Protein‐coding and MicroRNA Biomarker Gene Panels Predictive of Clinical Recurrence in Prostate Cancer
Bibliographic record
Abstract
An important challenge in prostate cancer research is to develop effective predictors of tumor recurrence following surgery. To identify predictive biomarkers, we performed DASL expression profiling with a custom‐designed panel of 522 prostate cancer relevant genes on 71 FFPE radical prostatectomy specimens with long‐term outcome. We identified a panel of eight genes that could be used to predict recurrence (p = 0.001). Furthermore, this gene panel could separate patients with and without recurrence in the subset of 46 patients with Gleason 7 tumors (p = 0.022) for whom the decision regarding adjuvant therapy is particularly important and difficult. We also performed comprehensive miRNA profiling of these samples, and identified six miRNAs predictive of recurrence (p=0.001) for all 71 patients and for the subset of 46 Gleason 7 patients (p=0.032). Following promoter analysis of these miRNA genes, we determined that four of them are directly bound by SOX4 in chromatin immunoprecipitations, suggesting that SOX4 may regulate their expression. Among the potential downstream targets of these miRNA genes are several tumor suppressors involved in cell‐cycle arrest and apoptosis. It may therefore now be possible to increase the accuracy of prognostication for individual patients following radical prostatectomy using universally available archived specimens.
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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 | 0.001 | 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".