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Record W2133164153 · doi:10.1158/0008-5472.can-05-2560

Characterization of a von Hippel Lindau Pathway Involved in Extracellular Matrix Remodeling, Cell Invasion, and Angiogenesis

2006· article· en· W2133164153 on OpenAlexaff
Ghada Kurban, Valérie Hudon, Eric Duplan, Michael Ohh, Arnim Pause

Bibliographic record

VenueCancer Research · 2006
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Hypoxia, and Metabolism
Canadian institutionsUniversity of TorontoMcGill UniversityMcGill University Health Centre
Fundersnot available
KeywordsAngiogenesisExtracellular matrixHypoxia-inducible factorsVascular endothelial growth factorCancer researchCell biologyBiologyMatrix metalloproteinaseGrowth factorPathologyChemistryMedicineGeneBiochemistryReceptor

Abstract

fetched live from OpenAlex

Inactivation of the von Hippel-Lindau (VHL) tumor suppressor gene results in highly vascularized tumors, making the VHL tumor syndrome an ideal system to study the mechanisms of angiogenesis. VHL operates along two pathways with the first involving hypoxia-inducible factor-alpha degradation and down-regulation of its proangiogenic target genes vascular endothelial growth factor and platelet-derived growth factor-beta, and the second pathway promoting extracellular matrix (ECM) assembly. Secretion of proangiogenic factors was shown to be a primary inducer of angiogenesis. Here, we show that loss of ECM assembly correlates with tumor angiogenesis in VHL disease. Upon inactivation of the VHL-ECM assembly pathway, we observe tumors that are highly vascularized, have a disrupted ECM, and show increased matrix metalloproteinase-2 activity. Loss of the VHL pathway leading to hypoxia-inducible factor-alpha degradation results in tumors with increased vascular endothelial growth factor levels but with surprisingly low microvessel density, a tightly assembled ECM and low invasive ability. We conclude that loss of ECM integrity could promote and maintain tumor angiogenesis by providing a route for blood vessels to infiltrate tumors.

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)0.000
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 score0.054
Threshold uncertainty score0.481

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
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 head0.029
GPT teacher head0.304
Teacher spread0.275 · 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

Citations124
Published2006
Admission routes1
Has abstractyes

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