Identification of shared genes of atherosclerosis and Crohn's disease based on bioinformatics and machine learning algorithm

Abstract

Abstract Background: Atherosclerosis (AS) is a major contributor to cardiovascular mortality and morbidity globally. There is currently a dearth of information about Crohn's disease (CD) and its relation to the risk of atherosclerotic cardiovascular disease. However, recent studies were unable to identify the biological mechanism at the genetic level. Methods: Four microarray datasets (GSE43292, GSE28829, GSE186582, and GSE102133) were downloaded from the Gene Expression Omnibus database. The Limma package was used to identify differentially expressed genes (DEGs) in AS and CD. Functional enrichment analysis, protein-protein interaction (PPI) network construction, and machine learning algorithms were applied to identify hub genes. Then the hub genes were calculated based on the receiver operating characteristic (ROC) curve. Subsequently, we conducted single-gene GSEA analysis and immune infiltration analysis to further investigated the possible mechanism of the hub genes. Results: A total of 24 common upregulated DEGs and 10 common downregulated DEGs were observed in AS and CD. According to enrichment analyses, these genes were connected with immune-related and inflammation-related signaling pathways. Three hub genes (IL1RN, TNFSF13B, PRDM1) were identified. The AUC of hub genes was higher than 0.7 and the AUC of the hub genes-based logistic regression model was 0.873. Single-gene GSEA analysis and immune infiltration analysis found the hub genes were associated with AS in CD patients, and there were some associations between hub genes and 22 immune cells. Conclusion: We identified and validated 3 hub genes (IL1RN, TNFSF13B, PRDM1) as biomarkers in AS and CD. This study may provide a new perspective on the pathogenesis of AS and CD comorbidity.