Identification of RNA binding proteins associated with differential splicing in neuroendocrine prostate cancer
Bibliographic record
Abstract
Alternative splicing is a tightly regulated process that can be disrupted in cancer. Established cancer genes express splice isoforms with distinct properties and their differential expression is associated with tumour progression. Although prostate adenocarcinoma (PCa) is effectively managed at early stage by therapies targeting the androgen receptor signaling axis, up to 30% of late stage prostate cancers progress to a treatment-resistant form of the disease called neuroendocrine prostate cancer (NEPC), for which there are few therapeutic options. It is histologically distinct from PCa, expresses a neuronal gene signature and is associated with poor survival (<1 year). We hypothesize that alternative splicing has an important role in driving transformation of PCa tumours towards the NEPC phenotype and we seek to identify regulators of aberrant alternative splicing. We integrated a number of bioinformatics tools to investigate alternative splicing in NEPC. Analyzing RNA-Seq data from a patient-derived xenograft model of neuroendocrine transdifferentiation, we compared splicing profiles between NEPC and PCa and identified a set of differentially spliced cassette exons. We found these cassette exons to code for protein segments containing DNA-binding domains, protein-binding regions and posttranslational modification sites. We discovered evolutionarily conserved motifs around intronic regions of the cassette exons and implicated them with RNA recognition motifs of tissue-specific RNA binding proteins. We corroborated our findings by analyzing RNA-Seq data from a patient-tumour cohort and found recurrent RNA binding proteins associated with cassette exon inclusion. Our integrated analysis suggests that splicing changes between PCa and NEPC are mediated by tissue-specific RNA binding proteins, which may be of therapeutic or diagnostic value.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".