In Journal of autoimmunity ; h5-index 65.0
OBJECTIVE : Pathogenesis of antiphospholipid syndrome (APS) isn't fully elucidated. We aimed to identify gene signatures characterizing thrombotic primary APS (thrPAPS) and subgroups at high risk for worse outcomes.
METHODS : We performed whole blood next-generation RNA-sequencing in 62 patients with thrPAPS and 29 age-/sex-matched healthy controls (HCs), followed by differential gene expression analysis (DGEA) and enrichment analysis. We trained models on transcriptomics data using machine learning.
RESULTS : DGEA of 12.306 genes revealed 34 deregulated genes in thrPAPS versus HCs; 33 were upregulated by at least 2-fold, and 14/33 were type I and II interferon-regulated genes (IRGs) as determined by interferome database. Machine learning applied to deregulated genes returned 79% accuracy to discriminate thrPAPS from HCs, which increased to 82% when only the most informative IRGs were analyzed. Comparison of thrPAPS subgroups versus HCs showed an increased presence of IRGs among upregulated genes in venous thrombosis (21/23, 91%), triple-antiphospholipid antibody (aPL) positive (30/50, 60%), and recurrent thrombosis (19/42, 45%) subgroups. Enrichment analysis of upregulated genes in triple-aPL positive patients revealed terms related to 'type I interferon signaling pathway' and 'innate immune response'. DGEA among thrPAPS subgroups revealed upregulated genes, including IRGs, in patients with venous versus arterial thrombosis (n = 11, 9 IRGs), triple-aPL versus non-triple aPL (n = 10, 9 IRGs), and recurrent versus non-recurrent thrombosis (n = 10, 3 IRGs).
CONCLUSION : Upregulated IRGs may better discriminate thrPAPS from HCs than all deregulated genes in peripheral blood. Taken together with DGEA data, IRGs are highly expressed in thrPAPS and high-risk subgroups of triple-aPL and recurrent thrombosis, with potential treatment implications.
Verrou Kleio-Maria, Sfikakis Petros P, Tektonidou Maria G
2022-Dec-30
Antiphospholipid antibodies, Antiphospholipid syndrome, Interferon regulated genes, Machine learning, RNA sequencing
