Exploration of biomarkers for systemic lupus erythematosus by machine-learning analysis

Abstract

Abstract Background In recent years, research on the pathogenesis of systemic lupus erythematosus (SLE) has made great progress. However, the prognosis of the disease remains poor, and high sensitivity and accurate biomarkers are particularly important for the early diagnosis of SLE. Methods SLE patient information was acquired from three Gene Expression Omnibus (GEO) databases and used for differential gene expression analysis, such as weighted gene coexpression network (WGCNA) and functional enrichment analysis. Subsequently, three algorithms, random forest (RF), support vector machine-recursive feature elimination (SVM-REF) and least absolute shrinkage and selection operation (LASSO), were used to analyze the above key genes. Furthermore, the expression levels of the final core genes in peripheral blood from SLE patients were confirmed by real-time polymerase chain reaction (PCR) assay. Results Five core genes (ABCB1, CD247, DSC1, KIR2DL3 and MX2) were found in this study. Moreover, the nomogram model showed that the five optimal key genes had good reliability and validity, which were further confirmed by clinical samples from SLE patients. The receiver operating characteristic (ROC) curves of the five genes also revealed that they had critical roles in the pathogenesis of SLE. Conclusion Overall, five key genes were obtained and validated through machine-learning analysis of the databases, which might offer a new perspective for the molecular mechanism and potential therapeutic targets for SLE.