PPARα agonist fenofibrate suppresses tumor growth through direct and indirect angiogenesis inhibition
Bibliographic record
Abstract
Angiogenesis and inflammation are central processes through which the tumor microenvironment influences tumor growth. We have demonstrated recently that peroxisome proliferator-activated receptor (PPAR)alpha deficiency in the host leads to overt inflammation that suppresses angiogenesis via excess production of thrombospondin (TSP)-1 and prevents tumor growth. Hence, we speculated that pharmacologic activation of PPARalpha would promote tumor growth. Surprisingly, the PPARalpha agonist fenofibrate potently suppressed primary tumor growth in mice. This effect was not mediated by cancer-cell-autonomous antiproliferative mechanisms but by the inhibition of angiogenesis and inflammation in the host tissue. Although PPARalpha-deficient tumors were still susceptible to fenofibrate, absence of PPARalpha in the host animal abrogated the potent antitumor effect of fenofibrate. In addition, fenofibrate suppressed endothelial cell proliferation and VEGF production, increased TSP-1 and endostatin, and inhibited corneal neovascularization. Thus, both genetic abrogation of PPARalpha as well as its activation by ligands cause tumor suppression via overlapping antiangiogenic pathways. These findings reveal the potential utility of the well tolerated PPARalpha agonists beyond their use as lipid-lowering drugs in anticancer therapy. Our results provide a mechanistic rationale for evaluating the clinical benefits of PPARalpha agonists in cancer treatment, alone and in combination with other therapies.
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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".