LEF1 in Androgen-Independent Prostate Cancer: Regulation of Androgen Receptor Expression, Prostate Cancer Growth, and Invasion
Bibliographic record
Abstract
A major obstacle in treating prostate cancer is the development of androgen-independent disease. In this study, we examined LEF1 expression in androgen-independent cancer as well as its regulation of androgen receptor (AR) expression, prostate cancer growth, and invasion in androgen-independent prostate cancer cells. Affymetrix microarray analysis of LNCaP and LNCaP-AI (androgen-independent variant LNCaP) cells revealed 100-fold increases in LEF1 expression in LNCaP-AI cells. We showed that LEF1 overexpression in LNCaP cells resulted in increased AR expression and consequently enhanced growth and invasion ability, whereas LEF1 knockdown in LNCaP-AI cells decreased AR expression and, subsequently, growth and invasion capacity. Chromatin immunoprecipitation, gel shift, and luciferase assays confirmed LEF1 occupancy and regulation of the AR promoter. Thus, we identified LEF1 as a potential marker for androgen-independent disease and as a key regulator of AR expression and prostate cancer growth and invasion. LEF1 is highly expressed in androgen-independent prostate cancer, potentially serving as a marker for androgen-independent 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".