Scutellarin Increases Cisplatin-Induced Apoptosis and Autophagy to Overcome Cisplatin Resistance in Non-small Cell Lung Cancer via ERK/p53 and c-met/AKT Signaling Pathways
Bibliographic record
Abstract
Cisplatin, as the first-line anti-tumor agent, is widely used for treatment of a variety of malignancies inluding non-small cell lung cancer (NSCLC). However, the acquired resistance has been a major obstacle for the clinical application. Scutellarin is a active flavone extracted from Erigeron breviscapus Hand-Mazz that has been shown to exhibit anticancer activities on various types of tumors. Here, we reported that scutellarin was capable of sensitizing A549/DDP cells to cisplatin by enhancing apoptosis and autophagy. Mechanistic analyses indicated that cisplatin-induced caspase-3-dependent apoptosis was elevated in the presence of scutellarin through activating ERK-mediated p53 pathway. Furthermore, scutellarin also promoted cisplatin-induced cytotoxic autophagy, downregulated expression of p-AKT and c-met. Deficiency of c-met reduced p-AKT level, and inhibition of p-AKT or c-met improved autophagy in A549/DDP cells. Interestingly, loss of autophagy attenuated the synergism of this combination. In vivo, the co-treatment of cisplatin and scutellarin notably reduced the tumour size when compared with cisplatin treatment alone. Notably, scutellarin significantly reduced the toxicity generated by cisplatin in tumor-bearing mice. This study identifies the unique role of scutellarin in reversing cisplatin resistance through apoptosis and autophagy, and suggests that combined cisplatin and scutellarin might be a novel therapeutic strategy for patients with NSCLC.
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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 | 0.001 | 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".