Differential gene expression analysis reveals common biomarkers for systemic lupus erythematosus and atrial fibrillation

Abstract

Abstract Background Atrial fibrillation (AF) is a significant outcome of systemic lupus erythematosus (SLE), increasing the chances of experiencing blood clotting events and unforeseen mortality. As the underlying mechanism of SLE companied with AF is still unknown, this study sought to uncover potential biomarkers that could be of significant value for individuals dealing with SLE and AF, employing thorough bioinformatics research as the primary approach. Methods The NCBI Gene Expression Omnibus database (GEO) was employed to retrieve a collection of five microarray datasets (GSE50772, GSE41177, GSE79768, GSE81622, and GSE2240). By employing the online analytical tool GEO2R, we conducted an analysis of GSE50772 to pinpoint genes that exhibited differential expression. Significant module genes were discovered by WGCNA (weighted gene co-expression network analysis). To identify DEGs in AF, the 'Limma' package was utilized. Function of the common DEGs was found by functional enrichment analysis. The candidate biomarkers were discovered by applying a machine learning technique. The investigation involved the utilization of Single Sample Gene Set Enrichment Analysis (ssGSEA) scores to perform functional enrichment analysis on the identified candidate biomarkers. To predict the risk of AF in individuals with SLE, a nomogram and a ROC curve were created. The analysis focused on examining the presence of immune cells infiltrating the training datasets of SLE and AF, while also conducting a consensus cluster analysis specifically for SLE. Results 29 common DEGs were identified between SLE and AF. The identification and utilization of five potential biomarkers-ANKRD36B, SLC4A4, ANKRD12, MTUS1 and DSC1-led to the creation of a nomogram with area under the receiver operating characteristic curve 0.900-0.981 across all datasets. The dysregulated immune cell infiltration was associated with the biomarkers. Based on the consensus clustering analysis, it was concluded that three subtypes were the most suitable in terms of quantity. The biomarkers exhibited different expression patterns among the subtypes. Regarding immunological infiltration, each subtype possessed unique traits. Conclusion By employing various bioinformatics research approaches and machine learning techniques, our study identified five candidate biomarkers (ANKRD36B, SLC4A4, ANKRD12, MTUS1, DSC1). Additionally, a nomogram capable of predicting the likelihood of both SLE and AF was developed. The results of our study provide a foundation for future investigations on potential important genes for AF in individuals with SLE. Moreover, it was discovered that AF and SLE exhibited abnormal compositions of immune cells.