Troglitazone suppresses telomerase activity independently of PPARγ in estrogen-receptor negative breast cancer cells
Bibliographic record
Abstract
BACKGROUND: Breast cancer is one the highest causes of female cancer death worldwide. Many standard chemotherapeutic agents currently used to treat breast cancer are relatively non-specific and act on all rapidly dividing cells. In recent years, more specific targeted therapies have been introduced. It is known that telomerase is active in over 90% of breast cancer tumors but inactive in adjacent normal tissues. The prevalence of active telomerase in breast cancer patients makes telomerase an attractive therapeutic target. Recent evidence suggests that telomerase activity can be suppressed by peroxisome proliferator activated receptor gamma (PPARgamma). However, its effect on telomerase regulation in breast cancer has not been investigated. METHODS: In this study, we investigated the effect of the PPARgamma ligand, troglitazone, on telomerase activity in the MDA-MB-231 breast cancer cell line. Real time RT-PCR and telomerase activity assays were used to evaluate the effect of troglitazone. MDA-MB-231 cells had PPARgamma expression silenced using shRNA interference. RESULTS: We demonstrated that troglitazone reduced the mRNA expression of hTERT and telomerase activity in the MDA-MB-231 breast cancer cell line. Troglitazone reduced telomerase activity even in the absence of PPARgamma. In agreement with this result, we found no correlation between PPARgamma and hTERT mRNA transcript levels in breast cancer patients. Statistical significance was determined using Pearson correlation and the paired Student's t test. CONCLUSIONS: To our knowledge, this is the first time that the effect of troglitazone on telomerase activity in breast cancer cells has been investigated. Our data suggest that troglitazone may be used as an anti-telomerase agent; however, the mechanism underlying this inhibitory effect remains to be determined.
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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 | 0.001 |
| 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".