PPAR γ agonist, pioglitazone, suppresses melanoma cancer in mice by inhibiting TLR4 signaling
Bibliographic record
Abstract
BACKGROUND: Although previous studies demonstrated an anticancer effect for the ligands of peroxisome proliferator-activated receptor gamma (PPARγ) through activation of its anti-inflammatory responses, nevertheless the anti-tumor mechanism of PPARγ has not been intensively investigated. One of the molecules involved in cancer progression is toll-like receptor 4 (TLR4). METHODS: B16F10 melanoma cells were cultured with or without LPS for 24 hr. The cells were subcutaneously injected to two groups of C57BL/6 mice. After the development of palpable tumors each group of animals were divide to four sub-groups and received pioglitazone in different dose ranges (0,10,50,100 mg/kg/day) for 10 days. At the end of the study, the expression of Tlr4, Myd-88, Nf-kb1 genes was evaluated by qRT-PCR in different groups in mice tumor. The TLR-4 protein expression was evaluated by IHC. TNF-α level in mice tumor and serum were measured by ELISA kits. Tumor volume was measured with Vernier calipers. RESULTS: We observed that activation of PPARγ by its agonist, pioglitazone, reduces tumor volume, Tlr-4, Myd-88, Nf-kb1 mRNA expression, TLR4 protein expression and TNF-α production in melanoma tumor especially in groups that were injected with LPS -stimulated cells. Moreover, treatment of melanoma cells with pioglitazone showed that the inhibitory effects of pioglitazone on LPS-induced inflammatory responses were TLR4 dependent. CONCLUSIONS: The results indicate that pioglitazone, a PPARγ agonist, has a beneficial protective effect against melanoma via interfering with the TLR4-dependent signaling pathways.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".