Overexpression of MUC1 and Genomic Alterations in Its Network Associate with Prostate Cancer Progression
Bibliographic record
Abstract
mice and LNCaP xenografts. Elevations in MUC1 expression occur in CRPC. Among nine patients with hormone-naïve bone metastasis, eight express MUC1 in 61% to 100% of PC cells. Utilizing cBioPortal PC genomic data, we organized a training (n=300), testing (n=185), and validation (n=194) cohort. Using the Cox model, a nine-gene signature was derived, including eight genes from a MUC1-related network (APC, CTNNB1/β-catenin, GALNT10, GRB2, LYN, SIGLEC1, SOS1, and ZAP70) and FAM84B. Genomic alterations in these genes reduce disease-free survival (DFS) in the training (P=.00161), testing (P=.00699), entire (training+testing, P=5.557e-5), and a validation cohort (P=3.326e-5). The signature independently predicts PC recurrence [hazard ratio (HR)=1.731; 95% confidence interval (CI): 1.104-2.712; P=.0167] after adjusting for known clinical factors and stratifies patients with high risk of PC recurrence using the median (HR 2.072; 95% CI: 1.245-3.450, P=.0051) and quartile 3 (HR 3.707, 95% CI: 1.949-7.052, P=6.51e-5) scores. Several novel β-catenin mutants are identified in PCs leading to a rapid onset of death and recurrence. Genomic alterations in APC and CTNNB1/β-catenin reduce DFS in two independent PC cohorts (n=485, P=.0369; n=84, P=.0437). The nine-gene signature also associates with reductions in overall survival (P=.0458) and DFS (P=.0163) in melanoma patients (n=367). MUC1 upregulation is associated with CRPC and bone metastasis. A nine-gene signature derived from a MUC1 network predicts PC recurrence.
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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".