Role of Silymarin in Cancer Treatment: Facts, Hypotheses, and Questions
Bibliographic record
Abstract
The flavonoid silymarin extracted from the seeds of Sylibum marianum is a mixture of 6 flavolignan isomers. The 3 more important isomers are silybin (or silibinin), silydianin, and silychristin. Silybin is functionally the most active of these compounds. This group of flavonoids has been extensively studied and they have been used as hepato-protective substances for the mushroom Amanita phalloides intoxication and mainly chronic liver diseases such as alcoholic cirrhosis and nonalcoholic fatty liver. Hepatitis C progression is not, or slightly, modified by silymarin. Recently, it has also been proposed for SARS COVID-19 infection therapy. The biochemical and molecular mechanisms of action of these substances in cancer are subjects of ongoing research. Paradoxically, many of its identified actions such as antioxidant, promoter of ribosomal synthesis, and mitochondrial membrane stabilization, may seem protumoral at first sight, however, silymarin compounds have clear anticancer effects. Some of them are: decreasing migration through multiple targeting, decreasing hypoxia inducible factor-1α expression, inducing apoptosis in some malignant cells, and inhibiting promitotic signaling among others. Interestingly, the antitumoral activity of silymarin compounds is limited to malignant cells while the nonmalignant cells seem not to be affected. Furthermore, there is a long history of silymarin use in human diseases without toxicity after prolonged administration. The ample distribution and easy accessibility to milk thistle—the source of silymarin compounds, its over the counter availability, the fact that it is a weed, some controversial issues regarding bioavailability, and being a nutraceutical rather than a drug, has somehow led medical professionals to view its anticancer effects with skepticism. This is a fundamental reason why it never achieved bedside status in cancer treatment. However, in spite of all the antitumoral effects, silymarin actually has dual effects and in some cases such as pancreatic cancer it can promote stemness. This review deals with recent investigations to elucidate the molecular actions of this flavonoid in cancer, and to consider the possibility of repurposing it. Particular attention is dedicated to silymarin's dual role in cancer and to some controversies of its real effectiveness.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".