Multi-omics identification of immune-related biomarkers predicting tofacitinib response in rheumatoid arthritis.

BACKGROUND

Rheumatoid arthritis (RA) is a prototypical autoimmune disease characterized by chronic inflammation and immune dysregulation. Although Janus kinase (JAK) inhibitors such as tofacitinib have expanded therapeutic options, treatment responses remain heterogeneous and reliable predictors of efficacy are lacking.

METHODS

Peripheral blood mononuclear cells (PBMCs) and serum samples were collected from 14 patients with active RA before initiation of tofacitinib treatment. Patients were classified as responders or non-responders according to EULAR DAS28 criteria after treatment. An integrative multi-omics approach was applied, including RNA sequencing, miRNA sequencing, proteomics, and untargeted metabolomics. Comprehensive bioinformatics analyses were performed to identify potential candidate predictors of tofacitinib response. Key findings were further assessed through internal validation in an independent cohort of tofacitinib-treated RA patients and external validation using publicly available datasets.

RESULTS

Multi-omics analyses revealed upregulation of ribosomal proteins in PBMCs of responders, with RPL21 emerging as a potential immune-related candidate. Consistently, hsa-miR-197-3p and hsa-miR-625-3p were downregulated in responders, suggesting possible regulatory roles in treatment efficacy. Proteomic profiling showed decreased serum apolipoproteins, particularly APOA1, while metabolomic analysis identified elevated choline, malate, and nervonic acid, reflecting immune-metabolic reprogramming. Integration of multi-omics data highlighted convergent immune pathways and identified exploratory candidate biomarkers associated with tofacitinib response.

CONCLUSIONS

This study provides exploratory integrative multi-omics evidence linking immune-related transcriptomic, proteomic, and metabolic alterations to heterogeneous therapeutic responses in RA. The identified signatures improve our understanding of molecular pathways underlying JAK inhibition response and offer potential candidate biomarkers to guide personalized treatment strategies.