ArticleRMD open2026
Transcriptomic and proteomic analysis stratifies patients with axial spondyloarthritis based on disease activity, structural damage and radiographic progression.
Article in RMD open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Progression to Radiographic Axial Spondyloarthritis: A Narrative Review on Timeline and Novel Prediction Factors.International journal of molecular sciences · 2026Review
- Biomarkers in axial spondyloarthritis diagnosis: from clinical signs to multi-omics integration.Frontiers in immunology · 2026Review
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Authors and funding
17 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo identify clusters of highly correlated genes enriched in biological functions and specific molecular pathways involved in the pathogenesis of radiographic damage in axial spondyloarthritis (axSpA) and to discover molecular biomarkers of radiographic progression and disease severity.
methodsA total of 144 patients with axSpA were included. First, RNA from peripheral blood mononuclear cells was sequenced in a cohort of 24 patients with axSpA. Hub genes were measured in a n=60 validation cohort through microfluidic PCR. A 5-year follow-up enabled the classification of the patients into fast/moderate or slow progressors. Machine learning approaches were applied to identify a predictive biomarker of progression by integrating gene expression data with clinical variables. An independent cohort of 60 patients with axSpA, with spine radiographs taken 5 years prior, underwent serum proteomic analysis using a Proximity Extension Assay.
resultsUnsupervised clustering analysis using transcriptomics revealed two distinct groups of patients with axSpA, differentiated by their clinical profiles. Weight gene correlation network analysis identified six gene modules differentially expressed between the two clusters. Patients in cluster 2 exhibited higher disease activity, greater functional impairment and more structural damage. Molecular alterations linked to structural damage revealed a specific circulating inflammatory proteome profile associated with disease severity. A predictive model composed of two genes and basal total modified Stoke Ankylosing Spondylitis Spinal Score emerged as a key biomarker for identifying moderate-to-fast radiographic progression.
conclusionsThis study identified molecular pathways involved in radiographic damage and discovered potential proteomic biomarkers of disease severity and transcriptomic predictors of radiographic progression in axSpA.
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