Peroxisome proliferator-activated receptor γ in bladder cancer: A promising therapeutic target
Bibliographic record
Abstract
Peroxisome Proliferator-Activated Receptors (PPARs) are ligand-activated intracellular transcription factors, members of the nuclear hormone receptor superfamily. The PPAR subfamily consist of three subtypes encoded by distinct genes denoted PPARalpha, PPARbeta/delta, and PPARgamma. The peroxisome proliferator-activated receptor gamma (PPARgamma) is the most extensively studied subtype of the PPARs. Over the last decade, research on PPARgamma unveiled its role in important biological processes, including lipid biosynthesis, glucose metabolism, anti-inflammatory response, and atherosclerosis. Recently, PPARgamma has been shown to be expressed in many cancers including, lung, prostate, bladder, colon, breast, duodenal, thyroid, and has been demonstrated to potentially play an important role in carcinogenesis. In bladder cancer, PPARgamma ligands such as troglitazone and 15d-PGJ2 have shown to inhibit tumor growth. We have recently published the first report to show that a new class of PPARgamma agonists, PPARgamma-active C-DIMs, which are more potent than the previous generation of drugs, exhibit anti-tumorigenic activity against bladder cancer cells in vitro and bladder tumors in vivo. The following review will discuss the molecular structure of PPARgamma, its function and its role in cancer biology and how it is emerging as a promising therapeutic target in bladder 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".