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Record W2132519593 · doi:10.1101/gad.1604407

Dissecting eIF4E action in tumorigenesis

2007· article· en· W2132519593 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueGenes & Development · 2007
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPI3K/AKT/mTOR signaling in cancer
Canadian institutionsMcGill University
FundersNational Cancer InstituteCold Spring Harbor LaboratoryLeukemia and Lymphoma SocietyHoward Hughes Medical Institute
KeywordsEIF4ECarcinogenesisBiologyCancer researchTranslation (biology)OncogenePhosphorylationMutantEukaryotic translationCancerCell biologyGeneGeneticsMessenger RNACell cycle

Abstract

fetched live from OpenAlex

Genetically engineered mouse models are powerful tools for studying cancer genes and validating targets for cancer therapy. We previously used a mouse lymphoma model to demonstrate that the translation initiation factor eIF4E is a potent oncogene in vivo. Using the same model, we now show that the oncogenic activity of eIF4E correlates with its ability to activate translation and become phosphorylated on Ser 209. Furthermore, constitutively activated MNK1, an eIF4E Ser 209 kinase, promotes tumorigenesis in a manner similar to eIF4E, and a dominant-negative MNK mutant inhibits the in vivo proliferation of tumor cells driven by mutations that deregulate translation. Phosphorylated eIF4E promotes tumorigenesis primarily by suppressing apoptosis and, accordingly, the anti-apoptotic protein Mcl-1 is one target of both phospho-eIF4E and MNK1 that contributes to tumor formation. Our results provide insight into how eIF4E contributes to tumorigenesis and pinpoint a level of translational control that may be suitable for therapeutic intervention.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.111
Threshold uncertainty score0.629

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.022
GPT teacher head0.302
Teacher spread0.280 · 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