Clinical value of ARG1 in acute myocardial infarction patients: Bioinformatics-based approach

Abstract

Background: In this study, we aimed to explore key genes as biomarker for diagnosis AMI through using Bioinformatics tools. Methods: GSE4648 and GSE60993 were downloaded from Gene Expression Omnibus (GEO). DEGs in GSE4648 and GSE60993 were selected to run GO enrichment, KEGG pathway, and PPI network. Hub genes in DEGS of GSE4648 and GSE60993 were selected out according to Molecular Complex Detection (MCODE) and overlapping genes were further screened out. Finally, the most important gene of coincide genes was used for deeper clinical study of patients with AMI. Results: A total of 41 and 173 DEGs were screened out in GSE4648 and GSE60993 respectively. GO and KEGG analysis showed similar biological process, cellular Component and molecular function of these two group DEGs. PPI network of these two group DEGs were built and 19 key genes of GSE4648 were selected out according to MCODE, while 48 key genes of GSE60993 were selected out. Overlapping genes of these 19 and 48 genes included PLAUR, ARG1, FOS, and IL1R2, and fold change (FC) of ARG1 was the biggest. Therefore, ARG1 was detected in 46 controls and 115 AMI patients by ELISA, and ARG1 was significantly upregulated in AMI group. Pearson correlation analysis indicated ARG1 was positive correlated with gensini score (R = 0.378). ROC curve revealed that area under ROC curve (AUC) of ARG1 was 77.6%. Conclusion: Therefore, ARG1 might play an important role in the development of AMI and could be used as biomarker of AMI.

Keywords

• Acute myocardial infarction
• Bioinformatics analysis
• Differentially expressed genes
• Biomarker
• ARG1