Saffron-Based Crocin Prevents Early Lesions of Liver Cancer: In vivo, In vitro and Network Analyses
Bibliographic record
Abstract
BACKGROUND: The angiogenesis inhibitor, sorafenib, remains the only available therapy of hepatocellular carcinoma (HCC). Only recently patents of VEGF receptors-3 inhibitors are developed. Thus, a novel approach against HCC is essential for a better therapeutic outcome. OBJECTIVE: The aims of this study were to examine the chemopreventive action of saffron's main biomolecule, crocin, against chemically-induced liver cancer in rats, and to explore the mechanisms by which crocin employs its anti-tumor effects. METHOD: We investigated the anti-cancer effect of crocin on an experimental carcinogenesis model of liver cancer by studying the anti-oxidant, anti-inflammatory, anti-proliferation, pro-apoptotic activities of crocin in vivo. In addition, we provided a network analysis of differentially expressed genes in tissues of animals pre-treated with crocin in comparison to induced-HCC animals' tissues. To further support our results, in vitro analysis was carried out. We assessed the effects of crocin on HepG2 cells viability by treating them with various concentrations of crocin; in addition, effects of crocin on cell cycle distribution of HepG2 cells were investigated. RESULTS: Findings reported herein demonstrated the anti-proliferative and pro-apoptotic properties of crocin when administrated in induced- HCC model. Crocin exhibited anti-inflammatory properties where NF-κB, among other inflammatory markers, was inhibited. In vitro analysis confirmed crocin's effect in HepG2 by arresting the cell cycle at S and G2/M phases, inducing apoptosis and down regulating inflammation. Network analysis identified NF-κB as a potential regulatory hub, and therefore, a candidate therapeutic drug target. CONCLUSION: Taken together, our findings introduce crocin as a candidate chemopreventive agent against HCC.
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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 | 0.000 |
| 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".