Thiazolidinedione drugs down-regulate CXCR4 expression on human colorectal cancer cells in a peroxisome proliferator activated receptor γ-dependent manner
Bibliographic record
Abstract
Peroxisome proliferator activated receptor (PPAR) gamma is a nuclear receptor involved primarily in lipid and glucose metabolism. PPARgamma is also expressed in several cancer types, and has been suggested to play a role in tumor progression. PPARgamma agonists have been shown to reduce the growth of colorectal carcinoma cells in culture and in xenograft models. Furthermore, the PPARgamma agonist thiazolidinedione has been shown to reduce metastasis in a murine model of rectal cancer. Since the chemokine receptor CXCR4 has emerged as an important player in tumorigenesis, particularly in the process of metastasis, we sought to determine if PPARgamma agonists might act in part by reducing CXCR4 expression. We found that rosiglitazone, a thiazolidinedione PPARgamma agonist used primarily in the treatment of type 2 diabetes, significantly reduced cell-surface expression of CXCR4 protein on HT-29 human colorectal carcinoma cells. This effect occurred at concentrations as low as 1 nM, and was first evident after 8 h of drug exposure. CXCR4 mRNA was also down-regulated after treatment with rosiglitazone, indicating that the effect occurs at the level of transcription. Four other thiazolidinedione compounds (ciglitazone, pioglitazone, troglitazone, and MCC555) also significantly reduced CXCR4 expression. To confirm the involvement of PPARgamma in thiazolidinedione-induced CXCR4 down-regulation, we used PPARgamma antagonists GW9662 and T0070907, both of which completely blocked the effect of rosiglitazone on CXCR4 expression. Furthermore, HT-29 cells in which PPARgamma expression was reduced using shRNA were less responsive to rosiglitazone. In conclusion, we have shown that thiazolidinedione compounds reduce CXCR4 mRNA and cell-surface protein expression in a PPARgamma-dependent manner.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".