The splicing factor SRSF6 regulates AR activity and represents a potential therapeutic target in prostate cancer
Bibliographic record
Abstract
Abstract Background Prostate cancer (PCa) is the fifth leading cause of cancer-related death worldwide. Finding novel therapeutic strategies to tackle PCa, especially its most advanced phenotype, named castration-resistant PCa (CRPC), is urgently needed. In this sense, although the dysregulation of the splicing process has emerged as a distinctive feature of advanced PCa, the potential role that splicing regulators may play in advanced PCa remains understudied. In this project, we aimed to explore the levels, pathophysiological role, and associated molecular landscape of the splicing factor SRSF6 in PCa.Methods SRSF6 alterations (CNA/mRNA/protein) were analyzed in eight well-characterized cohorts of PCa patients and in the Hi-MYC transgenic model. The effect of SRSF6 overexpression and silencing was tested in vitro (cell proliferation, migration, colony and tumorspheres formation), and in vivo (xenograft tumors). RNA-Seq was performed in PCa cells to analyze gene expression and splicing pattern changes in response to SRSF6 silencing.Results Our results showed that SRSF6 levels (mRNA/protein) were upregulated in PCa vs. non-tumor prostate samples, linked to clinical parameters of tumor aggressiveness (e.g., Gleason score, T-stage, perineural infiltration, metastasis at diagnosis), and associated with poor prognosis (i.e., shorter progression-free survival time) in PCa patients. Moreover, SRSF6 overexpression increased, while its silencing decreased, relevant functional parameters of aggressiveness in vitro and tumor growth in vivo. Mechanistically, SRSF6 modulation resulted in the dysregulation of key oncogenic pathways, especially AR-activity through transcriptional regulation of APPBP2 and TOP2BConclusions SRSF6 could represent a new therapeutic target to inhibit persistent AR-signaling in 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) | 0.002 | 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".