Salivary Biomarkers for the Early Detection and Prognostic Stratification of Oral Squamous Cell Carcinoma

Introduction: Oral squamous cell carcinoma (OSCC) remains a major public health burden, especially in low- and middle-income countries. Traditional diagnostic modalities are often invasive and fail to detect early-stage disease. Saliva, as a non-invasive and readily accessible diagnostic fluid, has gained increasing attention for its potential to reflect molecular changes associated with malignancy. This study explores the diagnostic and prognostic utility of salivary biomarkers—spanning genomic, transcriptomic, proteomic, exosomal, and metabolomic classes—in patients with OSCC. To assess the diagnostic accuracy and predictive value of selected salivary biomarkers for OSCC using logistic regression analysis and stage-wise correlation. Methods: This multicenter case-control study enrolled 400 participants: 200 confirmed OSCC cases, 120 patients with oral potentially malignant disorders (OPMDs), and 80 healthy controls. Unstimulated saliva samples were collected and analyzed using qPCR, ELISA, and mass spectrometry for key biomarkers including EGFR, p53, IL-8, Ki-67, miR-423-5p, miR-1246, CYFRA 21-1, SAT1, NUS1, sialic acid, and exosomal EGFR. Logistic regression analysis was used to evaluate associations between biomarker levels and OSCC risk, while ROC curves assessed diagnostic accuracy (AUC). Results: Logistic regression revealed strong associations between elevated salivary levels of exosomal miR-1246 (OR=2.90, 95% CI: 2.10–3.75), IL-8 (OR=2.10, 95% CI: 1.55–2.80), EGFR (OR=2.45, 95% CI: 1.80–3.34), and miR-423-5p (OR=2.70, 95% CI: 1.95–3.50) with OSCC presence (p < 0.001). Exosomal markers demonstrated >90% diagnostic accuracy (AUC > 0.90), outperforming genomic and proteomic markers. Stage-wise analysis showed increasing expression trends for IL-8, Ki-67, and p53 from stage I to IV. Combined biomarker panels improved diagnostic prediction, with integrated models yielding AUCs > 0.95. Conclusion: Salivary biomarkers—particularly from transcriptomic and exosomal categories—offer high diagnostic and prognostic value for OSCC. Logistic modeling supports their clinical use in non-invasive screening, especially in early-stage disease. This approach can significantly enhance early detection, reduce morbidity, and improve survival outcomes.