Assessment of STAT5 as a potential therapy target in enzalutamide-resistant prostate cancer
Bibliographic record
Abstract
Despite enzalutamide's efficacy in delaying the progression of metastatic castration-resistant prostate cancer (CRPC), resistance to this anti-androgen inevitably occurs. Several studies have revealed that the signal transducer and activator of transcription (STAT) 5 plays a role in tumour progression and development of drug resistance such as enzalutamide. Data mining revealed heterogeneous expression of STAT5 in enzalutamide-treated mCRPC patients and enzalutamide-resistant prostate cancer (PCa). Isobologram analysis revealed that the STAT5 inhibitor pimozide combined with enzalutamide has? additive and synergistic inhibitory effects on cell viability in the used models. Functional analysis with siRNA-mediated STAT5 knockdown yielded divergent results. The LNCaP-derived cell line MR49F could be resensitised to enzalutamide by siRNA-mediated STAT5b-knock-down. In contrast, neither STAT5a nor STAT5b knockdown resensitised enzalutamide-resistant LAPC4-EnzaR cells to enzalutamide. In conclusion, our results indicate that STAT5 may be a possible target in a subgroup of enzalutamide-resistant PCa. However, based on the data presented here, a general role of STAT5 in enzalutamide-resistance and its potential as a therapeutic target could not be shown.
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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) | 0.002 | 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".