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Record W1996022540 · doi:10.1073/pnas.1410609111

TAp73 opposes tumor angiogenesis by promoting hypoxia-inducible factor 1α degradation

2014· article· en· W1996022540 on OpenAlexaff
Ivano Amelio, Satoshi Inoue, Elke Markert, Arnold J. Levine, Richard A. Knight, Tak W. Mak, Gerry Melino

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Hypoxia, and Metabolism
Canadian institutionsUniversity Health NetworkOntario Institute for Cancer Research
FundersNational Cancer Institute
KeywordsAngiogenesisTranscription factorHypoxia-Inducible Factor 1Hypoxia (environmental)Cancer researchHypoxia-inducible factorsUbiquitinHIF1AProtein subunitBiologyTumor hypoxiaIn vivoTumor progressionCancerCell biologyChemistryInternal medicineMedicineOxygenBiochemistryGeneGenetics

Abstract

fetched live from OpenAlex

Tumor hypoxia and hypoxia-inducible factor 1 (HIF-1) activation are associated with cancer progression. Here, we demonstrate that the transcription factor TAp73 opposes HIF-1 activity through a nontranscriptional mechanism, thus affecting tumor angiogenesis. TAp73-deficient mice have an increased incidence of spontaneous and chemically induced tumors that also display enhanced vascularization. Mechanistically, TAp73 interacts with the regulatory subunit (α) of HIF-1 and recruits mouse double minute 2 homolog into the protein complex, thus promoting HIF-1α polyubiquitination and consequent proteasomal degradation in an oxygen-independent manner. In human lung cancer datasets, TAp73 strongly predicts good patient prognosis, and its expression is associated with low HIF-1 activation and angiogenesis. Our findings, supported by in vivo and clinical evidence, demonstrate a mechanism for oxygen-independent HIF-1 regulation, which has important implications for individualizing therapies in patients with 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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.294

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.028
GPT teacher head0.279
Teacher spread0.251 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations105
Published2014
Admission routes1
Has abstractyes

Explore more

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