Risk gene identification and support vector machine learning to construct an early diagnosis model of myocardial infarction

The present study aimed to identify genes associated with increased risk of myocardial infarction (MI) and construct an early diagnosis model based on support vector machine (SVM) learning. The gene expression profile data of GSE34198, containing 97 human blood samples including 49 patients with MI and 48 healthy individuals, were obtained from the Gene Expression Omnibus database. Differentially expressed gene (DEG) screening, DEG enrichment analysis, protein-protein interaction (PPI) network investigation and clustering analysis were performed. The feature genes were identified using the neighboring score algorithm. Furthermore, a recursive feature elimination (RFE) algorithm was employed to screen risk factors among feature genes. The SVM prediction model was constructed and validated using the dataset GSE61144. A total of 1,207 DEGs (724 downregulated, 483 upregulated) between the two groups were identified. PPI analysis investigated 1,083 DEGs and 46,363 edges. In total, 87 genes were selected as candidate genes, and were primarily enriched in functions including 'G-protein coupled receptor signaling' or pathways such as 'focal adhesion'. Furthermore, 15 genes with a high RFE score were selected to construct an SVM prediction model. The model's average accuracy was 86%. Data set verification showed that the predictive precision reached 0.92. High expression of the genes vascular endothelial growth factor A, A-kinase anchoring protein 12 and olfactory receptor 8D2 were potential risk factors for MI. The SVM early diagnosis model constructed by candidate genes could not only predict early MI, but also provide risk probability according to the severity of MI.