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Record W4406364748 · doi:10.1261/rna.080340.124

eIF4F-mediated dysregulation of mRNA translation in cancer

2025· review· en· W4406364748 on OpenAlexafffund
Mehdi Amiri, Niaz Mahmood, Soroush Tahmasebi, Nahum Sonenberg

Bibliographic record

VenueRNA · 2025
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPI3K/AKT/mTOR signaling in cancer
Canadian institutionsMcGill University
FundersNational Heart, Lung, and Blood InstituteFonds de Recherche du Québec - SantéNational Institutes of HealthLEO FondetCancer Research Society
KeywordsProteostasisBiologyTranslation (biology)Messenger RNAEIF4ECancerProtein biosynthesisTranslational regulationCell biologyCancer researchGeneticsGene

Abstract

fetched live from OpenAlex

Messenger RNA (mRNA) translational control plays a pivotal role in regulating cellular proteostasis under physiological and pathological conditions. Dysregulated mRNA translation is pervasive in cancer, in which protein synthesis is elevated to support accelerated cell growth and proliferation. Consequently, targeting the mRNA translation machinery has emerged as a therapeutic strategy to treat cancer. In this Perspective, we summarize the current knowledge of translation dysregulation in cancer, with emphasis on the eukaryotic translation initiation factor 4F complex. We outline recent endeavors to apply this knowledge to develop novel treatment strategies to combat cancer.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.988
Threshold uncertainty score0.824

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.040
GPT teacher head0.364
Teacher spread0.324 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations11
Published2025
Admission routes2
Has abstractyes

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