Troglitazone reverses the multiple drug resistance phenotype in cancer cells
Bibliographic record
Abstract
A major problem in treating cancer is the development of drug resistance. We previously demonstrated doxorubicin (DOX) resistance in K562 human leukemia cells that was associated with upregulation of glyoxalase 1 (GLO-1) and histone H3 expression. The thiazolidinedione troglitazone (TRG) downregulated GLO-1 expression and further upregulated histone H3 expression and post-translational modifications in these cells, leading to a regained sensitivity to DOX. Given the pleiotropic effects of epigenetic changes in cancer development, we hypothesized that TRG may downregulate the multiple drug resistance (MDR) phenotype in a variety of cancer cells. To test this, MCF7 human breast cancer cells and K562 cells were cultured in the presence of low-dose DOX to establish DOX-resistant cell lines (K562/DOX and MCF7/DOX). The MDR phenotype was confirmed by Western blot analysis of the 170 kDa P-glycoprotein (Pgp) drug efflux pump multiple drug resistance protein 1 (MDR-1), and the breast cancer resistance protein (BCRP). TRG markedly decreased expression of both MDR-1 and BCRP in these cells, resulting in sensitivity to DOX. Silencing of MDR-1 expression also sensitized MCF7/DOX cells to DOX. Use of the specific and irreversible peroxisome proliferator-activated receptor gamma (PPARgamma) inhibitor GW9662 in the nanomolar range not only demonstrated that the action of TRG on MCF/DOX was PPARgamma-independent, but indicated that PPARgamma may play a role in the MDR phenotype, which is antagonized by TRG. We conclude that TRG is potentially a useful adjunct therapy in chemoresistant cancers.
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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) | 0.001 | 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) | 0.002 | 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".