What question did this study set out to answer?

This study aims to identify T-cell exhaustion-related biomarkers associated with aortic dissection and their role in disease pathogenesis.

June 28, 2026Open Access

Identification of T-cell exhaustion-related biomarkers in aortic dissection via integrated multi-omics analysis and mendelian randomization

Key Points

This study aims to identify T-cell exhaustion-related biomarkers associated with aortic dissection and their role in disease pathogenesis.
Differentially expressed TEXRGs were identified using transcriptomic datasets and functional enrichment analyses.
Mendelian randomization was utilized to explore causal associations with aortic dissection risk.
Machine learning algorithms selected key diagnostic biomarkers and immune landscapes were characterized using GSEA and scRNA-seq.
Identified 270 DETEXRGs primarily enriched in cytokine-mediated signaling pathways.
Six genes were found to have significant causal associations with aortic dissection susceptibility.
Validation showed elevated expression levels of FPR1 and other genes in aortic dissection samples.

Abstract

BACKGROUND: Aortic dissection (AD) is a life-threatening cardiovascular emergency characterized by high acute mortality. While immune dysregulation is known to drive AD pathogenesis, the specific involvement of T-cell exhaustion-related genes (TEXRGs) remains largely elusive. METHODS: Differentially expressed TEXRGs (DETEXRGs) between AD and control samples were identified using transcriptomic datasets. Functional enrichment and Mendelian randomization (MR) analyses were performed to investigate potential causal associations with AD risk. Key diagnostic biomarkers were selected through integrated machine learning algorithms. Immune infiltration landscapes were characterized via gene set enrichment analysis (GSEA), while single-cell RNA sequencing (scRNA-seq) was employed to elucidate cell-type-specific expression profiles. Furthermore, the transcription factor (TF)-regulatory network and drug-gene interaction map were constructed. Finally, the expression levels of candidate genes were validated using quantitative real-time PCR (qRT-PCR). RESULTS: A total of 270 DETEXRGs were identified, which were predominantly enriched in cytokine-mediated signaling pathways and viral infection-related processes. MR analysis identified six genes significant causal associations with AD susceptibility. Among these, CASP4 and FPR1 were prioritized as core biomarkers through integrated machine learning algorithms. Immune infiltration analysis revealed a significantly altered immune landscape in AD tissues, characterized by the enrichment of eight immune cell subtypes that positively correlated with the expression of the identified biomarkers. Furthermore, scRNA-seq analysis localized FPR1 expression primarily to macrophages and monocytes. Finally, qRT-PCR validation confirmed significantly elevated expression levels of FPR1, PLSCR1, and other candidate genes in AD samples. CONCLUSION: This study underscores the critical involvement of T-cell exhaustion-related mechanisms in the pathogenesis of AD and robust diagnostic biomarkers. These findings offer novel insights for early risk stratification and provide a theoretical foundation for the development of targeted immunotherapeutic strategies in aortic dissection.

KI fragen

Bookmark

View Full Paper