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Record W3119982255 · doi:10.1002/bies.202000269

What deubiquitinating enzymes, oncogenes, and tumor suppressors actually do: Are current assumptions supported by patient outcomes?

2021· review· en· W3119982255 on OpenAlexaff
Sophie Gregoire‐Mitha, Douglas A. Gray

Bibliographic record

VenueBioEssays · 2021
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicUbiquitin and proteasome pathways
Canadian institutionsOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsDeubiquitinating enzymeSuppressorOncogeneBiologyCrosstalkTumor suppressor geneContext (archaeology)Cancer researchGeneTumor microenvironmentCarcinogenesisCell biologyUbiquitinGeneticsTumor cellsCell cycle

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.971
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.038
GPT teacher head0.327
Teacher spread0.289 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations4
Published2021
Admission routes1
Has abstractyes

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