Identification of Novel Salivary Metabolite Biomarkers for Oral Squamous Cell Carcinoma Using Untargeted UHPLC-MS/MS Metabolomic Profiling.

Authors

Asha Selvaraj, Alycia Devasagayam, Stalin Selvaraj

Published in

Molecular omics. Oct 19, 2026. Epub Oct 19, 2026.

Abstract

This study aimed to identify candidate salivary metabolites for differentiating oral squamous cell carcinoma (OSCC) patients from healthy controls. Untargeted metabolomics was performed using an Orbitrap UHPLC-MS/MS system on saliva samples from nine OSCC patients and nine healthy controls following ethical approval. A total of 1,105 metabolite features, including those detected in quality control samples, were identified. Sequential filtering to exclude non-endogenous or non-human-associated compounds retained 291 endogenous metabolites. Following further preprocessing, 75 metabolite features were subjected to downstream statistical analysis using MetaboAnalyst 6.0. Of these, 34 metabolites exhibited differential abundance based on a nominal p value < 0.05 and a fold-change threshold of >1.2, comprising 21 upregulated and 13 downregulated metabolites. The ten most significantly upregulated metabolites were prioritized for further evaluation. L-iditol, lignoceric acid, D-glucose 1-phosphate, ManGlcNAc2, and Man3GlcNAc2 emerged as candidate discriminatory features. Receiver operating characteristic (ROC) analysis demonstrated promising discriminatory performance, with a three-metabolite panel comprising Man3GlcNAc2, lignoceric acid, and ManGlcNAc2 achieving an area under the curve (AUC) of 1.0 within this discovery cohort. Nevertheless, this apparently perfect discrimination should be interpreted cautiously because of the small sample size, potential overfitting, and exploratory nature of the analysis. The identified metabolites represent preliminary candidate biomarkers rather than clinically validated diagnostic markers. Larger independent cohorts, appropriate disease-control groups, and targeted analytical validation are essential to establish their reproducibility, disease specificity, and potential clinical utility for OSCC detection and monitoring.

PMID:
42895963
Bibliographic data and abstract were imported from PubMed on 20 Oct 2026.

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