Activation of PPARα inhibits IGF‐I‐mediated growth and survival responses in medulloblastoma cell lines
Bibliographic record
Abstract
Recent studies suggest a potential role of lipid lowering drugs, fibrates and statins, in anticancer treatment. One candidate for tumor chemoprevention is fenofibrate, which is a potent agonist of peroxisome proliferator activated receptor alpha (PPARalpha). Our results demonstrate elevated expression of PPARalpha in the nuclei of neoplatic cells in 12 out of 13 cases of medulloblastoma, and of PPARgamma in six out of 13 cases. Further analysis demonstrated that aggressive mouse medulloblastoma cells, BsB8, express PPARalpha in the absence PPARgamma, and human medulloblastoma cells, D384 and Daoy, express both PPARalpha and PPARgamma. Mouse and human cells responded to fenofibrate by a significant increase of PPAR-mediated transcriptional activity, and by a gradual accumulation of cells in G1 and G2/M phase of the cell cycle, leading to the inhibition of cell proliferation and elevated apoptosis. Preincubation of BsB8 cells with fenofibrate attenuated IGF-I-induced IRS-1, Akt, ERKs and GSK3beta phosphorylation, and inhibited clonogenic growth. In Daoy and D384 cells, fenofibrate also inhibited IGF-I-mediated growth responses, and simultaneous delivery of fenofibrate with low dose of the IGF-IR inhibitor, NVP-AEW541, completely abolished their clonogenic growth and survival. These results indicate a strong supportive role of fenofibrate in chemoprevention against IGF-I-induced growth responses in medulloblastoma.
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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 | 0.000 | 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 | 0.001 |
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".