Role of a Polyphenol-Enriched Blueberry Preparation on Inhibition of Melanoma Cancer Stem Cells and Modulation of MicroRNAs
Bibliographic record
Abstract
Melanoma is a type of skin cancer known for its high mortality rate. Cancer stem cells (CSCs) are a subpopulation of cancer cells that significantly contribute to tumour recurrence and differentiation. Epigenetic-specific changes involving miRNAs maintain CSCs. Plant polyphenols have been reported to be involved in cancer chemoprevention and chemotherapy, with miRNAs being the novel effectors in their biological activities. A polyphenol-enriched blueberry preparation (PEBP) derived from fermented blueberries has demonstrated promising chemopreventative properties on breast cancer stem cells by influencing inflammatory pathways and miRNAs. In our current investigation, we seek to unveil the impact of PEBP on inhibiting melanoma development and to elucidate the underlying mechanisms. Our study employs various human cell lines, including an ex vivo cell line derived from a patient's metastatic tumour. We found that it elevates miR-200c, increasing E-cadherin expression and inhibiting miR-210-3p through NF-κB signalling, impacting Epithelial-to-Mesenchymal Transition (EMT), a critical process in cancer progression. PEBP increases the SOCS1 expression, potentially contributing to miR-210-3p inhibition. Experiments involving miRNA manipulation confirm their functional roles. The study suggests that PEBP's anti-inflammatory effects involve regulating miR-200c and miR-210 expression and their targets in EMT-related pathways. The overall aim is to provide evidence-based supportive care and preclinical evaluation of PEBP, offering a promising strategy for skin cancer chemoprevention.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".