Dropwort-induced metabolic reprogramming restrains YAP/TAZ/TEAD oncogenic axis in mesothelioma
Bibliographic record
Abstract
BACKGROUND: Over the past decade, newly designed cancer therapies have not significantly improved the survival of patients diagnosed with Malignant Pleural Mesothelioma (MPM). Among a limited number of genes that are frequently mutated in MPM several of them encode proteins that belong to the HIPPO tumor suppressor pathway. METHODS: The anticancer effects of the top flower standardized extract of Filipendula vulgaris (Dropwort) were characterized in "in vitro" and "in vivo" models of MPM. At the molecular level, two "omic" approaches were used to investigate Dropwort anticancer mechanism of action: a metabolomic profiling and a phosphoarray analysis. RESULTS: We found that Dropwort significantly reduced cell proliferation, viability, migration and in vivo tumor growth of MPM cell lines. Notably, Dropwort affected viability of tumor-initiating MPM cells and synergized with Cisplatin and Pemetrexed in vitro. Metabolomic profiling revealed that Dropwort treatment affected both glycolysis/tricarboxylic acid cycle as for the decreased consumption of glucose, pyruvate, succinate and acetate, and the lipid metabolism. We also document that Dropwort exerted its anticancer effects, at least partially, promoting YAP and TAZ protein ubiquitination. CONCLUSIONS: Our findings reveal that Dropwort is a promising source of natural compound(s) for targeting the HIPPO pathway with chemo-preventive and anticancer implications for MPM management.
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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".