Expression of DNA Methyltransferases in Breast Cancer Patients and to Analyze the Effect of Natural Compounds on DNA Methyltransferases and Associated Proteins
Bibliographic record
Abstract
PURPOSE: The DNA methylation mediated by specific DNA methyltransferases (DNMTs), results in the epigenetic silencing of multiple genes which are implicated in human breast cancer. We hypothesized that the natural compounds modulate the expression of DNMTs and their associated proteins in the breast cancer cell lines and affect the methylation mediated gene silencing. METHODS: The DNMTs transcript expression was analyzed by reverse transcription-polymerase chain reaction (RT-PCR) in the tumors and the adjacent normal breast tissues of the patients with invasive ductal breast carcinoma. We tested the hypothesis that the natural compounds, viz., epigallocatechin gallate (EGCG), genistein, withaferin A, curcumin, resveratrol, and guggulsterone, have demethylation potential. To investigate this hypothesis, we analyzed the DNMTs expression at the transcript levels, followed by the analysis of DNMT1 and its associated proteins (HDAC1, MeCP2, and MBD2). RESULTS: The increased DNMTs transcripts expression, viz., DNMT1, DNMT3a, and DNMT3b, in the breast cancer tissues suggest involvement of the DNMTs in the breast carcinogenesis. Quantitative RT-PCR analysis revealed that the treatment with natural compounds, viz., EGCG, genistein, withaferin A, curcumin, resveratrol, and guggulsterone, resulted in a significant decrease in the transcript levels of all the DNMTs investigated. Importantly, these natural compounds decreased the protein levels of DNMT1, HDAC1, and MeCP2. CONCLUSION: Our results demonstrate that the natural compounds, EGCG, genistein, withaferin A, curcumin, resveratrol, and guggulsterone, have the potential to reverse the epigenetic changes. Moreover, their lack of toxicity makes these natural compounds promising candidates for the chemoprevention of the breast cancer. In-depth future mechanistic studies aimed to elucidate how these compounds affect the gene transcription are warranted.
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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) | 0.002 | 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".