Agave negatively regulates YAP and TAZ transcriptionally and post-translationally in osteosarcoma cell lines
Bibliographic record
Abstract
Osteosarcoma (OS) is the most aggressive type of primary solid tumor that develops in bone. Whilst conventional chemotherapy can improve survival rates, the outcome for patients with metastatic or recurrent OS remains poor, so novel treatment agents and strategies are required. Research into new anticancer therapies has paved the way for the utilisation of natural compounds as they are typically less expensive and less toxic compared to conventional chemotherapeutics. Previously published works indicate that Agave exhibits anticancer properties, however potential molecular mechanisms remain poorly understood. In the present study, we investigate the anticancer effects of Agave leaf extract in OS cells suggesting that Agave inhibits cell viability, colony formation, and cell migration, and can induce apoptosis in OS cell lines. Moreover, Agave sensitizes OS cells to cisplatin (CDDP) and radiation, to overcome chemo- and radio-resistance. We demonstrate that Agave extract induces a marked decrease of Yes Associated Protein (YAP) and Tafazzin (TAZ) mRNA and protein expression upon treatment. We propose an initial mechanism of action in which Agave induces YAP/TAZ protein degradation, followed by a secondary event whereby Agave inhibits YAP/TAZ transcription, effectively deregulating the Nuclear Factor kappa B (NF-κB) p65:p50 heterodimers responsible for transcriptional induction of YAP and TAZ.
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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".