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Record W2127049578 · doi:10.1101/gad.1756509

Tumor suppressors and cell metabolism: a recipe for cancer growth

2009· review· en· W2127049578 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueGenes & Development · 2009
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Hypoxia, and Metabolism
Canadian institutionsMcGill UniversityMcGill University Health Centre
FundersNational Cancer InstituteCanadian Institutes of Health ResearchNational Institutes of HealthMcGill University
KeywordsBiologyCarcinogenesisSuppressorCell growthCancer cellCancerCell biologyBioenergeticsIntracellularCell metabolismCancer researchCellGeneticsMitochondrion

Abstract

fetched live from OpenAlex

Growing tumors face two major metabolic challenges-how to meet the bioenergetic and biosynthetic demands of increased cell proliferation, and how to survive environmental fluctuations in external nutrient and oxygen availability when tumor growth outpaces the delivery capabilities of the existing vasculature. Cancer cells display dramatically altered metabolic circuitry that appears to directly result from the oncogenic mutations selected during the tumorigenic process. An emerging theme in cancer biology is that many of the genes that can initiate tumorigenesis are intricately linked to metabolic regulation. In turn, it appears that a number of well-established tumor suppressors play critical roles in suppressing growth and/or proliferation when intracellular supplies of essential metabolites become reduced. In this review, we consider the potential role of tumor suppressors as metabolic regulators.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.985
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
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 head0.023
GPT teacher head0.291
Teacher spread0.268 · 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