In Silico Prediction of Salivary Biomarkers Associated with Nicotine Metabolism

Abstract

Background: Nicotine metabolism varies significantly among individuals and influences addiction risk, treatment response, and disease susceptibility. Current biomarkers for nicotine exposure rely primarily on blood or urine samples, limiting their utility in large-scale or point-of-care applications. Saliva offers a non-invasive alternative, but protein-based salivary biomarkers for nicotine metabolism remain underexplored.

Objective: To identify candidate salivary biomarkers associated with nicotine metabolism using a stepwise in silico bioinformatics approach integrating gene function, secretion potential, salivary expression, protein interaction networks, and miRNA regulation.

Methods: Genes involved in nicotine metabolism were curated from public databases (KEGG, CTD, GeneCards). Enrichment analysis was performed to identify relevant biological processes and pathways. Candidate proteins were filtered based on secretion potential (SignalP, SecretomeP), salivary expression (SalivaDB, Human Protein Atlas), and protein-protein interaction centrality (STRING, Cytoscape). miRNA regulators were predicted using TargetScan and miRTarBase.

Results: Three high-confidence candidate proteins (GSTP1, FMO3, and UGT2B10) were identified as biologically relevant, secreted, and experimentally detected in saliva. Protein-protein interaction analysis highlighted GSTP1 and UGT2B10 as central nodes. Several regulatory miRNAs, including hsa-miR-155 and hsa-miR-27b, were predicted or validated to target these proteins, adding a regulatory dimension to biomarker selection.

Conclusion: This study proposes a set of salivary proteins and miRNAs as potential non-invasive biomarkers for nicotine metabolism. The findings provide a strong basis for experimental validation and highlight the utility of bioinformatics in accelerating biomarker discovery for personalized and public health applications