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Record W2331260218 · doi:10.1158/1538-7445.am2011-1967

Abstract 1967: Ligand-dependent TGFβ signalling potential

2011· article· en· W2331260218 on OpenAlexaff
Sarah McLean, John Di Guglielmo

Bibliographic record

VenueCancer Research · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicTGF-β signaling in diseases
Canadian institutionsWestern University
Fundersnot available
KeywordsR-SMADTransforming growth factorCell biologySMADBiologySignal transductionReceptorCancer researchChemistryTGF alphaGrowth factorBiochemistry

Abstract

fetched live from OpenAlex

Abstract Transforming growth factor beta (TGFβ) is a cytokine which regulates many normal biological processes such as organogenesis and cell cycle control. Its signaling pathway is under intense study as it is commonly dysregulated in pathophysiological conditions such as cancer and fibrosis. The TGFβ signalling cascade is initiated by the binding of ligand to cell-surface serine-threonine kinase receptors. Binding of ligand to the TGFβ receptors initiates phosphorylation of intracellular signalling proteins called Smads, which can then enter the nucleus and initiate specific transcriptional programs. There are three TGFβ ligands which can activate canonical Smad signalling: TGFβ1, TGFβ2 and TGFβ3. These ligands share significant sequence homology (70-80%) but have different spatial and temporal patterns of expression in development. Although TGFβ1 and TGFβ3 are highly expressed in the tumour microenvironment, the role of TGFβ3 has been largely over-looked and thought to be the same as TGFβ1. The objective of the present study is to evaluate the signalling capacity of TGFβ1 and TGFβ3 in non-small cell lung cancer cells. Using dose response studies, we show that TGFβ1 ligand induces Smad2 phosphorylation to a greater extent than TGFβ3. We also show that TGFβ3 induces a lower reduction in steady-state levels of E-cadherin, a protein involved in cell-cell adhesion, compared to TGFβ1. To evaluate the transcriptional programs of both ligands, we used microarray technology to assess gene transcription in non-small cell lung cancer cells treated with TGFβ1 or TGFβ3. Consistent with our signalling data, we found that TGFβ3 induces far fewer genes than TGFβ1. Future studies will evaluate the differential roles of TGFβ1 and TGFβ3 in cancer cell migration and epithelial-to-mesenchymal transition. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 1967. doi:10.1158/1538-7445.AM2011-1967

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.032
Threshold uncertainty score0.106

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0320.011

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 head0.082
GPT teacher head0.367
Teacher spread0.286 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations1
Published2011
Admission routes1
Has abstractyes

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