PPARγ is dispensable for clear cell renal cell carcinoma progression
Bibliographic record
Abstract
OBJECTIVE: Clear cell renal cell carcinoma (ccRCC) is a subtype of kidney cancer defined by robust lipid accumulation, which prior studies have indicated plays an important role in tumor progression. We hypothesized that the peroxisome proliferator-activated receptor gamma (PPARγ), detected in both ccRCC tumors and cell lines, promotes lipid storage in ccRCC and contributes to tumorigenesis in this setting. PPARγ transcriptionally regulates a number of genes involved in lipid and glucose metabolism in adipocytes, yet its role in ccRCC has not been described. The objective of this study was to elucidate endogenous PPARγ function in ccRCC cells. METHODS AND RESULTS: Using chromatin immunoprecipitation followed by deep sequencing (ChIP-seq), we found that PPARγ and its heterodimer RXR occupy the canonical DR1 PPAR binding motif at approximately 1000 locations throughout the genome that can be subdivided into adipose-shared and ccRCC-specific sites. CRISPR-Cas9 mediated, loss-of-function studies determined that PPARγ is dispensable for viability, proliferation, and migration of ccRCC cells in vitro and in vivo. Also, surprisingly, PPARγ deletion had little effect on the robust lipid accumulation that typifies the "clear cell" phenotype of kidney cancer. CONCLUSION: Our results suggest that PPARγ plays neither a tumor suppressive nor oncogenic role in advanced ccRCC, and thus single-agent therapeutics targeting PPARγ are unlikely to be effective for the treatment of this disease. The unique cistrome of PPARγ in ccRCC cells demonstrates the importance of cell type in determining the functions of PPARγ.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".