Molecular subtype and RNA transcriptomics validation for rheumatoid arthritis characterized by fatty acid metabolism-related immune landscape

Background: Rheumatoid arthritis (RA) is a rheumatic disease charactered by severe bone destruction. Evidence suggests that fatty acid metabolism (FAM)-related proteins can regulate inflammation of synoviocytes in RA. However, the fundamental roles of FAM regulators in RA remain to be elucidated. Methods: We selected the GSE93272 dataset sourced from the Gene Expression Omnibus (GEO) for the classification of FAM-associated molecular subtypes and immune microenvironments in RA. Subsequently, bone marrow-derived macrophages (BMMs) with or without receptor activator of nuclear factor kappa-B ligand (RANKL) intervention were harvested for RNA sequencing (RNA-seq) to verify FAM hub gene expressions. Results: Difference analysis between RA samples and controls screened 53 significant FAM regulators. Random forest algorithm for RA risk prediction was utilized to identify ten diagnostic FAM regulators (hub genes). A nomogram incorporating hub genes was developed, and decision curve analysis suggested its potential utility in clinical practice. Additionally, consensus clustering analysis of these hub genes categorized RA patients to different FAM clusters (cluster A and cluster B). To quantify FAM clusters, principal component analysis (PCA) was adopted to count FAM score of every sample. ClusterB may be more linked with osteoclastogenesis in RA characterized by RXRA, IL17RA, and TBXA2R. Additionally, cases in cluster A were associated with the immunity of activated CD4 T cell, activated CD8 T cell, eosinophil, Gamma delta T cell, immature dendritic cell, MDSC, macrophage, regulatory T cell, and Type 2 T helper cell, while cluster B was linked to CD56dim natural killer cell, Natural killer T cell, T follicular helper cell, Type 1 T helper cell immunity, which has a higher FAM score. Remarkably, RNA-seq analysis confirmed the expression trend of SREBF1, FASN, CD36, SCD1 and SCD2, consistent with bioinformatics predictions. Conclusions: This scoring system of FAM subtypes provided promising markers and immunotherapeutic strategies for future RA treatment.