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Record W2032086812 · doi:10.1158/0008-5472.can-06-4477

Epigenetically Controlled Fibroblast Growth Factor Receptor 2 Signaling Imposes on the RAS/BRAF/Mitogen-Activated Protein Kinase Pathway to Modulate Thyroid Cancer Progression

2007· article· en· W2032086812 on OpenAlexafffund
Tetsuo Kondo, Lei Zheng, Wei Liu, Junichi Kurebayashi, L. Sylvia, Shereen Ezzat

Bibliographic record

VenueCancer Research · 2007
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicFibroblast Growth Factor Research
Canadian institutionsPrincess Margaret Cancer CentreUniversity of TorontoImmunovaccine (Canada)University Health Network
FundersHospital for Sick Children
KeywordsCancer researchMAPK/ERK pathwayFibroblast growth factor receptor 1Thyroid cancerFibroblast growth factor receptorFibroblast growth factor receptor 2BiologyCancerFibroblast growth factorCarcinogenesisFibroblast growth factor receptor 4Protein kinase ASignal transductionKinaseCell biologyReceptorGenetics

Abstract

fetched live from OpenAlex

Fibroblast growth factor (FGF) signals play fundamental roles in development and tumorigenesis. Thyroid cancer is an example of a tumor with nonoverlapping genetic mutations that up-regulate mitogen-activated protein kinase (MAPK). Here, we show that FGF receptor 1 (FGFR1), which is expressed mainly in neoplastic thyroid cells, propagates MAPK activation and promotes tumor progression. In contrast, FGFR2 is down-regulated in neoplastic thyroid cells through DNA promoter methylation. Reexpression of FGFR2 competes with FGFR1 for the immediate substrate FGFR substrate 2 to impede signaling upstream of the BRAF/MAPK pathway. These data unmask an epigenetically controlled FGFR2 signal that imposes precisely on the intragenically modified BRAF/MAPK pathway to modulate thyroid cancer behavior.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.040
GPT teacher head0.366
Teacher spread0.326 · 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 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

Citations63
Published2007
Admission routes2
Has abstractyes

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