Platform-Agnostic Quantification for Molecular Interception of Oral and Esophageal Malignancies: A Global Study Addressing Diagnostic Divide

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ID: 331572
2026
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Abstract
BACKGROUND: Oral and esophageal squamous cell carcinomas (OSCC/ESCC) are commonly diagnosed late since histopathology is subjective and robust molecular tools are lacking. Leveraging interpretable machine learning, we aimed to develop an objective and platform-agnostic molecular measure of malignancy that overcomes the technical and geographic barriers of conventional genomic testing. METHODS: Using a discovery-to-generalization framework with 2,541 samples, we developed and validated a single-sample transcriptomic scoring system, EsoralPrint. We feature engineered 34 intra-sample ordinal gene pairs (isoPairs) that capture consistent expression-order reversals between malignant and healthy states. It was trained on oral tissues and externally validated across diverse global cohorts spanning the upper aerodigestive tract, technical platforms, and geographic regions. Independent single-cell RNA sequencing was used to provide post-hoc mechanistic explainability. RESULTS: In the discovery cohort, EsoralPrint distinguished patients histologically graded as low-risk hyperplasia who nonetheless progressed to cancer (log-rank p < 0.0001); we treat this result as exploratory pending independent confirmation. In external validations, EsoralPrint reached 95.0% accuracy and 96.7% F1-score across OSCC cohorts (460 OSCC, 162 non-OSCC) and 91.6% accuracy and 91.6% F1-score in esophageal tissue unseen during discovery and training (574 ESCC, 614 healthy mucosa). Further, a malignancy gradient reflecting field cancerization effects in resection boundaries was observed, and a transition zone (scores 0.25-0.75) was defined where molecular alterations preceded morphological changes. CONCLUSION: EsoralPrint provides a platform-independent and quantitative complement to histological grading in oral and esophageal squamous malignancy, with the potential to give clinicians lead time for intervention. It can be accessed via https://github.com/aiphaqua/EsoralPrint.
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openalex_W7220706398 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Hung-Ming Lai, Hua-Ping Liu, Kaia Douglas, Keith Johnson, Xuanhua P Xie
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Journal jnci cancer spectrum
Year 2026
DOI
10.1093/jncics/pkag102
URL
https://doi.org/10.1093/jncics/pkag102
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