Metabolomic analysis for diagnosis of oral squamous cell carcinoma using machine learning

Oral squamous cell carcinoma (OSCC) undergoes significant metabolic reprogramming. Adopting metabolomics to identify altered metabolic enzymes and metabolites holds promise for the early and precise diagnosis of OSCC. However, most current workflows rely on single-perspective modeling and lack task-aware prioritization. They underextract metabolomic signals, limit classification robustness, and impede a traceable transition from untargeted discovery to targeted panels. Therefore, we propose Metabolomics-based Integrated Information Learning (MIIL), an interpretable framework for OSCC diagnosis and premalignant screening. MIIL prioritizes task-relevant metabolites to derive a compact biomarker panel for targeted assay development, while strengthening classification via decision-level fusion of heterogeneous learners. Initially, we collect 120 oral mucosa tissues from 40 OSCC patients, including 40 Cancer (CA) samples, 40 Margin-1 specimens (M1), and 40 Margin-2 tissues (M2). Moreover, MIIL conducts untargeted metabolomics analysis to identify the most significant differential metabolites contributing to OSCC diagnosis. Subsequently, targeted metabolomics techniques are exploited for in-depth analysis of amino acid metabolites. Finally, integrated learning models are combined via decision-level fusion of linear, probabilistic, and margin-based signals, supporting accurate OSCC classification. The final results demonstrate that this method excels in the CA.vs.M1, CA.vs.M2, and M1.vs.M2 diagnostic tasks, achieving mean accuracies of 88.33%, 91.67%, and 85.00%, respectively. Trial registration: Chinese Clinical Trial Registry (ChiCTR), ChiCTR2200064861; registered on 2023-04-23.