Therapeutic Effect of Epigallocatechin-3-gallate (EGCG) and Silibinin on ATM Dynamics in Prostate Cancer Cell Line LNCaP
Bibliographic record
Abstract
BACKGROUND: Epigallocatechin-3-gallate (EGCG) is a major ingredient of green tea (GT) and silibinin (SB), the active component of Silymarin presumably hold a potential to prevent pathogenomics. Prostate cancer exacerbation is triggered by fusion transcripts formed because of genomic instability induced by juxtapositioning of two genes. This chimeric transcript is implicated in androgen dependent and independent prostate cancer. Tremendous work is done on the characterization of the mediators involved in the disease refractoriness, yet no study has addressed clinical management of these prostate fusion transcripts impressively. METHODS: An abolished ATM dynamics challenges integrity of DNA. In agreement with this assumption, ATM and DNA-PK were impaired in LNCaP cell line to confirm a tight interaction of these mediators with the expression profile of TMPRSS2-ERG. Abolished ATM enhanced the expression of the fusion transcript. Similarly blunting of DNA-PK downregulated the expression of the fusion transcript giving a notion that DNA-PK is involved in the chromosomal translocation. LNCaP cell lines were analyzed for the effect of EGCG and SB on the expression profile of TMPRSS2-ERG. RESULTS: In this particular unprecedented study, treatment of the LNCaP cell line with EGCG and Silibilin recapitulated ATM expression and activity and downregulated the fusion transcript appearance. CONCLUSIONS: These results underscore the therapeutic effect of EGCG and SB in mitigating the exacerbation of the disease with reference to the fusion transcripts.
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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 | 0.001 |
| 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".