Construction of a set of novel and robust gene expression signatures predicting prostate cancer recurrence
Bibliographic record
Abstract
We report here numerous novel genes and multiple new signatures which robustly predict prostate cancer (PC) recurrence. We extracted 696 differentially expressed genes relative to a reported PC signature from the TCGA dataset (n = 492) and built a 15-gene signature (SigMuc1NW) using Elastic-net with 10-fold cross-validation through analyzing their expressions at 1.5 standard deviation/SD below and 2 SD above a population mean. SigMuc1NW predicts biochemical recurrence (BCR) following surgery with 56.4% sensitivity, 72.6% specificity, and 63.24 median months disease free (MMDF) (P = 1.12e-12). The prediction accuracy is improved with the use of SigMuc1NW's cutpoint (P = 3e-15) and is further enhanced (sensitivity 67%, specificity 75.7%, MMDF 45.2, P = 0) when all 15 genes were analyzed through their cutpoints instead of their SDs. These genes individually associate with BCR using either SD or cutpoint as the cutoff points. Eight of 15 genes are individual risk factors after adjusting for age at diagnosis, Gleason score, surgical margin, and tumor stage. Eleven of 15 genes are novel to PC. SigMuc1NW discriminates BCR with time-dependent AUC (tAUC) values of 76.6% at 11.5 months (76.6%-11.5 m), 73.8%-22.3 m, 78.5%-32.1 m, and 76.4%-48.4 m. SigMuc1NW is correlated with adverse features of PC, high Gleason scores (odds ratio/OR 1.48, P < 2e-16), and advanced tumor stages (OR 1.33, P = 4.37e-13). SigMuc1NW remains an independent risk factor of BCR (HR 2.44, 95% CI 1.53-3.87, P = 1.62e-4) after adjusting for age at diagnosis, Gleason score, surgical margin, and tumor stage. In an independent PC (MSKCC) cohort (n = 140), these 15 genes were altered in PC vs normal tissue, metastatic PCs vs primary PCs, and recurrent PCs vs nonrecurrent PCs. Importantly, a 10-gene subsignature SigMuc1NW1 predicts BCR in MSKCC (P = 3.11e-15) and TCGA (P = 3.13e-12); SigMuc1NW1 discriminates BCR at 18.4 m with tAUC as 82.5%. Collectively, our analyses support SigMuc1NW as a novel and robust signature in predicting BCR of PC.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".