Clusterin expression is significantly enhanced in prostate cancer cells following androgen withdrawal therapy
Bibliographic record
Abstract
INTRODUCTION AND OBJECTIVES: Progression of prostate cancer to androgen independence (AI) results in part from the upregulation of anti-apoptotic genes following androgen withdrawal, and androgen-independent disease remains the primary obstacle to improved survival. Testosterone-repressed prostate message-2 (TRPM-2) encodes the anti-apoptotic protein clusterin, which is upregulated in response to cellular compromise as observed in normal and malignant tissues undergoing apoptosis. Systemic administration of antisense clusterin oligonucleotides in prostate cancer xenograft models delays progression to AI and enhances chemosensitivity. The objective of this study was to define changes in clusterin expression following neoadjuvant hormone therapy (NHT) in prostate cancer patients. MATERIALS AND METHODS: Archival radical prostatectomy (RP) specimens were obtained for 128 patients who received either no NHT or treatment for 2-8 weeks, 3 months, or 8 months. Paired needle biopsy specimens were acquired for 30 patients and all tissues were subjected to clusterin immunohistochemistry. Western blot analysis was performed on frozen tissue from 5 untreated and 5 treated patients. RESULTS: Clusterin expression in malignant prostatic tissue was significantly greater in patients who underwent preoperative NHT (P < 0.001). Needle biopsies obtained prior to NHT consistently demonstrated lower staining intensity than corresponding RP specimens (P < 0.001). Western blot analysis confirmed clusterin levels increased 17-fold beginning within 4 weeks after androgen withdrawal. CONCLUSIONS: Upregulation of clusterin levels following androgen ablation therapy may represent an adaptive cell survival response following apoptotic signals like androgen withdrawal. These findings support clusterin as a valid therapeutic target in strategies employing novel multimodality therapy for advanced 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".