Combining machine learning with external validation to explore necroptosis and immune response in moyamoya disease

Moyamoya disease (MMD) is a rare chronic vascular disease leads to cognitive impairment and stroke with its etiology unknown. The relationship between necroptosis or necroinflammation and MMD pathogenesis was poorly understood. Differentially expressed necroinflammation and necroptosis related genes (DE-NiNRGs) were selected based on the public gene expression data from Gene Expression Omnibus (GEO) and validated by our self-test data of MMD patients and control group. Functional enrichment analysis, PPI network and multi-factors regulation network construction of DE-NiNRGs were employed to discover the connections between these genes. DE-NiNRGs and immune cells correlation analysis provided evidence for the relationship between DE-NiNRGs and necroinflammation in MMD patients. We then established an MMD prediction model using support vector machine (SVM) and selected DE-NiNRGs as features. The DE-NiNRGs based MMD prediction model had excellent performance on test set with the area under the curve (AUC) higher than 0.9. Four genes, PTGER3, ANXA1, ID1, and IL1R1, that contributed significantly to the SVM model and passed the test of validation set are key genes in DE-NiNRGs. The upregulation of PTGER3 expression indicated that necroptosis and angiogenesis were promoted in MMD patients, whereas the downregulation of ANXA1 expression indicated that the migration and differentiation of immune cells are closely related to MMD pathogenesis. These findings provided new inspiration for our study of the immune-related pathogenesis and therapeutic targets of MMD.