Roles of Alternative RNA Splicing of the Bif-1 Gene by SRRM4 During the Development of Treatment-induced Neuroendocrine Prostate Cancer
Bibliographic record
Abstract
Treatment-induced neuroendocrine prostate cancer (t-NEPC) is an aggressive subtype of prostate cancer (PCa) that becomes more prevalent when hormonal therapy, chemotherapy, or radiation therapy is applied to patients with metastatic prostate adenocarcinoma (AdPC). How AdPC cells survive these anti-cancer therapies and progress into t-NEPC remains unclear. By comparing the whole transcriptomes between AdPC and t-NEPC, we identified Bif-1, an apoptosis-associated gene, which undergoes alternative RNA splicing in t-NEPC. We found that while Bif-1a is the predominant variant of the Bif-1 gene in AdPC, two neural-specific variants, Bif-1b and Bif-1c, are highly expressed in t-NEPC patients, patient derived xenografts, and cell models. The neural-specific RNA splicing factor, SRRM4, promotes Bif-1b and Bif-1c splicing, and the expression of SRRM4 in tumors is strongly associated with Bif-1b/-1c levels. Furthermore, we showed that Bif-1a is pro-apoptotic, while Bif-1b and Bif-1c are anti-apoptotic in PCa cells under camptothecin and UV light irritation treatments. Taken together, our data indicate that SRRM4 regulates alternative RNA splicing of the Bif-1 gene that enables PCa cells resistant to apoptotic stimuli under anti-cancer therapies, and may contribute to AdPC progression into t-NEPC.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".