Peroxisome Proliferator-Activated Receptor γ Inhibition Prevents Adhesion to the Extracellular Matrix and Induces Anoikis in Hepatocellular Carcinoma Cells
Bibliographic record
Abstract
Activation of the nuclear transcription factor peroxisome proliferator-activated receptor gamma (PPARgamma) inhibits growth and survival of hepatocellular carcinoma (HCC) cell lines. To further investigate the function of PPARgamma in HCC, PPARgamma expression patterns in primary tumors were examined, and the responses of two HCC cell lines to PPARgamma activation and inhibition were compared. PPARgamma expression was increased in HCC and benign-appearing peritumoral hepatocytes compared with remote benign hepatocytes. Both compound PPARgamma inhibitors and PPARgamma small interfering RNAs prevented HCC cell lines from adhering to the extracellular matrix. Loss of adhesion was followed by caspase-dependent apoptosis (anoikis). PPARgamma inhibitors had no effect on initial beta1 integrin-mediated adhesion, or on total focal adhesion kinase levels but did reduce focal adhesion kinase phosphorylation. The PPARgamma inhibitor T0070907 was significantly more efficient at causing cancer cell death than the activators troglitazone and rosiglitazone. T0070907 caused cell death by reducing adhesion and inducing anoikis, whereas the activators had no direct effect on adhesion and caused cell death at much higher concentrations. In conclusion, PPARgamma overexpression is present in HCC. Inhibition of PPARgamma function causes HCC cell death by preventing adhesion and inducing anoikis-mediated apoptosis. PPARgamma inhibitors represent a potential novel treatment approach to HCC.
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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".