A 4-gene signature from histologically normal surgical margins predicts local recurrence in patients with oral carcinoma: clinical validation
Bibliographic record
Abstract
Abstract Prognostic biomarkers for recurrence of Oral Squamous Cell Carcinoma (OSCC) are urgently needed. We aimed to independently validate a 4-gene expression signature (MMP1, COL4A1, P4HA2, THBS2) predictive of OSCC recurrence risk. Gene expression was measured using Nanostring nCounter® in 245 histologically normal surgical resection margins from 62 patients. Association between risk scores for individual patients and recurrence was assessed by Kaplan-Meier analysis. Signature performance was quantified by concordance index (CI), hazard ratio (HR) and the area under receiver operating characteristics (AUC). Risk scores for recurrence were significantly higher than recurrence-free patients (p = 9.58e-7, Welch’s t-test). A solid performance of the 4-gene signature was determined: CI = 0.64, HR = 3.38 (p = 1.4E-4; log-rank test), AUC = 0.71. We showed that three margins per patient are sufficient to preserve predictive performance (CI = 0.65; HR = 2.92; p = 2.94e-3; AUC = 0.71). Association between the predicted risk scores and recurrence was assessed and showed HR = 2.44 (p = 9.6E-3; log-rank test, N = 62). Signature performance analysis was repeated using an optimized threshold (70th percentile of risks), resulting in HR = 3.38 (p = 1.4E-4; log-rank test, N = 62). The 4-gene signature was validated as predictive of recurrence risk in an independent cohort of patients with resected OSCC and histologically negative margins, and is potentially applicable for clinical decision making on adjuvant treatment and disease monitoring.
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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 | 0.001 | 0.002 |
| 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".