Delta-tocotrienol enhances the anti-tumor effects of interferon alpha through reactive oxygen species and Erk/MAPK signaling pathways in hepatocellular carcinoma cells
Bibliographic record
Abstract
The complexity of hepatocellular carcinoma (HCC) signaling and the failure of pharmacological therapeutics reveal the significance of establishing new anti-cancer strategies. Interferon alpha (IFN-α) has been used as adjuvant therapy for reducing HCC recurrence and improving survival. Delta-tocotrienol (δ-tocotrienol), a natural unsaturated isoform of vitamin E, is a promising candidate for cancer treatment. In this study, we evaluated whether the combination of δ-tocotrienol with IFN-α displays significant advantages in the treatment of HCC cells. Results showed that the combination significantly decreased cell viability, migration and invasion of HCC cells compared with single therapies. Combining δ-tocotrienol and IFN-α enhanced the decrease in proliferating cell nuclear antigen (PCNA) and matrix metalloproteinase (MMP) 7 and MMP-9. The combination also produced an enhancement of apoptosis together with increased Bax/Bcl-xL ratio and reactive oxygen species (ROS) generation. δ-tocotrienol induced Notch1 activation and changes in Erk and p38 MAPK signaling status. Blocking experiments confirmed that ROS and Erk are involved, at least in part, in the anti-cancer effects of the combined treatment. In conclusion, the combination of δ-tocotrienol with IFN-α therapy showed promising results for HCC cell treatment, which makes the combination of cytokine-based immunotherapy with natural products a potential strategy against liver cancer.
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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".