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Record W2078929667 · doi:10.1158/1538-7445.am2013-288

Abstract 288: TGF-beta induced epithelial-to-mesenchymal transition is attenuated when the MNK/eIF4E pathway is functionally impaired.

2013· article· en· W2078929667 on OpenAlexaff
Sonia V. del Rincón, Bonnie Huor, Elaine Ngan, Luca A. Petruccelli, Peter M. Siegel, Wilson H. Miller

Bibliographic record

VenueCancer Research · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer-related molecular mechanisms research
Canadian institutionsMcGill University
Fundersnot available
KeywordsEpithelial–mesenchymal transitionEIF4ECancer researchGene silencingPhosphorylationTransforming growth factor betaMetastasisBiologyBreast cancerCancerCell biologySignal transductionTranslation (biology)Messenger RNAGeneGenetics

Abstract

fetched live from OpenAlex

Abstract The epithelial-mesenchymal-like transition (EMT) is a process enabling epithelial cells to gain the motile characteristics of mesenchymal cells, in a manner resembling metastasis. TGF-beta, via well-defined transcriptional mechanisms, is considered a master regulator of EMT. However, the idea that TGF-beta can regulate the translational machinery to drive EMT remains largely unexplored. The eukaryotic translation initiation factor eIF4E is known to be overexpressed in breast cancer, has been linked to increased invasiveness, and is a promising target for the treatment of breast cancer. Our hypothesis is that phosphorylation of eIF4E stimulated by TGF-beta is required for inducing EMT and metastasis in breast cancer. Our novel preliminary data show that TGF-beta can stimulate eIF4E phosphorylation as normal mammary epithelial cells become mesenchymal. Silencing of eIF4E attenuates molecular and behavioral changes associated with EMT. Moreover, decreasing eIF4E levels can impair TGF-beta induced migration and invasion of ErbB2-expressing breast cancer cells. In keeping with a role of phosphorylated eIF4E in driving the metastatic phenotype, we show that chemically and genetically inhibiting the eIF4E kinase MNK1 attenuates TGF-beta-stimulated EMT. We hypothesized that the expression of master regulators of EMT could be restricted when the eIF4E/MNK pathway is functionally impaired, which prompted us to look at the expression of Twist and Snail in TGF-beta treated eIF4E- and MNK- silenced cells. Our data shows that TGF-beta-induced Snail protein expression, but not mRNA level, is repressed when the MNK/eIF4E pathway is functionally impaired. Our results indicate that MNK and eIF4E are essential for EMT and suggest that therapeutic inhibition of MNK/eIF4E pathway may be a useful strategy for the control of tumor invasion and metastasis. Citation Format: Sonia V. del Rincon, Bonnie Huor, Elaine Ngan, Luca Petruccelli, Peter Siegel, Wilson H. Miller, Jr. TGF-beta induced epithelial-to-mesenchymal transition is attenuated when the MNK/eIF4E pathway is functionally impaired. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 288. doi:10.1158/1538-7445.AM2013-288

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 score0.004
Threshold uncertainty score0.013

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.046
GPT teacher head0.334
Teacher spread0.288 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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