Epigenetic Effects of Resveratrol on Oncogenic Signaling in Breast Cancer
Bibliographic record
Abstract
The crosstalk between oncogenic signaling pathways plays a crucial role in driving cancer development. We previously demonstrated that dietary polyphenols, specifically resveratrol (RSV) and other stilbenoids, epigenetically target oncogenes for silencing via DNA hypermethylation in breast cancer. In the present study, we identify signal transduction regulators among RSV-hypermethylated targets and investigate the functional role of RSV-mediated DNA hypermethylation in the regulation of Hedgehog and Wnt signaling. Non-invasive ER-positive MCF-7 and highly invasive triple-negative MCF10CA1a human breast cancer cell lines were used as experimental models. Upon 9-day exposure to 15 µM RSV, pyrosequencing and qRT-PCR were performed to assess DNA methylation and expression of GLI2 and WNT4, which are upstream regulators of the Hedgehog and Wnt pathways, respectively. Our results showed that RSV led to a DNA methylation increase within GLI2 and WNT4 enhancers, which was accompanied by decreases in gene expression. Consistently, we observed the downregulation of genes downstream of the Hedgehog and Wnt signaling, including common targets shared by both pathways, CCND1 and CYR61. Further analysis using chromatin immunoprecipitation identified increased H3K27 trimethylation and decreased H3K9 and H3K27 acetylation, along with abolishing OCT1 transcription factor binding. Those changes indicate a transcriptionally silent chromatin state at GLI2 and WNT4 enhancers. The inhibition of the Wnt signal transduction was confirmed using a phospho-antibody array that demonstrated suppression of positive and stimulation of negative Wnt regulators. In conclusion, our results provide scientific evidence for dietary polyphenols as epigenetics-modulating agents that act to re-methylate and silence oncogenes, reducing the oncogenic signal transduction. Targeting such an action could be an effective strategy in breast cancer prevention and/or adjuvant therapy.
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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".