Transcriptome-Based Identification of Biomarkers Associated With Sphingosine-1-Phosphate Signaling Pathway in Aortic Dissection.

Background: Aortic dissection (AD) is the most dangerous disease in acute aortic syndrome and is associated with serious complications. Current studies have shown that sphingosine-1-phosphate (S1P) has a certain effect on AD. Therefore, this study focuses on exploring biomarkers related to S1P in AD. Methods: Differentially expressed genes (DEGs) between AD and normal samples were identified from the GSE153434 dataset. Key module genes associated with the S1P score were then obtained using weighted gene coexpression network analysis (WGCNA). The DEGs were intersected with these key module genes to derive a set of intersection genes. Subsequently, a protein-protein interaction (PPI) network was constructed and screened to identify candidate genes. Further biomarker mining was performed through machine learning approaches followed by validation. Following this, gene set enrichment analysis (GSEA), immune infiltration analysis, investigation of regulatory mechanisms, and drug prediction were conducted. Finally, we quantified S1P concentration in human plasma using an ELISA kit, established an AD rat model, and validated gene expression levels using quantitative real-time polymerase chain reaction (qRT-PCR). Results: A total of 651 intersection genes were identified from the overlap between the 702 DEGs and 7108 key module genes. Subsequently, 20 candidate genes were screened, yielding two biomarkers: CXCL5 and ITGA5. Both biomarkers were enriched in the p53 signaling pathway, porphyrin and chlorophyll metabolism, and the NOD-like receptor signaling pathway. Furthermore, eight types of immune cells, including central memory CD4 T cells and natural killer T cells, were significantly elevated in the AD group compared with controls. ELISA quantification confirmed elevated S1P levels in human plasma. Additionally, utilizing an established AD rat model, we provided the first experimental validation that ITGA5 is highly expressed in dissected aortic tissue. Notably, CXCL5 exhibited the strongest significant positive correlation with central memory CD4 T cells. Regulatory network analysis revealed a relatively complex lncRNA-miRNA-mRNA interaction network. Finally, seven potential small-molecule drugs targeting ITGA5 were predicted, including cilmostim, cilengitide, and dimethyl sulfoxide. Conclusion: This study identifies ITGA5 as a novel biomarker for S1P-associated AD and reveals its potential underlying mechanisms and therapeutic candidates, providing a theoretical foundation for AD diagnosis and treatment.