Diet‐derived gallated catechins prevent TGF‐b‐mediated epithelial‐mesenchymal transition, cell migration and vasculogenic mimicry in chemosensitive ES‐2 ovarian cancer cells
Bibliographic record
Abstract
Background Transforming growth factor (TGF)‐β triggers ovarian cancer metastasis through epithelial‐mesenchymal transition (EMT). Whereas drug design strategies targeting the TGF‐β signaling pathway have been envisioned, the anti‐TGF structure:function aspect of chemopreventive diet‐derived catechins remains unexplored. Methods We assessed the effects of eight catechins on TGF‐β‐mediated cell migration using real‐time exCELLigence, induction of EMT biomarkers by RT‐qPCR and immunoblotting, and in vitro vasculogenic mimicry (VM) by 3D‐Matrigel cultures, a process partly regulated by EMT‐related transcription factors. Results TGF‐β‐mediated phosphorylation of Smad‐3 and p38 signaling intermediates was more effective in a chemosensitive ES‐2 ovarian cancer cell line but was inoperative in cis‐platinum‐ and adriamycin‐chemoresistant SKOV‐3 ovarian cancer cells. Increases in cell migration and in gene/protein expression of EMT biomarkers Fibronectin, Snail, and Slug were observed in ES‐2 cells. When VM was assessed in ES‐2 cells, 3D capillary‐like structures were formed and increases in EMT biomarkers found. Catechins bearing the galloyl moiety (CG, ECG, GCG, and EGCG) exerted potent inhibition of TGF‐β‐induced cell migration as well as EMT, and inhibited VM, in part through inhibition of Snail and matrix metalloproteinase‐2 secretion. Conclusions Our data suggest that diet‐derived catechins exhibit chemopreventive properties that circumvent the TGF‐β‐mediated signaling which contributes to the ovarian cancer metastatic phenotype. Support or Funding Information UQAM Foundation and Chaire in Cancer Prevention and Treatment
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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".