Abstract 3206: A novel prodrug of the green tea polyphenol (−)-epigallocatechin-3-gallate inhibits the telomerase gene by modifying DNA methylation and histone acetylation in breast cancer cells
Bibliographic record
Abstract
Abstract Epigallocatechin-3-gallate (EGCG), a major component of green tea polyphenols (GTPs), has been reported to down-regulate telomerase activity in breast cancer cells thereby increasing cellular apoptosis and inhibiting cellular proliferation. However, the major concerns with GTPs and EGCG are the bioavailability and poor stability under physiological conditions. In the present study, we have observed that treatments with EGCG and a novel pro-drug of EGCG (Pro-EGCG) dose-dependently inhibit the cellular proliferation of human breast carcinoma MCF-7 and MDA-MB-231 cells. Inhibition of telomerase has received wide attention in breast cancer prevention because of its unique expression in cancer cells and low level of expression in normal mammary epithelial cells. Both EGCG and Pro-EGCG inhibited the transcription of hTERT (human telomerase reverse transcriptase) by epigenetic modification in MCF-7 and MDA-MB-231 cells. The inhibition of hTERT expression was consistently correlated with hTERT promoter hypomethylation and deacetylation. Chromatin immunoprecipitation (ChIP) analysis of the hTERT promoter revealed that EGCG and Pro-EGCG increased the level of inactive trimethyl H3K9, but decreased active dimethyl H3K4 and acetyl H3K9. In addition, EGCG and Pro-EGCG significantly inhibited DNA methyltransferase (DNMT) activity, which might have an important role in the down-regulation of hTERT by demethylation of the CpG sites in the hTERT promoter in breast cancer cells. Bisulfite sequencing of the hTERT promoter confirmed the induced hypomethylation of the hTERT promoter by EGCG and Pro-EGCG. Down-regulation of hTERT was also mediated through significant inhibition of histone acetyltransferase (HAT), an enzyme responsible for histone acetylation, by EGCG and Pro-EGCG. Histone deacetylase (HDAC) activity was not altered by EGCG and Pro-EGCG, which reveals that EGCG- and Pro-EGCG-induced histone deacetylation was mediated through inhibition of HAT, but not by effects on HDACs in these breast cancer cells. These results show that EGCG and Pro-EGCG are inhibiting the cellular proliferation and inducing apoptosis in these breast carcinoma cells but not in normal control mammary epithelial MCF10A cells, at least in part, through epigenetic modification of telomerase expression. Further, our results also show that Pro-EGCG is more stable than EGCG and required a lesser dose than EGCG to facilitate the same cellular and epigenetic effects in breast carcinoma cells. Collectively, our data provide new insights into breast cancer prevention through epigenetic modulation of telomerase by using Pro-EGCG, a more stable form of EGCG, as a novel chemopreventive drug. Note: This abstract was not presented at the AACR 101st Annual Meeting 2010 because the presenter was unable to attend. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 3206.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".