Dietary Feeding of Silibinin Inhibits Prostate Tumor Growth and Progression in Transgenic Adenocarcinoma of the Mouse Prostate Model
Bibliographic record
Abstract
Herein, for the first time, we evaluated the chemopreventive efficacy of dietary silibinin against prostate cancer (PCa) growth and progression in transgenic adenocarcinoma of the mouse prostate (TRAMP) mice from two different genetic backgrounds [C57BL/6 (TRAMP) x FVB; C57BL/6 (TRAMP) x C57BL/6]. At 4 weeks of age, mice were fed control or 0.1% to 1% silibinin-supplemented diets until 23 to 24 weeks of age. Silibinin-fed groups had a lower tumor grade and higher incidence of prostatic intraepithelial neoplasia (PIN) at the expense of a strong decrease in adenocarcinoma incidence. Prostate tissue showed a 47% (P < 0.001) decrease in proliferating cell nuclear antigen (PCNA)-positive cells and an approximately 7-fold (P < 0.001) increase in apoptotic cells at the highest silibinin dose. As potential mechanisms of silibinin efficacy, an approximately 50% (P < 0.05) decrease in insulin-like growth factor (IGF) receptor type I beta and an approximately 13-fold (P < 0.001) increase in IGF-binding protein 3 (IGFBP-3) protein levels were also observed. These changes were specific to tumors as they were not reflected in circulating IGF-IGFBP-3 system. Additionally, silibinin decreased protein expression of cyclin-dependent kinases (Cdk) by more than 90% (P < 0.001) with a concomitant increase in Cdk inhibitors, Cip1/p21 and Kip1/p27 (P < 0.05, for both). A dose-dependent decrease was also observed in cyclin B1, cyclin E, and cyclin A protein levels by silibinin. Together, these findings suggest that oral silibinin blocks PCa growth and progression at PIN stage in TRAMP mice via modulation of tumor IGF-IGFBP-3 axis and cell cycle regulation, and therefore it has practical and translational potential in suppressing growth and neoplastic conversion of PIN to PCa in humans.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".