SRRM4 gene expression correlates with neuroendocrine prostate cancer
Bibliographic record
Abstract
Neuroendocrine prostate cancer (NEPC) is an aggressive subtype of castrate-resistant prostate cancer characterized by poor patient outcome. Whole transcriptome sequencing analyses identified a NEPC-specific RNA splicing program that is predominantly controlled by the SRRM4 gene, suggesting that SRRM4 drives NEPC development. However, whether SRRM4 expression in patients may aid pathologists in diagnosing NEPC and predicting patient survival remains to be determined. In this study, we have applied RNA in situ hybridization and immunohistochemistry assays to measure the expressions of SRRM4, NEPC markers (SYP, CD56, and CHGA), and adenocarcinoma (AdPC) markers (AR, PSA) in a series of tissue microarrays constructed from castrate-resistant prostate tumors, treatment-naïve tumors collected from radical prostatectomy, and tumors treated with neoadjuvant hormonal therapy (NHT) for 0-12 months. Three pathologists also independently evaluated tumor histology and NEPC marker status. Here, we report that SRRM4 in castrate-resistant tumors is highly expressed in NEPC, strongly correlated with SYP, CD56, and CHGA expressions (Pearson correlation r = 0.883, 0.675, and 0.881; P < 0.0001) and negatively correlated with AR and PSA expressions (Pearson correlation r = -0.544 and -0.310; P < 0.05). Overall survival is 12.3 months for patients with SRRM4 positive tumors, comparing to 23 months for patients with SRRM4 negative tumors. In treatment-naïve AdPC, low SRRM4 expression is detected in ∼16% tumor cores. It correlates with SYP and CHGA expressions, but not Gleason scores. AdPC treated with >7 month NHT has significantly higher SRRM4 expression. Based on these findings, we conclude that SRRM4 expression in castrate-resistant tumors is highly correlated with NEPC and poor patient survival. It may serve as a diagnosis and prognosis biomarker of NEPC.
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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 | 0.000 |
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".