Anticancer Effects of Combined γ-Tocotrienol and PPARγ-Antagonist Treatment are Associated with a Suppression in Adipogenic Factor Expression
Bibliographic record
Abstract
Cancer cells reprogram their metabolism to meet the demands of accelerated growth. Glucose is the primary source of energy for cancer cells, but under conditions of high-energy demand lipids and free fatty acids become increasingly important. PPARγ is a member of the nuclear receptor superfamily and acts to regulate adipocyte differentiation and lipid metabolism. However, in many types of cancer, PPARγ activity is elevated in order to increase production of adipogenic factors [1, 2]. γ-Tocotrienol is an isoform of vitamin E that displays potent anticancer activity [3]. Previous studies have shown that the antiproliferative effects of combined treatment of ã-tocotrienol with PPAR?antagonists was associated with a reduction in PPARγ activity, expression of PPAR? and RXR, and suppression in Akt activation in MCF-7 and MBA-MB-231 human breast cancer cells [4]. The present study was conducted to determine the effects of combination treatment with these agents on adipogenic factor levels in rapidly proliferating human breast cancer cells. Western blot and qRT-PCR studies showed that combined treatment of ?-tocotrienol with PPAR?antagonists not only suppressed the adipogenic proteins, C/EBPγ and SREBP-1c, but also decreased their target lipogenic enzymes, ap2, FAS, and HMGCoR. However, treatment effects were also observed in PPARγ silenced breast cancer cells, indicating that these effects are mediated through PPARγ-independent mechanism. These findings suggest the combined treatment of γ-tocotrienol with PPARγ antagonist may have potential as a therapeutic strategy in the treatment of breast 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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".