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Record W2766121661 · doi:10.1016/j.jbior.2017.11.001

Dysregulation of mRNA translation and energy metabolism in cancer

2017· review· en· W2766121661 on OpenAlexafffund
Matthew Leibovitch, Ivan Topisirović

Bibliographic record

VenueAdvances in Biological Regulation · 2017
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Hypoxia, and Metabolism
Canadian institutionsMcGill University
FundersFonds de Recherche du Québec - SantéTerry Fox Research InstituteNational Cancer InstituteNational Institutes of HealthProstate Cancer CanadaCanadian Cancer Society Research InstituteCanadian Institutes of Health ResearchCancer Research Society
KeywordsPI3K/AKT/mTOR pathwayTranslation (biology)AMPKCancerProtein biosynthesisTranslational regulationMechanistic target of rapamycinMessenger RNABiologyCancer cellComputational biologyCell biologyCancer researchBioinformaticsSignal transductionGeneticsPhosphorylationGeneProtein kinase A

Abstract

fetched live from OpenAlex

Dysregulated mRNA translation and aberrant energy metabolism are frequent in cancer. Considering that mRNA translation is an energy demanding process, cancer cells must produce sufficient ATP to meet energy demand of hyperactive translational machinery. In recent years, the mammalian/mechanistic target of rapamycin (mTOR) emerged as a central regulatory node which coordinates energy consumption by the translation apparatus and ATP production in mitochondria. Aberrant mTOR signaling underpins the vast majority of cancers whereby increased mTOR activity is thought to be a major determinant of both malignant translatomes and metabolomes. Nonetheless, the role of mTOR and other related signaling nodes (e.g. AMPK) in orchestrating protein synthesis and cancer energetics is only recently being unraveled. In this review, we discuss recent findings that provide insights into the molecular underpinnings of coordination of translational and metabolic programs of cancer cells, and potential strategies to translate these findings into clinical treatments.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.998
Threshold uncertainty score0.892

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 integrity0.0010.000
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 head0.069
GPT teacher head0.374
Teacher spread0.305 · 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 designOther design
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

Citations50
Published2017
Admission routes2
Has abstractyes

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