MétaCan
Menu
Back to cohort
Record W2001295103 · doi:10.3109/08977191003745472

IGFBP-2 expression in MCF-7 cells is regulated by the PI3K/AKT/mTOR pathway through Sp1-induced increase in transcription

2010· article· en· W2001295103 on OpenAlexaff
Matei Mireuta, Andrew D. Darnel, Michaël Pollak

Bibliographic record

VenueGrowth Factors · 2010
Typearticle
Languageen
FieldMedicine
TopicGrowth Hormone and Insulin-like Growth Factors
Canadian institutionsMcGill UniversityJewish General Hospital
Fundersnot available
KeywordsPI3K/AKT/mTOR pathwayProtein kinase BTranscription factorTranscription (linguistics)mTORC2BiologyRPTORMessenger RNACell biologySp1 transcription factorInsulin-like growth factor-binding proteinLY294002ChemistryMolecular biologyCancer researchSignal transductionGene expressionGrowth factorPromoterGeneInsulin-like growth factormTORC1BiochemistryReceptor

Abstract

fetched live from OpenAlex

Insulin-like growth factor binding protein 2 (IGFBP-2) has been implicated in the pathophysiology of neoplasia. The PI3K/AKT/mTOR pathway has recently been shown to be a predominant regulator of IGFBP-2 at the protein level in MCF-7 breast cancer cells. However, there are gaps in knowledge with respect to the molecular mechanisms that underlie this regulation. Here, we show that the PI3K/AKT/mTOR pathway regulates IGFBP-2 protein levels by modulating IGFBP-2 mRNA abundance in MCF-7 cells. This change is achieved by regulating transcription through a critical region present in the first 200 bp upstream of the transcription initiation site where Sp1 transcription factor binds and drives transcription. IGF-1 treatment leads to increased nuclear abundance of Sp1 and increased IGFBP-2 mRNA and protein levels. Rapamycin and LY294002 induce a decline in Sp1 nuclear abundance and IGFBP-2 mRNA and protein levels. This work provides a mechanistic explanation for the observed effects of the PI3K/AKT/mTOR pathway on IGFBP-2 levels in MCF-7 cells.

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 categoriesMeta-epidemiology (narrow)
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 score0.117
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.002
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 head0.018
GPT teacher head0.239
Teacher spread0.221 · 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.

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

Citations54
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueGrowth FactorsSame topicGrowth Hormone and Insulin-like Growth FactorsFrench-language works237,207