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Record W2182989788 · doi:10.3389/fonc.2015.00264

Could the eIF2α-Independent Translation Be the Achilles Heel of Cancer?

2015· review· en· W2182989788 on OpenAlexafffund
Martin Holčı́k

Bibliographic record

VenueFrontiers in Oncology · 2015
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA regulation and disease
Canadian institutionsChildren's Hospital of Eastern OntarioUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Cancer Society Research InstituteCanadian Institutes of Health ResearchCancer Research Society
KeywordseIF2Eukaryotic initiation factorTranslation (biology)Internal ribosome entry siteEukaryotic translationInitiation factorIntegrated stress responseBiologyProtein biosynthesisCell biologyMechanism (biology)Eukaryotic translation initiation factor 4 gammaEIF4EGeneMessenger RNAGenetics

Abstract

fetched live from OpenAlex

Eukaryotic initiation factor eIF2 is a key component of the ternary complex whose role is to deliver initiator tRNA into the ribosome. A variety of stimuli, both physiologic and pathophysiologic activate eIF2 kinases that phosphorylate the α subunit of eIF2, preventing it from forming the ternary complex, thus attenuating cellular protein synthesis. Paradoxically, in cancer cells, the phosphorylation of eIF2α is associated with activation of survival pathways. This review explores the recently emerged novel mechanism of eIF2α-independent translation initiation. This mechanism, which appears to be shared by some RNA viruses and Internal Ribosome Entry Site-containing cellular mRNAs and utilizes auxiliary proteins, such as eIF5B, eIF2D, and MCT-1, is responsible for the selective translation of cancer-associated genes and could represent a weak point amenable to specific targeting for the treatment of 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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.002

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 head0.058
GPT teacher head0.387
Teacher spread0.329 · 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 source (direct Gemma or distilled Codex), 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

Citations63
Published2015
Admission routes2
Has abstractyes

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