Nimbolide-Induced Oxidative Stress Abrogates STAT3 Signaling Cascade and Inhibits Tumor Growth in Transgenic Adenocarcinoma of Mouse Prostate Model
Bibliographic record
Abstract
AIMS: Prostate cancer (PCa) is one of the most commonly diagnosed cancers worldwide. Currently available therapies for metastatic PCa are only marginally effective, hence novel treatment modalities are urgently required. Considerable evidence(s) suggest that deregulated activation of oncogenic transcription factor, signal transducer and activator of transcription 3 (STAT3) plays a pivotal role in the development and progression of PCa. Thus, agents that can abrogate STAT3 activation could form the basis of novel therapy for PCa patients. In the present study, we analyzed whether the potential anticancer effects of nimbolide (NL), a limonoid triterpene derived from Azadirachta indica, against PCa cell lines and transgenic adenocarcinoma of mouse prostate (TRAMP) model are mediated through the negative regulation of STAT3 pathway. RESULTS: Data from the in vitro studies indicated that NL could significantly inhibit cell viability, induce apoptosis, and suppress cellular invasion and migration. Interestingly, NL also abrogated STAT3 activation and this effect was found to be mediated via an increased production of reactive oxygen species (ROS) due to GSH/GSSG imbalance. Oral administration of NL significantly suppressed the tumor growth and metastasis in TRAMP mouse model without exhibiting any significant adverse effects. INNOVATION: The present study demonstrates the critical role of GSH/GSSG imbalance-mediated ROS production contributing to the STAT3 inhibitory and tumor-suppressive effect of NL in PCa. CONCLUSION: Overall, our findings indicate that NL exhibits significant anticancer effects in PCa that may be primarily mediated through the ROS-regulated inhibition of STAT3 signaling cascade.
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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 | 0.001 | 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) | 0.002 | 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".