Proanthocyanidins from the American Cranberry (<i>Vaccinium macrocarpon</i>) inhibit matrix metalloproteinase‐2 and matrix metalloproteinase‐9 activity in human prostate cancer cells via alterations in multiple cellular signalling pathways
Bibliographic record
Abstract
Prostate cancer is one of the most common cancers in the Western world, and it is believed that an individual's diet affects his risk of developing cancer. There has been an interest in examining phytochemicals, the secondary metabolites of plants, in order to determine their potential anti-cancer activities in vitro and in vivo. In this study we document the effects of proanthocyanidins (PACs) from the American Cranberry (Vaccinium macrocarpon) on matrix metalloproteinase (MMP) activity in DU145 human prostate cancer cells. Cranberry PACs decreased cellular viability of DU145 cells at a concentration of 25 µg/ml by 30% after 6 h of treatment. Treatment of DU145 cells with PACs resulted in an inhibition of both MMPs 2 and 9 activity. PACs increased the expression of TIMP-2, a known inhibitor of MMP activity, and decreased the expression of EMMPRIN, an inducer of MMP expression. PACs decreased the expression of PI-3 kinase and AKT proteins, and increased the phosphorylation of both p38 and ERK1/2. Cranberry PACs also decreased the translocation of the NF-κB p65 protein to the nucleus. Cranberry PACs increased c-jun and decreased c-fos protein levels. These results suggest that cranberry PACs decreases MMP activity through the induction and/or inhibition of specific temporal MMP regulators, and by affecting either the phosphorylation status and/or expression of MAP kinase, PI-3 kinase, NF-κB and AP-1 pathway proteins. This study further demonstrates that cranberry PACs are a strong candidate for further research as novel anti-cancer agents.
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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".