Gelsolin Governs the Neuroendocrine Transdifferentiation of Prostate Cancer Cells and Suppresses the Apoptotic Machinery
Bibliographic record
Abstract
BACKGROUND/AIM: Interleukin 6 (IL6) is increased in patients with progressive prostate cancer and induces its transdifferentiation to neuroendocrine prostate cancer. Neuroendocrine prostate cancer has become one of the greatest challenges in treating castration-resistant disease and is linked to poor prognosis. It is necessary to understand better the cellular events associated with IL6-mediated neuroendocrine differentiation to prevent it and identify potential new therapeutic targets. MATERIALS AND METHODS: In the present study, an IL6-inducible neuroendocrine differentiation model established specifically for this purpose was applied using LNCaP cells. Proteomics and western blot analyses were used to identify proteins involved in neuroendocrine differentiation. Subsequently, the role of gelsolin (GSN) in the neuroendocrine differentiation model was characterized (knock-down analyses, microscopic co-localization analyses, apoptosis assay) and GSN expression levels in patient material were investigated. RESULTS: This study revealed that GSN is a crucial factor in the neuroendocrine differentiation process. CONCLUSION: It was shown that siRNA-mediated knock-down of GSN can inhibit neuroendocrine differentiation, making it a valid target for preventing IL6-mediated neuroendocrine differentiation.
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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".