EZH2 is overexpressed in adrenocortical carcinoma and is associated with disease progression
Bibliographic record
Abstract
Adrenal Cortex Carcinoma (ACC) is an aggressive tumour with poor prognosis. Common alterations in patients include constitutive WNT/β-catenin signalling and overexpression of the growth factor IGF2. However, the combination of both alterations in transgenic mice is not sufficient to trigger malignant tumour progression, suggesting that other alterations are required to allow development of carcinomas. Here, we have conducted a study of publicly available gene expression data from three cohorts of ACC patients to identify relevant alterations. Our data show that the histone methyltransferase EZH2 is overexpressed in ACC in the three cohorts. This overexpression is the result of deregulated P53/RB/E2F pathway activity and is associated with increased proliferation and poorer prognosis in patients. Inhibition of EZH2 by RNA interference or pharmacological treatment with DZNep inhibits cellular growth, wound healing and clonogenic growth and induces apoptosis of H295R cells in culture. Further growth inhibition is obtained when DZNep is combined with mitotane, the gold-standard treatment for ACC. Altogether, these observations suggest that overexpression of EZH2 is associated with aggressive progression and may constitute an interesting therapeutic target in the context of ACC.
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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 | 0.001 | 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".