Suppression of Tumor Growth by Pleurotus ferulae Ethanol Extract through Induction of Cell Apoptosis, and Inhibition of Cell Proliferation and Migration
Bibliographic record
Abstract
Cancer is the second leading cause of death worldwide. Edible medicinal mushrooms have been used in traditional medicine as regimes for cancer patients. Recently anti-cancer bioactive components from some mushrooms have been isolated and their anti-cancer effects have been tested. Pleurotus ferulae, a typical edible medicinal mushroom in Xinjiang China, has also been used to treat cancer patients in folk medicine. However, little studies have been reported on the anti-cancer components of Pleurotus ferulae. This study aims to extract bioactive components from Pleurotus ferulae and to investigate the anti-cancer effects of the extracts. We used ethanol to extract anti-cancer bioactive components enriched with terpenoids from Pleurotus ferulae. We tested the anti-tumour effects of ethanol extracts on the melanoma cell line B16F10, the human gastric cancer cell line BGC 823 and the immortalized human gastric epithelial mucosa cell line GES-1 in vitro and a murine melanoma model in vivo. Cell toxicity and cell proliferation were measured by MTT assays. Cell cycle progression, apoptosis, caspase 3 activity, mitochondrial membrane potential (MMP), migration and gene expression were studied in vitro. PFEC suppressed tumor cell growth, inhibited cell proliferation, arrested cells at G0/G1 phases and was not toxic to non-cancer cells. PFEC also induced cell apoptosis and necrosis, increased caspase 3 activity, reduced the MMP, prevented cell invasion and changed the expression of genes associated with apoptosis and the cell cycle. PFEC delayed tumor formation and reduced tumor growth in vivo. In conclusion, ethanol extracted components from Pleurotus ferulae exert anti-cancer effects through direct suppression of tumor cell growth and invasion, demonstrating its therapeutic potential in cancer treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".