Anticancer Effect of Medicinal Mushroom Extract on Renal Cell Carcinoma: Alternative Therapeutic Implication
Bibliographic record
Abstract
Background: To investigate if medicinal Poria mushroom extract (PE) would be a potential therapeutic agent against renal cell carcinoma (RCC) as it has been shown to have anticancer/antitumor activity with few side effects. We examined its anticancer effect on RCC as well as its potential anticancer mechanism in vitro . Methods: Anticancer effect of PE was first assessed by the dose-dependent effects (0 - 200 µg/mL) on human RCC, ACHN cells. Its anticancer mechanism was then explored focusing on specific biochemical and molecular parameters Results: PE concentrations >= 100 µg/mL led to 50-90% reduction in cell viability, accompanied by a significant decrease in hexokinase activity and cellular adenosine triphosphate (ATP) level, implying the inhibition of glycolysis. The key metabolic regulators, adenosine monophosphate (AMP)-activated protein kinase (AMPK), protein kinase B (Akt), and mammalian target of rapamycin (mTOR), were specifically modulated by PE. These results were indicative of subsequent growth cessation and cell death. Cell cycle analysis also revealed a G 1 cell cycle arrest and the upregulation of activating transcription factor 4 (ATF4), C/EBP homologous protein (CHOP), and glucose-regulated protein 78 (GRP78), indicated induction of endoplasmic reticulum (ER) stress. Ultimately, such ER stress led to apoptosis, evidenced by proteolytic activation of caspase-3 (Csp-3) and poly-ADP ribose polymerase (PARP). Conclusions: PE has anticancer effect on ACHN cells, resulting in the significant reduction in cell viability. Its anticancer mechanism appears to be linked to glycolysis inhibition, modulations of metabolic pathways, and ER stress-induced apoptosis. World J Nephrol Urol. 2022;11(1):1-9 doi: https://doi.org/10.14740/wjnu433
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".