratification and Role in Tumor Cell Growth
Bibliographic record
Abstract
PURPOSE: Medulloblastomas are the most common malignant brain tumors in childhood. Survivors suffer from high morbidity because of therapy-related side effects. Thus, therapies targeting tumors in a specific manner with small molecules such as histone deacetylase (HDAC) inhibitors are urgently warranted. This study investigated the expression levels of individual human HDAC family members in primary medulloblastoma samples, their potential as risk stratification markers, and their roles in tumor cell growth. EXPERIMENTAL DESIGN: Gene expression arrays were used to screen for HDAC1 through HDAC11. Using quantitative real time reverse transcriptase-PCR and immunohistochemistry, we studied the expression of HDAC5 and HDAC9 in primary medulloblastoma samples. In addition, we conducted functional studies using siRNA-mediated knockdown of HDAC5 and HDAC9 in medulloblastoma cells. RESULTS: HDAC5 and HDAC9 showed the highest expression in prognostically poor subgroups. This finding was validated in an independent set of medulloblastoma samples. High HDAC5 and HDAC9 expression was significantly associated with poor overall survival, with high HDAC5 and HDAC9 expression posing an independent risk factor. Immunohistochemistry revealed a strong expression of HDAC5 and HDAC9 proteins in most of all primary medulloblastomas investigated. siRNA-mediated knockdown of HDAC5 or HDAC9 in medulloblastoma cells resulted in decreased cell growth and cell viability. CONCLUSION: HDAC5 and HDAC9 are significantly upregulated in high-risk medulloblastoma in comparison with low-risk medulloblastoma, and their expression is associated with poor survival. Thus, HDAC5 and HDAC9 may be valuable markers for risk stratification. Because our functional studies point toward a role in medulloblastoma cell growth, HDAC5 and HDAC9 may potentially be novel drug targets.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".