Identification of immune-related genes in diagnosing atherosclerosis with rheumatoid arthritis through bioinformatics analysis and machine learning - Liu, Fuze; Huang, Yue; Liu, Fuhui; Wang, Hai (Frontiers in Immunology, 2023)
Abstract Background: Increasing evidence has proven rheumatoid arthritis can aggerate atherosclerosis, and we aimed to explore potential diagnostic genes for RA patients with AS. Methods: We got the data from public databases, including gene expression omnibus (GEO), STRING, and obtained the differentially expressed genes (DEGs) and module genes with Limma and weighted gene co-expression network analysis (WGCNA). Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analysis, protein-protein interaction (PPI) network and machine learning algorithms (least absolute shrinkage and selection operator (LASSO) regression and random forest) were performed to explore the immune-related hub genes. And we used a nomogram and receiver operating characteristic (ROC) curve to assess the diagnostic efficacy. Finally, immune infiltration was developed in AS. Result: The AS dataset included 5322 DEGs, while there were 1439 DEGs and 206 module genes in RA. The intersection of DEGs for AS and crucial genes for RA was 53, which were involved in immunity. After the PPI network and machine learning construction, six hub genes were used for the construction of nomogram and diagnostic efficacy assesment, which showed great diagnostic value (area under the curve from 0.723 to 1). And immune infiltration also revealed the disorder of immunocytes. Conclusion: Six immune-related hub genes (NFIL3, EED, GRK2, MAP3K11, RMI1, TPST1) were recognized, and the nomogram was developed for AS with RA diagnosis.