Development of Neuroendocrine Prostate Cancers by the Ser/Arg Repetitive Matrix 4-Mediated RNA Splicing Network
Bibliographic record
Abstract
While the use of next generation androgen receptor pathway inhibition (ARPI) therapy has significantly increased the survival of patients with metastatic prostate adenocarcinoma (AdPC), several groups have reported a treatment-resistant mechanism, whereby cancer cells can become androgen receptor indifferent and gain a neuroendocrine (NE)-like phenotype. This subtype of castration-resistant prostate cancer has been termed "treatment-induced castration-resistant neuroendocrine prostate cancer" (CRPC-NE). Recent reports indicate that the overall genomic landscapes of castration-resistant tumors with AdPC phenotypes and CRPC-NE are not significantly altered. However, CRPC-NE tumors have been found to contain a NE-specific pattern throughout their epigenome and splicing transcriptome, which are significantly modified. The molecular mechanisms by which CRPC-NE develops remain unclear, but several factors have been implicated in the progression of the disease. Recently, SRRM4, a neuronal-specific RNA splicing factor that is upregulated in CRPC-NE tumors, has been shown to establish a CRPC-NE-unique splicing transcriptome, to induce a NE-like morphology in AdPC cells, and, most importantly, to transform AdPC cells into CRPC-NE xenografts under ARPI. Moreover, the SRRM4-targeted splicing genes are highly enriched in various neuronal processes, suggesting their roles in facilitating a CRPC-NE program. This article will address the importance of SRRM4-mediated alternative RNA splicing in reprogramming translated proteins to facilitate NE differentiation, survival, and proliferation of cells to establish CRPC-NE tumors. In addition, we will discuss the potential roles of SRRM4 in conjunction with other known pathways and factors important for CRPC-NE development, such as the androgen receptor pathway, TP53 and RB1 genes, the FOXA family of proteins, and environmental factors. This work aims to explore the multifaceted functions of SRRM4 and SRRM4-mediated splicing in driving a CRPC-NE program as a coping mechanism for therapy resistance, as well as define future SRRM4-targeted therapeutic approaches for treating CRPC-NE or mitigating its development.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".