Akt/cAMP-Responsive Element Binding Protein/Cyclin D1 Network: A Novel Target for Prostate Cancer Inhibition in Transgenic Adenocarcinoma of Mouse Prostate Model Mediated by Nexrutine, a <i>Phellodendron Amurense</i> Bark Extract
Bibliographic record
Abstract
PURPOSE: Development of prostate cancer prevention strategies is an important priority to overcome high incidence, morbidity, and mortality. Recently, we showed that Nexrutine, an herbal extract, inhibits prostate cancer cell proliferation through modulation of Akt and cAMP-responsive element binding protein (CREB)-mediated signaling pathways. However, it is unknown if Nexrutine can be developed as a dietary supplement for the prevention of prostate cancer. In this study, we used the transgenic adenocarcinoma of mouse prostate (TRAMP) model to examine the ability of Nexrutine to protect TRAMP mice from developing prostate cancer. EXPERIMENTAL DESIGN: Eight-week-old TRAMP mice were fed with pelleted diet containing 300 and 600 mg/kg Nexrutine for 20 weeks. Efficacy of Nexrutine was evaluated by magnetic resonance imaging at 18 and 28 weeks of progression and histologic analysis of prostate tumor or tissue at the termination of the experiment. Tumor tissue was analyzed for modulation of various signaling molecules. RESULTS: We show that Nexrutine significantly suppressed palpable tumors and progression of cancer in the TRAMP model. Expression of total and phosphorylated Akt, CREB, and cyclin D1 was significantly reduced in prostate tissue from Nexrutine intervention group compared with tumors from control animals. Nexrutine also inhibited cyclin D1 transcriptional activity in androgen-independent PC-3 cells. Overexpression of kinase dead Akt mutant or phosphorylation-defective CREB inhibited cyclin D1 transcriptional activity. CONCLUSIONS: The current study shows that Nexrutine-mediated targeting of Akt/CREB-induced activation of cyclin D1 prevents the progression of prostate cancer. Expression of CREB and phosphorylated CREB increased in human prostate tumors compared with normal tissue, suggesting their potential use as prognostic markers.
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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 | 0.001 |
| 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".