Prostate‐Specific genes and their regulation by dihydrotestosterone
Bibliographic record
Abstract
BACKGROUND: Prostate is a well-known androgen-dependent tissue. METHODS: By sequencing 4,294,186 serial analysis of gene expression (SAGE) tags, we have investigated the transcriptomes of normal mouse prostate, liver, testis, lung, brain, femur, skin, adipose tissue, skeletal muscle, vagina, ovary, mammary gland, and uterus in order to identify the most abundant and tissue-specific transcripts in the prostate, as well as to target the androgen responsive transcripts specifically regulated in the prostate. Small interference RNA (siRNA) in LNCaP cells was applied to validate the roles of prostate-specific/enriched ARGs in the growth of human prostate cancer cells. RESULTS: The most abundant transcripts were involved in prostatic secretion, energy metabolism and immunity. Previously well-known prostate-specific transcripts, including many transcripts involved in prostatic secretion, polyamine biosynthesis and transport, and immunity were specific/enriched in the prostate. Only 22 transcripts among 114 androgen-regulated genes (ARGs) in the mouse prostate were modulated by dihydrotestosterone (DHT) in two or more tissues. The siRNA results showed that inhibition of HSPA5 and MAT2A gene expression repressed growth of human cancer LNCaP cells. CONCLUSIONS: The current study globally assessed the transcriptome of the prostate and revealed the most abundant and tissue-specific transcripts which are responsible for the unique functions of this organ. These prostate-specific ARGs might be used as targets to develop safe and effective gene-based therapy for the prevention and treatment of prostate cancer.
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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".