Bioinformatics-Based Identification of Key Fibroblast Module Biomarkers and Potential Drug Candidates in Osteoarthritis

Diagnostic performance was evaluated using ROC analysis and validated in an external cohort. Functional and structural relevance was further explored through single-gene GSEA, virtual knockout analysis, and molecular docking.Results Seven synovial cell types were identified by single-cell analysis, with fibroblasts exhibiting enhanced intercellular communication in OA. HdWGCNA identified two OA-associated fibroblast modules, yielding 39 candidate genes mainly related to extracellular matrix organization.

Five core genes were ultimately identified, among which SSR4 and PRDX4 were significantly upregulated in OA and demonstrated strong diagnostic performance in both training and validation cohorts (AUC > 0.90). Functional analyses suggested their involvement in energy metabolism, extracellular matrix remodeling, and inflammatory signaling pathways. Molecular docking indicated that several small molecules, including vitamin E, L-methionine, and streptozotocin, showed stable binding conformations with SSR4 and PRDX4, with vitamin E exhibiting relatively favorable binding potential.Conclusion This study systematically characterizes fibroblast-driven molecular signatures in OA and identifies SSR4 and PRDX4 as potential diagnostic biomarkers and candidate intervention targets, providing a foundation for future mechanistic and drug discovery studies.