Scutellarin induces apoptosis and autophagy in NSCLC cells through ERK1/2 and AKT Signaling Pathways in vitro and in vivo
Bibliographic record
Abstract
Curative molecular therapy for non-small cell lung cancer (NSCLC) is still lacking. Scutellarin, an active flavone extracted from Erigeron breviscapus Hand-Mazz, displays anti-tumor property in diverse cancer types, yet its tumor-suppressive effect on NSCLC is not reported. In this study, we found that scutellarin significantly inhibited the proliferation of NSCLC cells, induced cell apoptosis, and triggered autophagy. Notably, inhibition of autophagy with inhibitor HCQ attenuated the anti-proliferative activity of scutellarin, indicating that scutellarin-induced autophagy is antineoplastic. In addition, HCQ treatment reduced scutellarin-induced apoptosis. Further study demonstrated that scutellarin stimulated phosphorylation of ERK1/2, and inhibition of ERK1/2 with inhibitor U0126 markedly attenuated scutellarin-induced autophagy. Similarly, scutellarin downregulated the expression of p-AKT, and AKT inhibitor MK-2206 induced autophagy. Moreover, there also existed crosstalk between ERK and AKT pathways. Finally, in vivo xenograft nude mice experiment proved that scutellarin treatment significantly reduced tumor growth and increased the levels of LC3-II and p-ERK1/2, suppressed p-AKT in mice tumors. Thus, our study for the first time uncovered the anti-cancer function of scutellarin on NSCLC cells, and might provide a potential novel therapy for treatment of 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 | 0.001 |
| 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) | 0.001 | 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".