miRNA-200b Signature in the Prevention of Skin Cancer Stem Cells by Polyphenol-enriched Blueberry Preparation
Bibliographic record
Abstract
Exposure of the skin to solar UV radiation leads to inflammation, DNA damage, and dysregulation of cellular signaling pathways, which may cause skin cancer. Photochemoprevention with natural products is an effective strategy for the control of cutaneous neoplasia. Polyphenols have been proven to help prevent skin cancer and to inhibit the growth of cancer stem cells (CSCs) through epigenetic mechanisms, including modulation of microRNAs expression. Thus, the current study aimed to assess the effect of polyphenol enriched blueberry preparation (PEBP) or non-fermented blueberry juice (NBJ) on expression of miRNAs and target proteins associated with different clinicopathological characteristics of skin cancer such as stemness, motility, and invasiveness. We observed that PEBP significantly inhibited the proliferation of skin CSCs derived from different melanoma cell lines, HS 294T and B16F10. Moreover, PEBP was able to reduce the formation of melanophores. We also showed that the expression of the CD133 + stem cell marker in B16F10 and HS294T cell lines was significantly decreased after treating the cells with PEBP in comparison to the NBJ and control groups. Importantly, tumor suppressors' miR-200s, involved in the regulation of the epithelial-to-mesenchymal transition and metastasis, were strikingly upregulated. In addition, we have shown that a protein target of the tumor suppressor miR200b, ZEB1, was also significantly modulated. Thus, the results demonstrates that PEBP possesses potent anticancer and anti-metastatic potentials and may represent a novel chemopreventative agent against skin cancer.
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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".