Identification of shared diagnostic biomarkers and immune-related mechanisms between sepsis and thoracic aortic aneurysm based on integrated bioinformatics and machine learning approaches.

backgroundSepsis and thoracic aortic aneurysm (TAA) share pathological features of inflammation and vascular dysfunction, yet their molecular connections remain unclear.

methodsTranscriptomic datasets of sepsis and TAA from GEO were integrated to identify shared differentially expressed genes (DEGs). Functional enrichment and gene set enrichment analyses were performed to explore potential pathways. Machine learning models were applied to identify candidate genes with discriminatory potential, followed by immune infiltration analysis and assessment of gene expression in lipopolysaccharide (LPS)-treated vascular smooth muscle cells (VSMCs).

resultsA total of 193 shared DEGs were identified, mainly enriched in T cell activation, Th17 differentiation, and oxidative stress-related pathways. Five candidate genes (ARG1, IL18R1, LIX1L, TRIM32, and PRKCH) were selected through sequential machine-learning analysis and showed generally reproducible expression patterns across independent datasets. Their expression was associated with estimated immune cell proportions. In LPS-treated VSMCs, ARG1 and IL18R1 were upregulated, whereas LIX1L was downregulated.

conclusionSepsis and TAA share common transcriptional signatures involving immune dysregulation and metabolic stress. ARG1 and IL18R1 may represent candidate molecular features associated with inflammatory and vascular responses.