Fibroblast Growth Factor 2 Induces E-Cadherin Down-Regulation via PI3K/Akt/mTOR and MAPK/ERK Signaling in Ovarian Cancer Cells
Bibliographic record
Abstract
Fibroblast growth factor 2 (FGF2) is produced by ovarian cancer cells and it has been suggested to play an important role in tumor progression. In this study, we report that FGF2 treatment down-regulated E-cadherin by up-regulating its transcriptional repressors, Slug and ZEB1, in human ovarian cancer cells. The pharmacological inhibition of phosphatidylinositol-3-kinase (PI3K), mammalian target of rapamycin (mTOR), and MEK suggests that both PI3K/Akt/mTOR and MAPK/ERK signaling are required for FGF2-induced E-cadherin down-regulation. Moreover, FGF2 up-regulated Slug and ZEB1 expression via the PI3K/Akt/mTOR and MAPK/ERK signaling pathways, respectively. Finally, FGF2-induced cell invasion was abolished by the inhibition of the PI3K/Akt/mTOR and MAPK/ERK pathways, and the forced expression of E-cadherin diminished the intrinsic invasiveness of ovarian cancer cells as well as the FGF2-induced cell invasion. This study demonstrates a novel mechanism in which FGF2 down-regulates E-cadherin expression through the activation of PI3K/Akt/mTOR and MAPK/ERK signaling, and the up-regulation of Slug and ZEB1 in human ovarian 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 | 0.000 |
| 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".