Prostate Specific Antigen Expression Is Down-Regulated by Selenium through Disruption of Androgen Receptor Signaling
Bibliographic record
Abstract
A previous controlled intervention trial showed that selenium supplementation was effective in reducing the incidence of prostate cancer. Physiological concentrations of selenium have also been reported to inhibit the growth of human prostate cancer cells in vitro. The present study describes the observation that selenium was able to significantly down-regulate the expression of prostate-specific antigen (PSA) transcript and protein within hours in the androgen-responsive LNCaP cells. Decreases in androgen receptor (AR) transcript and protein followed a similar dose and time response pattern upon exposure to selenium. The reduction of AR and PSA expression by selenium occurred well before any significant change in cell number. With the use of a luciferase reporter construct linked to either the PSA promoter or the androgen responsive element, it was found that selenium inhibited the trans-activating activity of AR in cells transfected with the wild-type AR expression vector. Selenium also suppressed the binding of AR to the androgen responsive element site, as evidenced by electrophoretic mobility shift assay of the AR-androgen responsive element complex. In view of the fact that PSA is a well-accepted prognostic indicator of prostate cancer, an important implication of this study is that a selenium intervention strategy aimed at toning down the amplitude of androgen signaling could be helpful in controlling morbidity of this disease.
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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) | 0.001 | 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".