What deubiquitinating enzymes, oncogenes, and tumor suppressors actually do: Are current assumptions supported by patient outcomes?
Bibliographic record
Abstract
Context can determine whether a given gene acts as an oncogene or a tumor suppressor. Deubiquitinating enzymes (DUBs) regulate the stability of many components of the pathways dictating cell fate so it would be expected that alterations in the levels or activity of these enzymes may have oncogenic or tumor suppressive consequences. In the current review we survey publications reporting that genes encoding DUBs are oncogenes or tumor suppressors. For many DUBs both claims have been made. For such "double agents," the effects of gain or loss of function will depend on the overall status of a complex of molecular signaling networks subject to extensive crosstalk. As the TGF-β paradox makes clear context is critical in cell fate decisions, and the disconnect between experimental findings and patient survival outcomes can in part be attributed to disparities between culture conditions and the microenvironment in vivo. Convincing claims for oncogene or tumor suppressor roles require the documentation of gene alterations in patient samples; survival curves are alone inadequate.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".