Resveratrol Inhibits Cisplatin-Induced Epithelial-to-Mesenchymal Transition in Ovarian Cancer Cell Lines
Bibliographic record
Abstract
BACKGROUND: Many patients diagnosed with ovarian cancer experience recurrence and metastasis, two aspects that will often cause their demise. Epithelial-to-mesenchymal transition (EMT) is a key process involved in cancer progression. With increasing evidence linking Cisplatin and EMT, we wanted to identify a compound able to counter EMT progression when cancer cells are treated with Cisplatin. METHODOLOGY/PRINCIPAL FINDINGS: Cell death was evaluated by cytometry with Annexin V/PI staining in A2780 and A2780CP cells. Ovarian cancer cell lines were treated with Cisplatin (24 h, 10 µM) and different concentrations of Resveratrol to evaluate its effect on Cisplatin-induced EMT using Western Blot and RT-PCR analysis. Morphological studies and wound healing assay to evaluate cell motility were performed using 72 h Cisplatin treatment with A2780 and A2780CP cells. Densitometry was done on Western Blot and PCR results, and statistical significance was determined using One-Way ANOVA followed by Tukey post-hoc test. Our results show that Cisplatin induced EMT-associated morphological changes in the A2780 ovarian cancer cell line and to a lesser extent in its Cisplatin-resistant counterpart A2780CP. Resveratrol caused cell death in A2780 and A2780CP cell lines in an apoptotic-independent manner. Resveratrol inhibited Cisplatin-induced Snail expression by reducing the Erk pathway activation, reverted morphological changes induced by Cisplatin and decreased cell migration. CONCLUSIONS: These results indicate that Resveratrol has interesting potential to prevent Cisplatin-induced EMT in ovarian cancer cells. By increasing cell death, it also represents an inviting approach as adjuvant therapy to be used with chemotherapy. Using Erk pathway inhibitors could also prove helpful in ovarian cancer treatment to reduce the risk of metastasis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.001 | 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".