Comprehensive analysis of metabolic and molecular alterations in the blood of patients with Sjögren's syndrome based on untargeted metabolomics analysis.

BACKGROUND

Sjögren's syndrome (SS) is a chronic autoimmune disorder marked by lymphocytic infiltration of exocrine glands, leading to xerostomia, keratoconjunctivitis sicca, and systemic involvement including fatigue, arthralgia, and visceral organ impairment. Its pathogenesis reflects a multifactorial interaction of genetic, environmental, and hormonal influences that collectively disrupt immune homeostasis and drive tissue injury. Extensive investigations remain essential to clarify molecular pathways and establish reliable biomarkers that can enable early detection and precision therapy in SS. Detailed characterization of metabolic and molecular disturbances associated with SS is indispensable for advancing both pathophysiological insight and clinical management.

MATERIAL AND

METHODS

Rigorous quality control, batch adjustment, and data normalization were applied to untargeted metabolomics to ensure consistency and analytical reliability. Serum metabolite profiling in SS was assessed through unsupervised principal component analysis (PCA) to distinguish intergroup variations. Quantitative evaluation of metabolite abundance, machine learning-based metabolite selection, and functional enrichment analyses using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were employed to identify candidate biomarkers. Differential gene expression and enrichment analyses, construction of protein-protein interaction (PPI) networks in the SS group, and orthogonal partial least squares discriminant analysis (OPLS-DA) were subsequently performed to investigate underlying molecular mechanisms. Correlations among metabolites, key genes, and immune cell subsets were also examined.

RESULTS

Quality control confirmed the reliability and precision of the untargeted metabolomics data. Differential metabolite analysis highlighted significant alterations, with PC O-36:5, 4-Aminobutyric acid, and PC 16:0_16:1 exhibiting the most marked changes. Machine learning algorithms further identified metabolites including PC O-36:5, Prostaglandin B1, and L-Ergothioneine as candidates with diagnostic potential for SS. Functional enrichment revealed altered KEGG pathways including arginine and proline metabolism, pyrimidine metabolism, and nucleotide metabolism. Sequencing data analysis indicated enriched GO terms related to viral response, KEGG pathways such as Influenza A, and Gene Set Enrichment Analysis (GSEA) pathways including HUNTINGTONS_DISEASE. Two-way orthogonal partial least squares (O2PLS) delineated metabolites central to metabolic networks, such as PC O-36:5, along with genes critical to gene interaction networks, including GZMA. Correlation analysis demonstrated tight associations between metabolites, genes, and immune cell subsets in SS.

CONCLUSION

This integrative analysis identified molecular markers with diagnostic relevance for SS and advanced the understanding of metabolic and molecular alterations underlying the disease.