Chestnut Leaf Extract Increases Sensitivity of Breast Cancer Stem Cells to Paclitaxel by Inhibiting Nrf2 Activity
Bibliographic record
Abstract
Tumor cells consist of various clonal subpopulations, thus displaying heterogeneous tumorigenicity. Only a subset of tumor cells, called cancer stem cells (CSCs), uniquely possesses high potency to initiate tumorigenesis and to locally or systematically disseminate tumor cells. Based on their properties, CSCs come to be a critical target for the development of chemotherapeutic strategies. Interestingly, it has been known that Nrf2‐mediated antioxidant enzymes are highly expressed in CSCs, which may be responsible for lowering the intracellular ROS level and the vulnerability to chemotherapeutic agents. As anticancer drugs usually utilize ROS as an arsenal for killing cancer cells, we hypothesized that suppression of Nrf2 activity may increase susceptibility of CSCs to anticancer drugs, improving their therapeutic efficacy. Our findings demonstrate that MCF‐7 CSCs having a CD44 high /CD24 low phenotype formed mammospheres and highly expressed Nrf2 compared to original MCF‐7 cells. In addition, total 89 kinds of Korean edible plant extracts were screened for the inhibitory activity against Nrf2 signaling pathway using an ARE‐luciferase assay system. Among these samples, Chestnut ( Castanea crenata ) leaf extract dramatically decreased nuclear translocation of Nrf2 and its downstream protein expression of antioxidant enzymes in MCF‐7 CSCs, and also attenuated the resistance to paclitaxel. These findings suggest that Chestnut leaf extract or its constituents could be utilized to increase therapeutic efficacy of anticancer agents through inhibition of Nrf2 signaling pathway. Support or Funding Information This study was supported by the National Research Foundation (Grant No. R2014R1A2A2A01005773, funded by the Ministry of Science, ICT and Future Planning; Grant No. 2013R1A1A2013362, funded by the Ministry of Education) Republic of Korea.
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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".