IGFBP-2 expression in MCF-7 cells is regulated by the PI3K/AKT/mTOR pathway through Sp1-induced increase in transcription
Bibliographic record
Abstract
Matei Mireutaa, Andrew Darnela & Michael Pollak*aa Departments of Medicine and Oncology, Lady Davis Institute for Medical Research, Montreal SMBD Jewish General Hospital, and McGill University, Montreal, Quebec, CanadaH3T 1E2,Correspondence: M. Pollak, Departments of Medicine and Oncology, Jewish General Hospital, 3755 Chemin Cote Ste-Catherine, Montreal, Quebec, Canada H3T 1E2. Tel: 514 340 8222. Ext. 5530. Fax: 514 340 8600. E-mail: michael.pollak@mcgill.caInsulin-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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".