Sjögren's syndrome (SS) is a chronic autoimmune disease characterized by lymphocytic infiltration of exocrine glands and B-cell hyperreactivity. Recent findings suggest a potential link to abnormal lactate metabolism. This study aimed to identify lactate metabolism-related genes (LMRGs) associated with SS and to explore their roles in the immune-related molecular mechanisms of the disease.

Gene expression profiles of PBMC samples from patients with SS were obtained from the Gene Expression Omnibus (GEO) database. Common lactate metabolism-related differentially expressed genes (LMRDEGs) were identified by intersecting SS-related DEGs with a lactate metabolism-related gene set, and feature genes were further selected using a support vector machine (SVM) model. Functional enrichment analysis, immune infiltration analysis, and protein–protein interaction (PPI) network analysis were subsequently performed.

A total of 280 upregulated genes were identified in the SS dataset, and 73 common lactate metabolism-related differentially expressed genes (LMRDEGs) were obtained by intersecting these genes with the lactate metabolism-related gene set. Forty-three characteristic DE-LMRGs were further selected using a support vector machine. GSEA and KEGG analyses showed that these genes were significantly enriched in the NOD -like receptor signaling, IL-18 signaling, and Toll-like receptor signaling pathways. The key SS-related LMRDEGs were identified by integrating seven algorithms in the PPI network analysis. Immune cell infiltration analysis indicated that the proportions of neutrophils, macrophages, and dendritic cells were significantly increased and positively correlated with the expression of STAT1, IL1B, CXCL8, and NLRP3. STAT1 may be involved in dendritic cell regulation, whereas IL1B, CXCL8, and NLRP3 may be associated with macrophage function. In the GSE43874 and GSE127952 datasets, the AUC values for STAT1, IL1B, CXCL8, and NLRP3 all exceeded 0.9.

In conclusion, this study identified lactate metabolism-related biomarkers in SS using bioinformatics and machine learning and further explored their associations with immune cell infiltration. These findings provide new insights into potential diagnostic biomarkers and therapeutic targets for SS.