Investigation of androgen receptor-dependent alternative splicing has identified a unique subtype of lethal prostate cancer
Bibliographic record
Abstract
A complete proteomics study characterizing active androgen receptor (AR) complexes in prostate cancer (PCa) cells identified a diversity of protein interactors with tumorigenic annotations, including known RNA splicing factors. Thus, we chose to further investigate the functional role of AR-mediated alternative RNA splicing in PCa disease progression. We selected two AR-interacting RNA splicing factors, Src associated in mitosis of 68 kDa (SAM68) and DEAD (Asp-Glu-Ala-Asp) box helicase 5 (DDX5) to examine their associative roles in AR-dependent alternative RNA splicing. To assess the true physiological role of AR in alternative RNA splicing, we assessed splicing profiles of LNCaP PCa cells using exon microarrays and correlated the results to PCa clinical datasets. As a result, we were able to highlight alternative splicing events of clinical significance. Initial use of exon-mini gene cassettes illustrated hormone-dependent AR-mediated exon-inclusion splicing events with SAM68 or exon-exclusion splicing events with DDX5 overexpression. The physiological significance in PCa was investigated through the application of clinical exon array analysis, where we identified exon-gene sets that were able to delineate aggressive disease progression profiles and predict patient disease-free outcomes independently of pathological clinical criteria. Using a clinical dataset with patients categorized as prostate cancer-specific death (PCSD), these exon gene sets further identified a select group of patients with extremely poor disease-free outcomes. Overall, these results strongly suggest a nonclassical role of AR in mediating robust alternative RNA splicing in PCa. Moreover, AR-mediated alternative spicing contributes to aggressive PCa progression, where we identified a new subtype of lethal PCa defined by AR-dependent alternative splicing.
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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 | 0.001 | 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".