Genetic ancestry concordant RNA splicing in prostate cancer involves oncogenic genes and associates with recurrence
Bibliographic record
Abstract
Black men suffer disproportionately from prostate cancer (PCa) compared to men of other races and ethnicities. Comparing the molecular landscape of PCa among Black and White patients has the potential to identify targets for development of new precision medicine interventions. Herein, we conducted transcriptomic analysis of prostate tumors and paired tumor-adjacent normals from self-reported Black and White PCa patients and estimated patient genetic ancestry. Clinical follow-up revealed increased biochemical recurrence (BCR) among Black patients compared to White patients with high-grade PCa. Transcriptomic analysis identified differential alternative RNA splicing events (ARSs) between Black and White PCa patients. Genes undergoing genetic ancestry-concordant ARSs in high-grade or low-grade tumors involved cancer promoting genes. Most genes undergoing genetic ancestry-concordant ARSs did not exhibit differential aggregate gene expression or alternative polyadenylation. A number of the genetic ancestry-concordant ARSs associated with BCR; thus, genetic ancestry-concordant RNA splice variants may represent unique targets for PCa precision oncology.
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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 | 0.001 |
| 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".