Upregulation of Stat1-HDAC4 confers resistance to etoposide through enhanced multidrug resistance 1 expression in human A549 lung cancer cells
Post-publication record
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Bibliographic record
Abstract
Despite efforts to develop efficient chemotherapeutic drug strategies to treat cancer, acquired drug resistance is a commonly encountered problem. In the present study, to investigate this phenomenon, human A549 lung cancer cells resistant to the topoisomerase inhibitor etoposide (A549RT‑eto) were used and compared with A549 parental cells. A549RT‑eto cells demonstrated increased resistance to etoposide‑induced apoptosis when compared with A549 parental cells. Notably, A549RT‑eto cells were observed to exhibit greater levels of histone deacetylase 4 (HDAC4), phospho‑Stat1 and P‑glycoprotein [P‑gp; encoded by the multidrug resistance 1 (MDR1) gene], compared with A549 cells. To address whether HDAC4 protein is involved in etoposide resistance in A549 cells, A549RT‑eto cells were treated with trichostatin A (TSA; an HDAC inhibitor) during etoposide treatment. The combined treatment was demonstrated to enhance etoposide‑induced apoptosis and reduce expression levels of HDAC4, P‑gp and phospho‑Stat1. In addition, the suppression of Stat1 with siRNA enhanced etoposide‑induced apoptosis and reduced the expression levels of HDAC4 and P‑gp, suggesting that Stat1 is essential in the regulation of resistance to etoposide, and in the upregulation of P‑gp. Notably, TSA treatment reduced P‑gp transcript levels but Stat1 siRNA treatment did not, suggesting that P‑gp is regulated by HDAC at the transcriptional level and by Stat1 at the post‑transcriptional level. These results suggest that the upregulation of Stat1 and HDAC4 determines etoposide resistance through P‑gp expression in human A549 lung cancer cells.
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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) | 0.002 | 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".