Integrative network fusion-based multi-omics study for biomarker identification and patient classification of rheumatoid arthritis

Abstract

Abstract Background Cold-dampness Syndrome (RA-Cold) and Hot-dampness Syndrome (RA-Hot) are two distinct groups of rheumatoid arthritis (RA) patients with different clinical symptoms based on traditional Chinese medicine (TCM) theories and clinical empirical knowledge. However, the biological basis of the two syndromes has not been fully elucidated, which may restrict the development of personalized medicine and drug discovery for RA diagnosis and therapy. Methods An integrative strategy combining clinical transcriptomics, phenomics, and metabolomics data based on clinical cohorts and adjuvant-induced arthritis rat models was performed to identify novel candidate biomarkers and to investigate the biological basis of RA-Cold and RA-Hot. Results The main clinical symptoms of RA-Cold patients are joint swelling, pain, and contracture, which may be associated with the dysregulation of T cell-mediated immunity, osteoblast differentiation, and subsequent disorders of steroid biosynthesis and phenylalanine metabolism. In contrast, the main clinical symptoms of RA-Hot patients are fever, irritability, and vertigo, which may be associated with various signals regulating angiogenesis, adrenocorticotropic hormone release, and NLRP3 inflammasome activation, leading to disorders of steroid biosynthesis, nicotinamide, and sphingolipid metabolism. IL17F, 5-HT, and IL4I1 were identified as candidate biomarkers of RA-Cold, while S1P and GLNS were identified as candidate biomarkers of RA-Hot. Conclusions The current study presents the most comprehensive metabonomic and transcriptomic profiling of serum, urine, synovial fluid, and synovial tissue samples obtained from RA-Cold and RA-Hot patients and experimental animal models to date. Through the integration of multi-omics data and clinical independent validation, a list of novel candidate biomarkers of RA-Cold and RA-Hot syndromes were identified, that may be useful in improving RA diagnosis and therapy.

Keywords

• Rheumatoid arthritis
• Patient classification
• Biomarker identification
• Precision medicine
• Integration of the multi-omics data