Compound K-enriched Korean red ginseng prevents lung cancer progression by targeting cancer cells and fibroblasts
Bibliographic record
Abstract
Background Despite the efficacy of anticancer drugs , patients frequently experience relapse, metastasis, and resistance. A promising therapeutic approach not only targets cancer cell growth but also modulates cancer-associated fibroblasts, which support malignancies. Compound K (CK), a metabolite derived from red ginseng , has demonstrated anticancer properties. Recently, we developed a CK-enriched red ginseng extract (CKP) and explored its potential to suppress lung cancer by inhibiting cancer cell proliferation and inactivating fibroblasts. Methods To evaluate the in vitro efficacy of CKP in inhibiting lung cancer cell proliferation, MTT and colony formation assays were performed. The apoptotic effects of CKP on lung cancer cells were assessed using Western blot and flow cytometry. Furthermore, the ability of CKP to inhibit TGF β1-induced migration of cancer cells was investigated through Western blot, RT-PCR, and a wound healing assay. Additionally, the impact of CKP on lung fibroblast inactivation was examined via Western blot and RT-PCR analysis. For in vivo experiments, a xenograft model was utilized, incorporating a combination of lung cancer cells and lung fibroblasts in xenografts. Results CKP significantly reduced the proliferation and invasiveness of TGF-β1-stimulated A549 cells, demonstrating its potential to inactivate lung fibroblasts. Additionally, CKP inhibited the secretion of cytokines, such as interleukin-6, interleukin-8, and TGF-β1, by activated fibroblasts. In vivo , CKP markedly inhibited tumor growth in the xenograft model. Conclusion In conclusion, CKP effectively induced apoptosis in lung cancer cells, suppressed metastasis, and inactivated fibroblasts, thereby preventing cancer invasion and reducing extracellular matrix production, highlighting its potential as a novel anticancer agent.
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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".