Potent effects of dioscin against pancreatic cancer via miR‐149‐3P‐mediated inhibition of the Akt1 signalling pathway
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The aim of the present study was to investigate the effects and possible underlying mechanisms of dioscin against pancreatic cancer in vitro and in vivo. EXPERIMENTAL APPROACH: In vitro actions of dioscin on viability of ASPC-1 and PANC-1 cells, and in vivo effects to suppress the tumour growth of cell xenografts in nude mice were assessed. In addition, microRNA microarray analysis determined which microRNAs were affected by dioscin. The mechanisms underlying the actions of dioscin against pancreatic cancer were elucidated in terms of Akt1 and other proteins related to aopoptosis. KEY RESULTS: Dioscin markedly induced apoptosis and significantly suppressed the tumour growth of ASPC-1 and PANC-1 cell xenografts, in nude mice. Total of 107 microRNAs with differential changes were found, in which miR-149-3P targeted with Akt1 was markedly up-regulated by dioscin. Further studies showed that dioscin significantly down-regulated Akt1 levels, and thus induced cell apoptosis by increasing the levels of Bax, Apaf-1, cleaved caspase-3/9, cleaved PARP, suppressing Bcl-2 levels, and causing cytochrome c release. The effects of an inhibitor of miR-149-3P and of siRNA of testicular Akt1 suggested that dioscin showed excellent activity against pancreatic cancer via miR- 149-3P-mediated inhibition of Akt1 signalling pathway. CONCLUSIONS AND IMPLICATIONS: Collectively, these findings confirmed the potent effects of dioscin against pancreatic cancer and also provided novel insights into the mechanisms of the compound as a potential candidate for the treatment of pancreatic cancer.
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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) | 0.001 | 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 | 0.001 |
| 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".