Machine learning and single-cell RNA sequencing identify shared diagnostic genes and mechanistic links between antiphospholipid syndrome and carotid atherosclerosis

Abstract

Background Antiphospholipid syndrome (APS) is an acquired autoimmune disorder characterized by recurrent vascular events in large, medium, or small vessels. These events contribute to cardiovascular disease primarily through thrombosis and atherosclerosis (AS). Carotid atherosclerosis (CAS) represents a particularly high-risk manifestation of subclinical AS in patients with APS. However, the shared molecular signatures linking APS and CAS remain unclear. Methods Bulk transcriptome datasets from Gene Expression Omnibus (GEO) were analyzed to identify differentially expressed genes (DEGs) in APS and CAS. Common DEGs were characterized by Gene Ontology (GO)/Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment and protein–protein interaction analyses. Candidate hub genes were prioritized by integrating Least Absolute Shrinkage and Selection Operator (LASSO), random forest, weighted gene co-expression network analysis (WGCNA), and MCODE, followed by diagnostic evaluation in independent datasets. Upstream regulatory networks and immune infiltration were assessed using in silico approaches. Single-cell RNA-seq was used to determine cell-type specificity. Genome-Wide Association Study (GWAS) summary statistics were integrated via MAGMA and intersected with expression signatures and OMIM-curated genes to prioritize additional candidates. Results A total of 4,264 DEGs were identified in APS and 838 DEGs in CAS, including 52 common DEGs (43 upregulated and nine downregulated). These common DEGs were enriched in plasma-membrane and actin-cytoskeleton–related functions, with nominal KEGG signals involving oxytocin, Jak–STAT, and PI3K–Akt pathways. Cross-method prioritization highlighted CLEC4A and P2RY13, which showed consistent dysregulation and exploratory diagnostic performance across discovery and validation datasets and exhibited stage-associated patterns in carotid plaques. Immune deconvolution suggested that CLEC4A/P2RY13 tracked with myeloid- and mastcell—related signals in CAS and with neutrophil-related signals in APS. Single-cell analysis indicated predominant expression in the mDC/cDC compartment and positive associations with mast-cell proportions in CAS. MAGMA/OMIM integration further implicated TLR8 (APS) and IL18/HAND2 (CAS) as additional candidates. Collectively, our results nominate CLEC4A and P2RY13 as candidate genes potentially involved in shared APS–CAS immune-related pathways, with expression changes associated with CAS progression among APS patients.