Identification of Potential Biomarkers for Rheumatoid Arthritis Based on Integrated Bioinformatics and Single-Cell RNA-Seq.

BACKGROUND/

OBJECTIVES

Rheumatoid arthritis (RA) is a chronic autoimmune disease that causes progressive joint damage and systemic complications. Despite multiple treatment options, many patients fail to achieve sustained remission. Our study aimed to integrate bioinformatics and single-cell RNA-seq analyses to identify potential biomarkers and therapeutic targets and explore bioactive compounds from traditional Chinese medicine (TCM).

METHODS

We integrated gene expression quantitative trait loci (eQTL), protein quantitative trait loci (pQTL), and genome-wide association study (GWAS) data for RA using two-sample Mendelian randomization to identify causal druggable genes. Bulk transcriptomics and machine learning were used for candidate gene screening and validation, while single-cell RNA-seq analysis characterized cell type-specific expression and functional relevance. TCM compound screening, molecular docking, and molecular dynamics (MD) simulations were subsequently performed.

RESULTS

CXCL6, IFNG, and SLAMF1 were identified as RA-associated candidate targets with distinct cell type-specific expression patterns, strong immune associations, and favorable diagnostic performance. Functional analyses linked these genes to immune activation and intercellular communication. In silico analyses prioritized sesamin, (+)-Ganoderic acid Mf, and (24R)-saringosterol as candidate compounds, with the IFNG-(+)-Ganoderic acid Mf complex showing stable behavior during MD simulation.

CONCLUSIONS

This integrative framework identified CXCL6, IFNG, and SLAMF1 as candidate biomarkers and druggable targets for RA. Sesamin, (+)-Ganoderic acid Mf, and (24R)-saringosterol warrant further experimental evaluation. These findings provide a basis for future mechanistic and translational studies.