Selenium inhibition of survivin expression by preventing Sp1 binding to its promoter
Bibliographic record
Abstract
Survivin, an antiapoptotic protein highly expressed in cancer, regulates multiple cellular network associated with cancer cell viability and drug resistance. Inhibition of survivin expression has been pursued as a valid cancer therapeutic target. In this study, we showed that selenium, an effective chemopreventive agent for many types of cancers, down-regulated survivin expression. Selenium inhibited survivin expression in both mRNA and protein levels in a dose- and time-dependent manner. Using a series of survivin promoter-luciferase constructs, a 37-bp DNA element in the survivin core promoter region that mediates the ability of selenium to inhibit survivin transcription was identified. Gel mobility shift assays and chromatin immunoprecipitation analyses revealed that selenium prevents the binding of Sp1 or Sp1-like proteins to the 37-bp cis-acting DNA element in the survivin promoter. Furthermore, inhibition of survivin expression by small interfering RNA enhanced selenium's inhibitory effects on cell growth, whereas overexpression of survivin in LNCaP human prostate cancer cells desensitized cancer cells to selenium effect, suggesting that the expression of survivin plays an important role in determining the response of cancer cells to selenium. Taken together, these results suggest that selenium down-regulated survivin expression by preventing the binding of Sp1 or Sp1-like proteins to the promoter of survivin, which contributes at least in part to the inhibitory effect of selenium on survivin gene transcription. In addition, down-regulation of survivin expression may account for one of the molecular mechanisms of the anticancer effects of selenium.
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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".