Establishment of a nomogram model based on immune-related genes using machine learning for aortic dissection diagnosis and immunomodulation assessment.

The clinical manifestation of aortic dissection (AD) is complex and varied, making early diagnosis crucial for patient survival. This study aimed to identify immune-related markers to establish a nomogram model for AD diagnosis. Three datasets from GEO-GSE52093, GSE147026 and GSE153434-were combined and used for identification of immune-related causative genes using weighted gene co-expression network analysis, and 136 immune-related genes were obtained. Then, 15 pivotal genes were screened by the protein-protein interaction network. Through machine learning including the Least Absolute Shrinkage and Selection Operator algorithm, random forest algorithm, and multivariate logistic regression, four key feature genes were obtained-CXCL1, ITGA5, PTX3, and TIMP1-and the diagnostic scores based on these four genes were proved to be effective in distinguishing between AD patients and healthy donors. External dataset (GSE98770 and GSE190635) validation revealed this nomogram displayed strong predictive significance. Further analysis revealed that these genes are related with neutrophils, resting NK cells, resting mast cells, activated mast cells, activated dendritic cells, central memory CD4 T cells, γδ T cells, natural killer T cells, and myeloid-derived suppressor cells in AD. Finally, these four genes were validated to be upregulated in AD patients' tissue and serum samples compared with controls. These results suggest that this nomogram model, using machine learning identified four immune-related genes CXCL1, ITGA5, PTX3, and TIMP1, displays superior diagnostic ability in distinguishing AD and healthy individuals, and immune cells commonly associated with these hub genes may be therapeutic targets for AD.