Multi-Omics Integration Identifies FGF1 as a Diagnostic Biomarker and RAS-MAPK-Driven Pathogenic Factor in Osteoarthritis.

Background: Osteoarthritis (OA) is a chronic inflammatory disease characterized by cartilage damage, but its pathogenesis remains unclear. Objective: This study aims to identify potential therapeutic targets for OA and explore associated mechanistic pathways. Methods: OA-related data from GWAS, eQTLGen, and GEO databases were analyzed. Differential expression analysis, WGCNA, and PPI network analysis identified OA-associated genes. Machine learning algorithms determined diagnostic markers, validated by ROC curve analysis. Mendelian randomization assessed causal relationships, and single-cell sequencing explored gene dynamics in OA cartilage. In vitro and in vivo experiments validated the findings. Results: We identified 282 differentially expressed genes (DEGs) associated with OA, with 52 hub genes, including FGF1, as a key candidate. Machine learning identified FGF1 as a diagnostic biomarker, validated by ROC curve analysis (AUCs up to 1.000 in the training set, and 0.790 and 0.761 in validation sets). Mendelian randomization suggested a potential causal relationship between FGF1 expression and OA risk (95% CI = 1.002-1.081, p = 0.041). Single-cell sequencing explored the dynamics of diagnostic marker genes in OA cartilage progression. In vitro and in vivo experiments confirmed FGF1's role in inflammation and matrix degradation via the RAS-MAPK pathway. Conclusion: This study confirms FGF1 as a diagnostic biomarker for OA, with a key role in pathogenesis through RAS-MAPK pathway activation. MR analysis provides suggestive causal evidence. FGF1 induces a pro-inflammatory and catabolic state in chondrocytes by upregulating MMP13 and TNFα, while inhibiting Aggrecan synthesis, driving irreversible cartilage matrix destruction. These findings support targeting FGF1 as a novel therapeutic strategy for OA.