Papers › Joining up the scattered anticancer knowledge on auraptene and umbelliprenin: a meta-analysis
Joining up the scattered anticancer knowledge on auraptene and umbelliprenin: a meta-analysis
Shakiba, M. and Rassouli, F.B.
Auraptene (AUR) and umbelliprenin (UMB) are naturally occurring prenylated coumarins that have demonstrated promising anticancer effects across various human cancer cell lines. This meta-analysis aimed to systematically assess, compare, and quantify the anticancer efficacy of AUR and UMB by synthesizing evidence from in vitro studies. A comprehensive literature search identified 27 eligible studies investigating AUR or UMB against cancer cells. Mixed-effects models revealed significant negative associations between coumarin dose and viability for AUR (est. = − 2.27) and UMB (est. = − 3.990), underscoring their dose-dependent cytotoxicity. Meta-regression indicated slightly higher potency for UMB over AUR, potentially due to increased lipophilicity imparted by additional isoprenyl units. Machine learning approaches identified coumarin dose and cancer type as the most influential determinants of toxicity, while treatment duration and the specific coumarin displayed weaker effects. Moderate (AUR) to substantial (UMB) between-study heterogeneity was detected, although the findings proved robust. In summary, this meta-analysis establishes AUR and UMB as promising natural anticancer candidates with clear dose-toxicity relationships across diverse malignancies. The structural insights and quantifications of anticancer efficacy can inform forthcoming efforts assessing therapeutic potential in pre-clinical models and human trials.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Datasets
Introduced by this paper, per the archive.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Classification | AUR & UMB dataset | RandomForestClassifier | Accuracy | 82% | #1 of 1 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections