Comparison of the antineoplastic action of 3-deazaneplanocin-A and inhibitors that target the catalytic site of EZH2 histone methyltransferase
Bibliographic record
Abstract
EZH2 is the histone methyltransferase (HMT) that catalyzes the trimethylation of histone H3 lysine 27 (H3K27me3), a histone marker that silences gene expression.Overexpression of EZH2 enhances the growth of malignant cells due to silencing of tumor suppressor genes (TSGs).3-deazaneplanocin-A (DZNep) blocks the metabolism of methionine resulting in global inhibition of HMTs, including EZH2.This action of DZNep leads to inhibition of growth of malignant cells and reactivation of TSGs.On the other hand, specific inhibitors that target the catalytic site of EZH2: GSK-126, GSK-343, CPI-1205, and tazemetostat (EPZ-6438) were also investigated and exhibited interesting antineoplastic activity.These studies indicated that their anticancer action required a longer duration of treatment than DZNep to exhibit significant antineoplastic activity.This observation suggests that DZNep is a more potent antineoplastic agent than the specific EZH2 inhibitors.Such a difference in anticancer potency may be explained in part by the limited penetration into cells of the specific EZH2 inhibitors due to their large complex molecular structure as compared to the smaller molecular size of DZNep.An additional explanation is that DZNep has several targets in the cell which contribute to its anticancer action: deregulation of methionine metabolism, proteosomal degradation of EZH2, and activation of miRNAs with TSG function.In this study, we compared the in vitro antineoplastic action of DZNep and the specific EZH2 inhibitors using growth inhibition and colony assays on leukemic cells.These assays confirm that DZNep is a more potent anticancer agent than the specific EZH2 inhibitors.DZNep merits clinical investigation in patients with cancer.
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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) | 0.000 | 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".