Abstract
This study was designed to identify new biomarkers for early diagnosis of acute myocardial infarction (AMI). GSE66360 and GSE48060 datasets were downloaded from the Gene Expression Omnibus (GEO) database. The "limma" tool was used to screen differentially expressed genes (DEGs). A total of 557 anoikis-related genes (ARGs) were obtained from the GeneCard database. Differentially expressed ARGs (DEARGs) were obtained by intersecting DEGs with ARGs. The least absolute shrinkage and selection operator (LASSO), support vector machine (SVM), and Random Forest (RF) were used to screen the hub DEARGs. Real-time quantitative polymerase chain reaction (RT-qPCR) was used to determine the expression of hub DEARGs. A total of 21 DEARGs were obtained, all of which were up-regulated in AMI samples. Functional enrichment analysis showed that the DEARGs were mainly enriched in peptidase activity and extracellular matrix. Immune cell infiltration analysis revealed a significant difference in 14 immune cells between the AMI and normal groups. Nine feature risk genes, including ITPRIP, MMP9, NAMPT, CDKN1A, PLAUR, PLAU, SERPINA1, THBS1, and FN1 were screened by LASSO, SVM, and RF. The RT-qPCR analyses verified that the feature genes were up-regulated in AMI patients, which were basically consistent with the main bioinformatics analysis results. We also validated 9 hub DEARGs in the GSE48060 dataset and constructed a nomogram by integrating these DEARGs. This study analyzed the differential expression of ARGs and immune profiles in AMI and normal samples, screened 9 risk feature genes for predicting AMI, and provided a theoretical basis for the immunotherapy regimen of AMI.