Uncovering the proteogenomic landscape of head and neck squamous cell carcinoma through urine analysis
Bibliographic record
Abstract
Head and neck squamous cell carcinoma (HNSCC) is a major clinical challenge due to its aggressive nature and poor prognosis in advanced stages. Late detection, often due to delayed diagnosis, limits treatment success. This study investigates non-invasive diagnostic methods to identify early-stage molecular biomarkers using a proteogenomic approach. We analyzed urine samples from 19 male HNSCC patients and identified 1427 proteins by mass spectrometry. Of these, 730 overlapped with healthy controls, highlighting prognostic markers such as RNASE1, LRG1 and CD44. Machine learning techniques, including principal component analysis and partial least squares discriminant analysis, distinguished HNSCC patients from controls and revealed unique proteomic signatures. Pathogenic variants such as GAA p.(Trp746Cys) and SIAE p.(Pro210Leu) were found to be potential indicators of advanced disease. Functional analyzes linked the identified proteins to important tumor-related processes, including epithelial-mesenchymal transition and neutrophil degranulation. These results support urinary proteomics as a promising non-invasive diagnostic tool for early detection of HNSCC and disease monitoring. Future research should validate these biomarkers in larger, more diverse cohorts to improve clinical applicability.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".