BMP4 induces EMT and Rho GTPase activation in human ovarian cancer cells
Bibliographic record
Abstract
We identified previously an autocrine bone morphogenetic protein-4 (BMP4) signalling pathway in primary human normal ovarian surface epithelial (OSE) and epithelial ovarian cancer (OvCa) cells. Herein we show that treatment of OvCa cells with BMP4 produced morphological alterations and increased cellular adhesion, motility and invasion. The BMP4 inhibitor noggin blocked the BMP4-induced phenotype, and decreased autocrine BMP4-mediated OvCa cell motility and adherence. In response to exogenous BMP4, the epithelial-mesenchymal transition (EMT) markers Snail and Slug mRNA and protein were up-regulated, E-cadherin mRNA and protein were down-regulated and the network of alpha smooth muscle actin changed to resemble a mesenchymal cell. We also observed changes in the level of activated Rho GTPases in OvCa cells treated with BMP4, strongly suggesting that the changes in morphology, adhesion, motility and invasion are probably mediated through the activation of these molecules. Strikingly, treatment of normal OSE cells with BMP4 or noggin failed to alter cell motility, providing evidence that OSE and OvCa cells possess a distinct capability to respond to BMP4. Overall, our studies suggest a link between autocrine BMP signalling mediated through the Rho GTPase family and Snail- and Slug-induced EMT that may collectively contribute to aggressive OvCa behaviour.
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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".