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Objectives <p>We aim to identify novel biomarkers of rheumatoid arthritis (RA) outcomes by performing serum proteomic profiling of our longitudinal RA cohort using Data-Independant Acquisition (DIA) mass spectrometry.</p> Methods <p>Serum proteomes from 107 previously untreated early RA patients were recruited in the EUPA cohort and their sera were analyzed at baseline and at the 12-month follow-up visit using DIA mass spectrometry technology. Clustering analyses on both baseline and follow-up serum profiles was performed to assess overall patient heterogeneity. Generalized estimating equations (GEE) analyses of longitudinal serum proteome data was used to query for proteins associated with disease activity or erosion outcomes. Functional networks of the identified proteins were explored using KEGG enrichment analysis while novel predictors of RA outcomes were identified using generalized linear models (GLM) with baseline serum proteomes, and performance was evaluated by receiver operating characteristic (ROC) curves.</p> Results <p>RA patients could be separated in 2 distinct clusters based on their serum proteome profiles, regardless of disease activity or erosion outcomes. Compared to CRP, the protein signature composed of APOC4, SAA1/SAA2 and PFN1 exhibited a trend toward improved predictive performance for disease activity. Furthermore, an additional protein signature combining CTBS and IGHV1-18 outperformed classic autoantibodies serology status to predict erosiveness Functional network enrichment analyses uncovered association between the complement system and RA progression.</p> Conclusion <p>Leveraging serum proteomic profiling, we identified novel biomarker candidates of RA outcomes, reinforcing the notion that protein signatures can improve predictive performance while highlighting crucial elements of pathophysiology that might lie in the complement system. Further clinical validation of these predictors may potentially pave the way toward improved personalized treatment strategies and ultimately better RA management.</p>

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Serum mass spectrometry unveil the heterogeneity of rheumatoid arthritis and reveals insights into complement pathways and IGHV as potential prognostic markers

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年8月1日

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  • Benoît Marchand,
  • Nathalie Carrier,
  • Elizabeth Beaulieu,
  • Dominique Lévesque,
  • Barath Ramanathan,
  • François-Michel Boisvert,
  • Sophie Roux,
  • Javier Marrugo,
  • Gilles Boire,
  • Hugues Allard-Chamard

摘要

Objectives

We aim to identify novel biomarkers of rheumatoid arthritis (RA) outcomes by performing serum proteomic profiling of our longitudinal RA cohort using Data-Independant Acquisition (DIA) mass spectrometry.

Methods

Serum proteomes from 107 previously untreated early RA patients were recruited in the EUPA cohort and their sera were analyzed at baseline and at the 12-month follow-up visit using DIA mass spectrometry technology. Clustering analyses on both baseline and follow-up serum profiles was performed to assess overall patient heterogeneity. Generalized estimating equations (GEE) analyses of longitudinal serum proteome data was used to query for proteins associated with disease activity or erosion outcomes. Functional networks of the identified proteins were explored using KEGG enrichment analysis while novel predictors of RA outcomes were identified using generalized linear models (GLM) with baseline serum proteomes, and performance was evaluated by receiver operating characteristic (ROC) curves.

Results

RA patients could be separated in 2 distinct clusters based on their serum proteome profiles, regardless of disease activity or erosion outcomes. Compared to CRP, the protein signature composed of APOC4, SAA1/SAA2 and PFN1 exhibited a trend toward improved predictive performance for disease activity. Furthermore, an additional protein signature combining CTBS and IGHV1-18 outperformed classic autoantibodies serology status to predict erosiveness Functional network enrichment analyses uncovered association between the complement system and RA progression.

Conclusion

Leveraging serum proteomic profiling, we identified novel biomarker candidates of RA outcomes, reinforcing the notion that protein signatures can improve predictive performance while highlighting crucial elements of pathophysiology that might lie in the complement system. Further clinical validation of these predictors may potentially pave the way toward improved personalized treatment strategies and ultimately better RA management.