Cancer Chemopreventive and Therapeutic Effects of Diosgenin, a Food Saponin
Bibliographic record
Abstract
Cancer chemoprevention is a strategy taken to retard, regress, or resist the multistep process of carcinogenesis, including the blockage of its vital morphogenetic milestones viz. normal-preneoplasia-neoplasia-metastasis. For several reasons, including safety, minimal (or no) toxicity and side-effects, and better availability, alternatives such as naturally occurring phytochemicals that are found in foods are becoming increasingly popular over synthetic drugs. Food saponins have been used in complimentary and traditional medicine against a variety of diseases including several cancers. Diosgenin, a naturally occurring steroid saponin found abundantly in legumes and yams, is a well-known precursor of various synthetic steroidal drugs that are extensively used in the pharmaceutical industry. Over the past decade, a series of preclinical and mechanistic studies have been conducted to understand the role of diosgenin as a chemopreventive/therapeutic agent against several cancers. This review highlights the biological activity of diosgenin that contributes to cancer chemoprevention and control. The anticancer mode of action of diosgenin has been demonstrated via modulation of multiple cell signaling events involving critical molecular candidates associated with growth, differentiation, apoptosis, and oncogenesis. Altogether, these preclinical and mechanistic findings strongly implicate the use of diosgenin as a novel, multitarget-based chemopreventive or therapeutic agent against several cancer types. Future research in this field will help to establish not only whether diosgenin is safe and efficacious as a chemopreventive agent against several human cancers, but also to develop and evaluate standards of evidence for health claims for diosgenin-containing foods as they become increasingly popular and enter the marketplace labeled as functional foods and nutraceuticals.
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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 | 0.000 |
| 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".