The significance of dynamin 2 expression for prostate cancer progression, prognostication, and therapeutic targeting
Bibliographic record
Abstract
Dynamin 2 (Dyn2) is essential for intracellular vesicle formation and trafficking, cytokinesis, and receptor endocytosis. In this study, we investigated the implication of Dyn2 as a prognostic marker and therapeutic target for progressive prostate cancer (PCA). We evaluated Dyn2 protein expression by immunohistochemistry in two cohorts: men with localized PCA treated by retropubic radical prostatectomy (n = 226), and men with advanced/castrate-resistant PCA (CRPC) treated by transurethral resection of prostate (TURP) (n = 253). The role of Dyn2 in cell invasiveness was assessed by in vitro and in vivo experiments using androgen-responsive and refractory PCA preclinical models. Dyn2 expression was significantly increased across advanced stages of PCA compared to benign prostate tissue (P < 0.0001). In the CRPC cohort, high Dyn2 was associated with higher Gleason score (P = 0.004) and marginally with cancer-specific mortality (P = 0.052). In preclinical models, Dyn2 gene silencing significantly reduced cell migration and invasion in vitro, as well as tumor size and lymph node metastases in vivo. In isolated PCA cells, Dyn2 was found to regulate focal adhesion turnover, which is critical for cell migration; this mechanism requires full Dyn2 compared to mutants deficient in GTPase activity. In conclusion, Dyn2 overexpression is associated with neoplastic prostate epithelium and is associated with poor prognosis. Inhibition of Dyn2 prevents cell invasiveness in androgen-responsive and -refractory PCA models, supporting the potential benefit of Dyn2 to serve as a therapeutic target for advanced PCA.
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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".