Rheumatoid arthritis (RA) is a chronic, systemic inflammatory disease characterized by joint inflammation, pain and loss of function. In recent years, studies have shown that ferroptosis, a novel mode of cell death, plays an important role in the development of many diseases. Although several studies have been conducted to reveal the relationship between ferroptosis and RA, the specific mechanisms have not been fully understood, especially the role in immune infiltration and joint damage. This study aimed to explore the potential biomarkers associated with ferroptosis and to further analyze the role of immune cell infiltration in synovial tissues of RA patients and its interrelationship with ferroptosis, aiming to provide new ideas and strategies for the early diagnosis and treatment of RA.

This study analyzed integrated RA transcriptomic datasets and ferroptosis-related genes (FRGs) from FerrDb to identify FRDEGs. Weighted gene co-expression network analysis (WGCNA) was first conducted to identify key modules, followed by consensus clustering to stratify patients. Hub ferroptosis genes (Hub RA-FRDEGs) were subsequently screened using machine learning. Their diagnostic value was confirmed via an independent validation cohort and quantitative PCR (qPCR). Potential upstream miRNAs and transcription factors were predicted. The immune microenvironment was assessed using single-sample GSEA (ssGSEA), while biological functions of hub genes were elucidated through single-gene GSEA. Drug prediction and molecular docking were performed to identify potential therapeutics. Finally, cellular localization was verified by single-cell RNA-seq analysis.

Comparative analysis of normal and RA synovial samples identified 24 ferroptosis-related differentially expressed genes (RA-FRDEGs) through integrated WGCNA and differential expression analysis. Consensus clustering based on these genes revealed three distinct molecular subtypes (C1-C3), demonstrating significant patient heterogeneity. Using five machine learning algorithms, we refined four core hub genes (GDF15, SLAMF8, AIM2, NTRK2) whose diagnostic utility (AUC>0.8) was validated in independent cohorts and qRT-PCR. Immune infiltration analysis via ssGSEA showed significant elevation of 11 immune cell types in RA samples, correlating with hub gene expression. We identified four potential therapeutics through drug prediction and molecular docking studies. Finally, single-cell RNA-seq analysis revealed intercellular communication.

This study reveals key genes (GDF15, SLAMF8, AIM2, and NTRK2) associated with ferroptosis in RA, which show significant potential in the diagnosis of RA. These findings not only provide promising biomarkers for the early diagnosis of RA but also create new directions for exploring new therapeutic targets, further emphasizing their importance in clinical applications.