A Novel Prodrug of Epigallocatechin-3-gallate: Differential Epigenetic <i>hTERT</i> Repression in Human Breast Cancer Cells
Bibliographic record
Abstract
Epigallocatechin-3-gallate (EGCG), a major component of green tea polyphenols (GTP), has been reported to downregulate telomerase activity in breast cancer cells thereby increasing cellular apoptosis and inhibiting cellular proliferation. However, the major concerns with GTPs are their bioavailability and stability under physiologic conditions. In the present study, we show that treatments with EGCG and a novel prodrug of EGCG (pro-EGCG or pEGCG) dose- and time-dependently inhibited the proliferation of human breast cancer MCF-7 and MDA-MB-231 cells but not normal control MCF10A cells. Furthermore, both EGCG and pro-EGCG inhibited the transcription of hTERT (human telomerase reverse transcriptase), the catalytic subunit of telomerase, through epigenetic mechanisms in estrogen receptor (ER)-positive MCF-7 and ER-negative MDA-MB-231 cells. The downregulation of hTERT expression was found to be because of hTERT promoter hypomethylation and histone deacetylations, mediated at least partially through inhibition of DNA methyltransferase and histone acetyltransferase activities, respectively. In addition, we also observed that EGCG and pEGCG can remodel chromatin structures of the hTERT promoter by decreasing the level of acetyl-H3, acetyl-H3K9, and acetyl-H4 to the hTERT promoter. EGCG and pEGCG induced chromatin alterations that facilitated the binding of many hTERT repressors such as MAD1 and E2F-1 to the hTERT regulatory region. Depletion of E2F-1 and MAD1 by using siRNA reversed the pEGCG downregulated hTERT expression and associated cellular apoptosis differently in ER-positive and ER-negative breast cancer 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 compound.
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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) | 0.000 | 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 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".