Comparative effects of retinoic acid, vitamin D and resveratrol alone and in combination with adenosine analogues on methylation and expression of phosphatase and tensin homologue tumour suppressor gene in breast cancer cells
Bibliographic record
Abstract
Aberrations in DNA methylation patterns have been reported to be involved in driving changes in the expression of numerous genes during carcinogenesis and have become promising targets for chemopreventive action of natural compounds. In the present study, we investigated the effects of all-trans retinoic acid (ATRA), vitamin D₃ and resveratrol alone and in combination with adenosine analogues, 2-chloro-2'-deoxyadenosine (2CdA) and 9-β-d-arabinosyl-2-fluoroadenine (F-ara-A), on the methylation and expression of phosphatase and tensin homologue (PTEN) tumour suppressor gene in MCF-7 and MDA-MB-231 breast cancer cells. The present results showed that in non-invasive MCF-7 cells, ATRA, vitamin D₃ and resveratrol possess high efficacy in the reduction of PTEN promoter methylation. It was associated with PTEN induction as well as DNA methyltransferase down-regulation and p21 up-regulation after treatments with vitamin D₃ and resveratrol, suggesting a complex regulation of the DNA methylation machinery. Vitamin D₃ and resveratrol improved the inhibitory effects of 2CdA and F-ara-A on PTEN methylation in MCF-7 cells; however, only the combined action of vitamin D₃ and 2CdA boosted the induction of PTEN expression, suggesting a cooperation of these compounds in additional processes driving changes in PTEN expression. In contrast, in highly invasive MDA-MB-231 cells, only vitamin D₃ reduced PTEN methylation and induced its expression without notable effects in combined treatments. The present results suggest that natural compounds can find application in epigenetic anticancer therapy aimed at inhibition of promoter methylation of tumour suppressor genes and induction of their expression at early stages of carcinogenesis.
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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".