Decoding Fibroblast Heterogeneity in Osteoarthritis: Identification of a Fibrosis‐Associated Subtype and Novel Diagnostic Biomarkers

<b>Background:</b> Fibroblasts are key contributors to extracellular matrix remodeling and fibrosis, thereby playing a crucial role in the pathogenesis of osteoarthritis (OA). However, their heterogeneity and functional subtypes in OA remain poorly understood. <b>Methods:</b> The single-cell RNA sequencing (scRNA-seq) data of OA and two independent datasets were downloaded from the Gene Expression Omnibus (GEO) database. Subsequently, the Seurat package was utilized to normalize and downscale the scRNA-seq data and classify different cell types. Gene set enrichment analysis (GSEA) was applied to identify significantly enriched biological processes (BPs) specific in each cell cluster. Employing the Monocle2 package, the progression trajectories of OA were analyzed based on the dynamic gene expression changes in the fibroblast subtypes. Finally, single sample GSEA (ssGSEA) and differential expression analysis were combined to screen diagnostic biomarkers for OA, and their diagnostic efficacy was assessed by receiver operating characteristic (ROC) curves and principal component analysis (PCA). <b>Results:</b> Using the scRNA-seq data of OA samples, we identified five different cell types (fibroblasts, endothelial cells [ECs], lymphoid cells, mural cells, and myeloid cells), with fibroblasts accounting for the highest proportion. Then, we found that Fibroblast subtype 3 was notably enriched in fibrosis-related pathways. The pseudotime trajectory analysis showed that genes associated with extracellular matrix and cell adhesion were significantly upregulated during the transformation from healthy status to OA. Additionally, the enrichment score of Fibroblast 3 in the OA tissue was higher than that in healthy tissue, which indicated that fibroblast 3 may promote the development of OA. Finally, four genes (<i>MTUS2</i>, <i>GPR1</i>, <i>GABRA4</i>, and <i>SGCA</i>) with strong diagnostic performance were identified as the biomarkers for OA. <b>Conclusion:</b> The fibroblast subtypes identified by the present research played a critical role in the pathogenesis of OA, and the four biomarkers may serve as new targets for the early diagnosis and treatment of OA.