Breast Cancer Cells Proliferation Is Regulated by Tyrosine Phosphatase SHP1 through c-jun N-Terminal Kinase and Cooperative Induction of RFX-1 and AP-4 Transcription Factors
Bibliographic record
Abstract
In this study, we show that proliferation of breast cancer cells is suppressed by IGF-1-activated JNK MAPK pathway. The molecular mechanism by which c-jun-NH,-kinase (JNK) activation induces antiproliferative signals in IGF-1-stimulated breast cancer cells remains unknown. Tyrosine phosphatase SHP1 is known to negatively regulate signal transduction pathways activated by cell surface receptors including IGF-1. Moreover, SHP1 transcript and protein levels are increased in epithelial tumors. Therefore, we hypothesized that IGF-activated JNK induces expression of SHP1 in breast cancer cells. To further clarify the role of SHP1 in tumor growth, we correlated the proliferation rates of breast adenocarcinoma cells with SHP1 expression and JNK activation. We show that proliferation of serum- or IGF-1-stimulated breast adenocarcinoma cells is negatively regulated by SHP1 and show for the first time that IGF-1-activated JNK induces SHP1 expression in MCF-7 cells used as experimental model. In an attempt to understand the mechanism by which serum- or IGF-1-activated JNK induces SHP1 expression resulting in suppression of cell proliferation, we reveal for the first time that in serum- or IGF-1-stimulated breast cancer MCF-7 cells, JNK induces SHP1 expression through the binding of AP-4 and RFX-1 transcription factors to the epithelial tissue-specific SHP1 promoter. Overall, we show for the first time that IGF-1-stimulated proliferation of breast adenocarcinoma cells is negatively regulated by SHP1 through activation of JNK.
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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) | 0.001 | 0.000 |
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.
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".