Ginsenoside metabolite compound K induces apoptosis and autophagy in non-small cell lung cancer cells via AMPK–mTOR and JNK pathways
Bibliographic record
Abstract
)-protopanaxadiol], as a metabolite of ginsenoside, has been verified to have antitumor effects in various cancers, including non-small cell lung cancer (NSCLC). However, the detailed mechanisms of C-K in NSCLC remain largely unknown. In this study, we aimed to evaluate the effect of C-K on apoptosis and autophagy in NSCLC cells as well as its related mechanisms. According to the results, C-K suppressed the proliferation, and led to G1 phase arrest and apoptosis in A549 and H1975 cells. Subsequently, C-K promoted autophagy, as confirmed by the enhanced rate of cells staining positive with acridine orange, increased levels of LC3II and Beclin-1, and with decreased levels of p62 in A549 and H1975 cells. Moreover, 3-methyladenine (3-MA; an inhibitor of autophagy) effectively suppressed the inhibition of proliferation and apoptosis that was induced with C-K. Finally, C-K treatment promoted the activation of the AMPK-mTOR and c-Jun N-terminal kinase (JNK) signaling pathways. Treatment with compound C (AMPK inhibitor) or SP600125 (JNK inhibitor) significantly restrained the inhibition of proliferation, apoptosis, and autophagy induced with C-K in A549 and H1975 cells. In conclusion, this study demonstrates that C-K promotes autophagy-mediated apoptosis in NSCLC via AMPK-mTOR and JNK signaling pathways.
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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) | 0.001 | 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".