ERG/AKR1C3/AR Constitutes a Feed-Forward Loop for AR Signaling in Prostate Cancer Cells
Bibliographic record
Abstract
PURPOSE: Intratumoral androgen synthesis in prostate cancer contributes to the development of castration-resistant prostate cancer (CRPC). Several enzymes responsible for androgen biosynthesis have been shown to be overexpressed in CRPC, thus contributing to CRPC in a castrated environment. The TMPRSS2-ERG transcription factor has been shown to be present in primary prostate cancer tumors as well as CRPC tumors. We hypothesize that TMPRSS2-ERG fusions regulate androgen biosynthetic enzyme (ABE) gene expression and the production of androgens, which contributes to the development of CRPC. EXPERIMENTAL DESIGN: We used a panel of assays, including lentivirus transduction, gene expression, chromatin immunoprecipitation and sequencing, liquid chromatography-mass spectrometric quantitation, immunocytochemistry, immunohistochemistry, and bioinformatics analysis of gene microarray databases, to determine ERG regulation of androgen synthesis. RESULTS: We found that ERG regulated the expression of the ABE AKR1C3 in prostate cancer cells via direct binding to the AKR1C3 gene. Knockdown of ERG resulted in reduced AKR1C3 expression, which caused a reduction in both DHT synthesis and PSA expression in VCaP prostate cancer cells treated with 5α-androstanedione (5α-Adione), a DHT precursor metabolite. Immunohistochemical staining revealed that ERG was coexpressed with AKR1C3 in prostate cancer tissue samples. CONCLUSIONS: These data suggest that AKR1C3 catalyzes the biochemical reduction of 5α-Adione to DHT in prostate cancer cells, and that ERG regulates this step through upregulation of AKR1C3 expression. Elucidation of ERG regulation of ABEs in CRPC may help to stratify TMPRSS2-ERG fusion-positive prostate cancer patients in the clinic for anti-androgen receptor-driven therapies; and AKR1C3 may serve as a valuable therapeutic target in the treatment of CRPC.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".