Snail knockdown reverses stemness and inhibits tumour growth in ovarian cancer
Bibliographic record
Abstract
To develop effective therapies for advanced high grade serous ovarian cancer (HGSOC), understanding mechanisms of recurrence and metastasis is necessary. In this study, we define the epithelial/mesenchymal status of cell lines that accurately model HGSOC, and evaluate the therapeutic potential of targeting Snai1 (Snail), a master regulator of the epithelial/mesenchymal transition (EMT) in vitro and in vivo. The ratio of Snail to E-cadherin (S/E index) at RNA and protein levels was correlated with mesenchymal morphology in four cell lines. The cell lines with high S/E index (OVCAR8 and COV318) showed more CSC-like, motile, and chemoresistant phenotypes than those with low S/E index (OVSAHO and Kuramochi). We tested the role of Snail in regulation of malignant phenotypes including stemness, cell motility, and chemotherapy resistance: shRNA-mediated knockdown of Snail reversed these malignant phenotypes. Interestingly, the expression of let-7 tumour suppressor miRNA was upregulated in Snail knockdown cells. Furthermore, knockdown of Snail decreased tumour burden in an orthotopic xenograft mouse model. We conclude that Snail is important in controlling HGSOC malignant phenotypes and suggest that the Snail/Let-7 axis may be an attractive target for HGSOC 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".