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Record W3034143654 · doi:10.3390/ijms21114075

Regulation of Epithelial-to-Mesenchymal Transition by Alternative Translation Initiation Mechanisms and Its Implications for Cancer Metastasis

2020· review· en· W3034143654 on OpenAlexafffund
Amit Bera, Stephen M. Lewis

Bibliographic record

VenueInternational Journal of Molecular Sciences · 2020
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA modifications and cancer
Canadian institutionsBeatrice Hunter Cancer Research InstituteUniversité de MonctonAtlantic Cancer Research Institute
FundersCancer Research Society
KeywordsInternal ribosome entry siteTranslation (biology)MetastasisEpithelial–mesenchymal transitionEukaryotic translationBiologyCancer researchCancerProtein biosynthesisCell biologyMessenger RNAGeneGenetics

Abstract

fetched live from OpenAlex

Translation initiation plays a critical role in the regulation of gene expression for development and disease conditions. During the processes of development and disease, cells select specific mRNAs to be translated by controlling the use of diverse translation initiation mechanisms. Cells often switch translation initiation from a cap-dependent to a cap-independent mechanism during epithelial-to-mesenchymal transition (EMT), a process that plays an important role in both development and disease. EMT is involved in tumor metastasis because it leads to cancer cell migration and invasion, and is also associated with chemoresistance. In this review we will provide an overview of both the internal ribosome entry site (IRES)-dependent and N6-methyladenosine (m6A)-mediated translation initiation mechanisms and discuss how cap-independent translation enables cells from primary epithelial tumors to achieve a motile mesenchymal-like phenotype, which in turn drives tumor metastasis.

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)0.000
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.061
GPT teacher head0.377
Teacher spread0.316 · 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

Citations20
Published2020
Admission routes2
Has abstractyes

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