Alternative <scp>RNA</scp> splicing of the <i><scp>GIT</scp>1</i> gene is associated with neuroendocrine prostate cancer
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Bibliographic record
Abstract
Potent androgen receptor pathway inhibition (ARPI) therapies have given rise to a lethal, aggressive subtype of castration-resistant prostate cancer (CRPC) called treatment-induced neuroendocrine prostate cancer (t-NEPC). Now, t-NEPC poses a major clinical problem as approximately 20% of CRPC cases bear this subtype-a rate of occurrence that is predicted to rise with the widespread use of ARPI therapies. Unfortunately, there are no targeted therapies currently available to treat t-NEPC as the origin and molecular underpinnings of t-NEPC development remain unclear. In the present study, we identify that RNA splicing of the G protein-coupled receptor kinase-interacting protein 1 (GIT1) gene is a unique event in t-NEPC patients. Specifically, upregulation of the GIT1-A splice variant and downregulation of the GIT1-C variant expressions are associated with t-NEPC patient tumors, patient-derived xenografts, and cell models. RNA-binding assays show that RNA splicing of GIT1 is directly driven by SRRM4 and is associated with the neuroendocrine phenotype in CRPC cohorts. We show that GIT1-A and GIT1-C regulate differential transcriptomes in prostate cancer cells, where GIT1-A regulates genes associated with morphogenesis, neural function, environmental sensing via cell-adhesion processes, and epigenetic regulation. Consistent with our transcriptomic analyses, we report opposing functions of GIT1-A and GIT1-C in the stability of focal adhesions, whereby GIT1-A promotes focal adhesion stability. In summary, our study is the first to report that alternative RNA splicing of the GIT1 gene is associated with t-NEPC and reprograms its function involving FA-mediated signaling and cell processes, which may contribute to t-NEPC development.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it