Extract of Bryophyllum laetivirens reverses etoposide resistance in human lung A549 cancer cells by downregulation of NF-κB
Bibliographic record
Abstract
Since multidrug resistance (MDR) is one of the main reasons for failure in cancer treatment, its suppression may increase the efficacy of cancer therapy. In the present study we attempted to identify a new and effective anticancer drug against MDR cancer cells. We first found that lung cancer A549 cells resistant to etoposide (A549RT-eto) exhibit upregulation of NF-κB and SIRT1 in comparison to A549 parental cells. During a search for anticancer drug candidates from medicinal plant sources, we found that an extract fraction (F14) of Bryophyllum laetivirens leaves downregulated expression of NF-κB and SIRT1, sensitizing the levels of A549RT-eto cells to apoptosis through downregulation of P-glycoprotein (P-gp), which is encoded by the MDR1 gene. To address whether NF-κB is involved in resistance to etoposide through P-gp, we treated A549RT-eto cells with Bay11-7802, an inhibitor of NF-κB. We then observed that Bay11-7802 treatment reduced P-gp expression levels, and furthermore combined treatment with the F14 extract and Bay11-7802 accelerated apoptosis through a decrease in P-gp levels, suggesting that NF-κB is involved in MDR. To address whether upregulation of SIRT1 is involved in resistance to etoposide through P-gp, we treated A549RT-eto cells with SIRT1 siRNA or nicotinamide (NAM), an inhibitor of SIRT1. we found that suppression of SIRT1 did not reduce P-gp levels. furthermore, the combined treatment with the F14 extract, and SIRT1 siRNA or NAM did not accelerate apoptosis, indicating that SIRT1 is not involved in the regulation of P-gp levels in A549RT-eto cells. Taken together, we suggest that upregulation of NF-κB determines etoposide resistance through P-gp expression in human A549 lung cancer cells. We herein demonstrated that B. laetivirens extract reverses etoposide resistance in human A549 lung cancer cells through downregulation of NF-κB.
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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 | 0.001 |
| 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".