Silibinin induces immunogenic cell death in cancer cells and enhances the induced immunogenicity by chemotherapy
Bibliographic record
Abstract
Introduction: Silibinin is a natural flavonoid compound known to induce apoptosis in cancer cells. Despite silibinin's safety and efficacy as an anticancer drug, its effects on inducing immunogenic cell death (ICD) are largely unknown. Herein, we have evaluated the stimulating effects of silibinin on ICD in cancer cells treated with silibinin alone or in combination with chemotherapy. Methods: The anticancer effect of silibinin, alone or in combination with doxorubicin or oxaliplatin (OXP), was assessed using the MTT assay. Compusyn software was used to analyze the combination therapy data. Western blotting was conducted to examine the level of STAT3 activity. Flow cytometry was used to analyze calreticulin (CRT) and apoptosis. The heat shock protein (HSP70), high mobility group box protein1 (HMGB1), and IL-12 levels were assessed by ELISA. Results: Compared to the negative control groups, silibinin induced ICD in CT26 and B16F10 cells and significantly enhanced the induction of this type of cell death by doxorubicin, and these changes were allied with substantial increases in the level of damage-associated molecular patterns (DAMPs) including CRT, HSP70, and HMGB1. Furthermore, conditioned media from cancer cells exposed to silibinin and doxorubicin was found to stimulate IL-12 secretion in dendritic cells (DCs), suggesting the link of this treatment with the induction of Th1 response. Silibinin did not augment the ICD response induced by OXP. Conclusion: Our findings showed that silibinin can induce ICD and it potentiates the induction of this type of cell death induced by chemotherapy in cancer cells.
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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".