Background: Rheumatoid arthritis (RA) transitions from early (EA-RA) to established (ES-RA) disease, a process driven by poorly understood molecular mechanisms hindering personalized treatment. Understanding these molecular differences is crucial for improved therapeutic strategies and patient outcomes.

Objectives: This study aimed to delineate the molecular landscapes of EA-RA and ES-RA using integrative transcriptomic and proteomic analyses, identifying distinct and shared molecular profiles, exploring associated pathways, investigating correlations with clinical features, and identifying potential biomarkers as predictors for treatment response to guide personalized therapies.

Methods: Peripheral blood mononuclear cells (PBMCs) from EA-RA (n=53), ES-RA (n=119; 75 initiating bDMARDs, 37 tsDMARDs), and healthy donors (HD, n=42) underwent RNAseq. Serum inflammatory mediators (92 proteins) were analyzed using proximity extension assay (PEA, Olink/Cobiomics). EA-RA was defined as a disease duration of ≤1 year, and ES-RA as 5 to 20 years. Clinical outcomes were assessed after 6 months of therapy (EULAR criteria). Differential gene expression analysis (DGEA), weighted gene co-expression network analysis (WGCNA), and functional enrichment analyses were performed. Correlations between molecular profiles and clinical outcomes were investigated.

Results: RNAseq revealed distinct transcriptomic profiles among HD, EA-RA and ES-RA. EA-RA showed 1241 differentially expressed genes (DEGs) compared to HD, primarily related to innate immunity, while ES-RA exhibited 3712 DEGs, enriched in adaptive immunity and broader pathways including metabolic and regulatory processes. Notably, 1182 DEGs were common between both groups. To elucidate the broader molecular mechanisms and regulatory networks underlying RA progression, we employed Weighted Gene Co-expression Network Analysis (WGCNA) on the RNA sequencing data, identifying 32 functionally enriched gene modules. ES-RA exhibited a significantly greater number of dysregulated modules compared to EA-RA, encompassing most of the modules already disrupted in EA-RA. Modules shared between EA-RA and ES-RA were primarily associated with immuno-inflammatory responses and fundamental cellular regulatory processes. However, ES-RA uniquely displayed dysregulation in additional modules implicated in adaptive immune responses, including those related to stress responses, cell cycle control, metabolic pathways, and T and B cell activation. In EA-RA, the identified gene modules showed limited correlations with disease activity and clinical parameters, primarily involving modules associated with innate immune responses and their regulation. Conversely, ES-RA demonstrated a substantially larger number of significant correlations between dysregulated gene modules and various clinical features, including disease activity scores, cardiovascular risk factors, and autoimmune profile. These findings underscore the significantly more complex and intricate network of transcriptomic alterations characterizing established RA and their potential contribution to the observed clinical heterogeneity and disease progression. Recognizing the importance of inflammatory disturbances in this disease, we expanded our investigation to include the inflammatory proteomic profile. PEA identified more altered proteins in ES-RA (37 vs HD, predominantly linked to cytotoxic pathways) than in EA-RA (32 vs HD, mainly associated with T helper cell functions). ES-RA showed a greater abundance of cytokines, chemokines, and growth factors associated with severity and extra-articular manifestations. Furthermore, the correlation analysis of these proteins with deregulated inflammatory gene modules highlighted distinct increases in immunological and metabolic complexity in established RA. Lastly, baseline potential gene and protein predictors of therapeutic response to biologic and synthetic DMARDS at 6 months were identified. In ES-RA, baseline gene modules associated with immune response, cellular signalling, and energy metabolism predicted good response to TNF inhibitors but poor response to JAK inhibitors. At the protein level, altered baseline levels of circulating inflammatory mediators, which are direct targets of TNF and JAK inhibitors, predicted response to these therapies.

Conclusion: This study reveals substantial molecular differences between early and established rheumatoid arthritis, highlighting the increased molecular complexity of established forms, particularly within immune, metabolic, and regulatory pathways. This underscores the dynamic evolution of RA and its multifaceted effects on patient health. The identified gene modules and inflammatory proteins show promise as potential biomarkers for disease activity and therapeutic response, supporting the development of personalized treatment strategies for RA.

REFERENCES: NIL.

Acknowledgements: Supported by EU/EFPIA IMI-JU 3TR, ISCIII (PI21/00591, PI21/00959, CD21/00187 and RICOR-21/0002/0033), co-financed by European Union, and MINECO (RYC2021-033828-I/PID2022-141500OA-I00).

Disclosure of Interests: None declared.

© The Authors 2025. This abstract is an open access article published in Annals of Rheumatic Diseases under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ). Neither EULAR nor the publisher make any representation as to the accuracy of the content. The authors are solely responsible for the content in their abstract including accuracy of the facts, statements, results, conclusion, citing resources etc.