Single-Cell and Mendelian Randomization Analyses Identify Macrophage Polarization and Efferocytosis Biomarkers in...

Osteoarthritis (OA) is a progressive joint disease influenced by macrophage-associated efferocytosis.

This study aimed to identify key OA biomarkers by integrating transcriptomic and single-cell RNA sequencing (scRNA-seq) data with Mendelian randomization (MR).

We integrated three OA-related transcriptomic datasets (GSE89408, GSE55457, and GSE152805) from synovial tissue with efferocytosis- and macrophage polarization-related genes (ERGs and MPRGs). Differentially expressed genes (DEGs) were identified and analyzed using gene set enrichment (ssGSEA), weighted gene co-expression network analysis (WGCNA), and functional enrichment (GO/KEGG). MR was performed with GWAS data to evaluate causal links between candidate genes and OA. Machine learning methods (LASSO and SVM-RFE), supported by ROC analysis and artificial neural network (ANN) modeling, and were used to screen potential biomarkers. Single-cell RNA-seq analysis was conducted with Seurat, incorporating clustering, functional enrichment, cell-cell communication (CellChat), and pseudotime trajectory analysis (Monocle) to characterize cellular heterogeneity and differentiation pathways in OA.

MR and machine learning highlighted FMO4 and GPR65 as risk biomarkers, validated in independent datasets (AUC > 0.7). GPR65 showed strong associations with neutrophils, regulatory T cells, and antigen-presenting cell (APC) co-inhibition, while both genes were enriched in immune and metabolic pathways. Single-cell analysis identified synovial subintimal fibroblasts and HLA-DRA+ cells as key contributors with distinct differentiation patterns. An ANN model further improved OA classification.

These findings provide new insights into OA pathogenesis and support FMO4 and GPR65 as promising diagnostic and therapeutic targets.