Exploring the potential common genetic characteristics and molecular mechanisms between inflammatory bowel disease and atherosclerosis using bioinformatics analysis and machine learning

Abstract

Abstract Background The prevalence of inflammatory bowel disease (IBD) is increasing worldwide. According to recent research, IBD is a separate risk element for atherosclerosis (AS),however the cause of IBD combined with AS is still not clear. Through a thorough bioinformatics analysis, this study investigated the potential mechanisms of AS in conjunction with IBD and aimed to pinpoint biomarkers for patients with IBD and AS complications. Methods From two microarray datasets, we obtained differentially expressed genes(DEGs) for AS and IBD using the "Limma" package. The CDEGs underwent enrichment analysis, leading to the construction of a network for protein-protein interaction (PPI). Afterward, two algorithms based on machine learning were utilized to search for hub genes. A diagnostic nomogram was created using the Hub genes. To assess the dependability of the nomogram, the ROC curve was employed.qPCR was used to analyze the expression of hub genes in animal models. The AS dataset underwent immune infiltration analysis and consensus clustering analysis in the end. Results A total of 51 CDEGs were obtained. Further screening yielded three hub genes (LCP2, MMP9, and NCF2). The nomogram demonstrated good diagnostic performance. The disease group exhibited markedly elevated expression levels of hub genes compared to the control group, as revealed by the qPCR findings.In AS, the analysis of immune infiltration showed irregularity in the infiltration of immune cell. Two molecular subtypes were identified through consensus clustering analysis, with subtype B exhibiting higher expression levels of hub genes and immune checkpoint genes compared to subtype A. Conclusion Our study revealed the common inflammatory immune pathways in IBD and AS and constructed a nomogram with good diagnostic performance based on hub genes.