Anticancer Drug-Induced Epithelial-Mesenchymal Transition via p53/miR-34a axis in A549/ABCA3 Cells
Bibliographic record
Abstract
PURPOSE: Several anticancer drugs including bleomycin (BLM) and methotrexate (MTX) cause serious lung diseases such as pulmonary fibrosis. Although evidences showing the association of epithelial-mesenchymal transition (EMT) with pulmonary fibrosis are increasing, the mechanism underlying anticancer drug-induced EMT has been poorly understood. On the other hand, miR-34a, a non-coding small RNA, has been highlighted as a key factor to regulate EMT in lung. In this study, we elucidated the role of miR-34a in anticancer drug-induced EMT using A549/ABCA3 cells as a novel type II alveolar epithelium model. METHODS: Expression levels of α-smooth muscle actin (α-SMA) mRNA, miR-34a, and p53 were evaluated by real-time PCR and western blot analysis, respectively. RESULTS: BLM and MTX induced EMT-like morphological changes and increase in mRNA expression level of α-SMA, an EMT marker. Also, both drugs increased the expression level of miR-34a. Furthermore, mRNA expression level of α-SMA was enhanced by introduction of miR-34a mimic into A549/ABCA3 cells. To examine the mechanism underlying drug-induced enhancement of miR-34a expression, we focused on p53/miR-34a axis. Both drugs upregulated protein expression of p53, an inducer of miR-34a, as well as phosphorylation of Ser15 in p53. CONCLUSIONS: These findings indicated that p53/miR-34a axis may contribute to anticancer drug-induced EMT in type II alveolar epithelial 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 | 0.001 |
| 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".