IGF-1 increases invasive potential of MCF 7 breast cancer cells and induces activation of latent TGF-β1 resulting in epithelial to mesenchymal transition
Bibliographic record
Abstract
INTRODUCTION: TGF-β signaling has been extensively studied in many developmental contexts, amongst which is its ability to induce epithelial to mesenchymal transitions (EMT). EMTs play crucial roles during embryonic development and have also come under intense scrutiny as a mechanism through which breast cancers progress to become metastatic. Interestingly, while the molecular hallmarks of EMT progression (loss of cell adhesion, nuclear localization of β-catenin) are straightforward, the cellular signaling cascades that result in an EMT are numerous and diverse. Furthermore, most studies describing the biological effects of TGF-β have been performed using high concentrations of active, soluble TGF-β, despite the fact that TGF-β is produced and secreted as a latent complex. METHODS: MCF-7 breast cancer cells treated with recombinant IGF-1 were assayed for metalloproteinase activity and invasiveness through a matrigel coated transwell invasion chamber. IGF-1 treatments were then followed by the addition of latent-TGF-β1 to determine if elevated levels of IGF-1 together with latent-TGF-β1 could cause EMT. RESULTS: Results showed that IGF-1 - a molecule known to be elevated in breast cancer is a regulator of matrix metalloproteinase activity (MMP) and the invasive potential of MCF-7 breast cancer cells. The effects of IGF-1 appear to be mediated through signals transduced via the PI3K and MAPK pathways. In addition, increased IGF-1, together with latent TGF-β1 and active MMPs result in EMT. CONCLUSIONS: Taken together our data suggest a novel a link between IGF-1 levels, MMP activity, TGF-β signaling, and EMT in breast cancer 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".