A novel mechanism of SRRM4 in promoting neuroendocrine prostate cancer development via a pluripotency gene network
Bibliographic record
Abstract
BACKGROUND: Prostate adenocarcinoma (AdPC) cells can undergo lineage switching to neuroendocrine cells and develop into therapy-resistant neuroendocrine prostate cancer (NEPC). While genomic/epigenetic alterations are shown to induce neuroendocrine differentiation via an intermediate stem-like state, RNA splicing factor SRRM4 can transform AdPC cells into NEPC xenografts through a direct neuroendocrine transdifferentiation mechanism. Whether SRRM4 can also regulate a stem-cell gene network for NEPC development remains unclear. METHODS: Multiple AdPC cell models were transduced by lentiviral vectors encoding SRRM4. SRRM4-mediated RNA splicing and neuroendocrine differentiation of cells and xenografts were determined by qPCR, immunoblotting, and immunohistochemistry. Cell morphology, proliferation, and colony formation rates were also studied. SRRM4 transcriptome in the DU145 cell model was profiled by AmpliSeq and analyzed by gene enrichment studies. FINDINGS: SRRM4 induces an overall NEPC-specific RNA splicing program in multiple cell models but creates heterogeneous transcriptomes. SRRM4-transduced DU145 cells present the most dramatic neuronal morphological changes, accelerated cell proliferation, and enhanced resistance to apoptosis. The derived xenografts show classic phenotypes similar to clinical NEPC. Whole transcriptome analyses further reveal that SRRM4 induces a pluripotency gene network consisting of the stem-cell differentiation gene, SOX2. While SRRM4 overexpression enhances SOX2 expression in both time- and dose-dependent manners in DU145 cells, RNA depletion of SOX2 compromises SRRM4-mediated stimulation of pluripotency genes. More importantly, this SRRM4-SOX2 axis is present in a subset of NEPC patient cohorts, patient-derived xenografts, and clinically relevant transgenic mouse models. INTERPRETATION: We report a novel mechanism by which SRRM4 drives NEPC progression via a pluripotency gene network. FUND: Canadian Institutes of Health Research, National Nature Science Foundation of China, and China Scholar Council.
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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) | 0.001 | 0.001 |
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".