Identification of Diagnostic Gene Markers and Immune Infiltration in Systemic Lupus

Abstract

Background. Systemic lupus erythematosus (SLE) is an autoimmune disease involving multiple organs, with atypical clinical manifestations and indefinite diagnosis and treatment. So far, the etiology of the disease is not completely clear. Current studies have known the interaction of genetic system, endocrine system, infection, environment, and other factors. Due to abnormal immune function, the human body, with the participation of various immune cells such as T cells and B cells, abnormally recognizes autoantigens, so as to produce a variety of autoantibodies and combine them to form immune complexes. These complexes will stay in the skin, kidney, serosa cavity, large joints, and even the central nervous system, resulting in multisystem damage of the body. The disease is heterogeneous, and it can show different symptoms in different populations and different disease stages; patients with systemic lupus erythematosus need individualized diagnosis and treatment. Therefore, we aimed to search for SLE immune-related hub genes and determine appropriate diagnostic genes to provide help for the detection and treatment of the disease. Methods. Gene expression data of whole blood samples of SLE patients and healthy controls were downloaded from the GEO database. Firstly, we analyzed and identified the differentially expressed genes between SLE and the normal population. Meanwhile, the single-sample gene set enrichment analysis (ssGSEA) was used to identify the activation degree of immune-related pathways based on gene expression profile of different patients, and weighted gene coexpression network analysis (WGCNA) was used to search for coexpressed gene modules associated with immune cells. Then, key networks and corresponding genes were found in the protein-protein interaction (PPI) network. The above corresponding genes were hub genes. After that, this study used receiver operating characteristic (ROC) curve to evaluate hub gene in order to verify its ability to distinguish SLE from the healthy control group, and miRNA and transcription factor regulatory network analyses were performed for hub genes. Results. Through bioinformatics technology, compared with the healthy control group, 2996 common differentially expressed genes (DEGs) were found in SLE patients, of which 1639 genes were upregulated and 1357 genes were downregulated. These differential genes were analyzed by ssGSEA to obtain the enrichment fraction of immune-related pathways. Next, the samples were selected by WGCNA analysis, and a total of 18 functional modules closely related to the pathogenesis of SLE were obtained. Thirdly, the correlation between the above modules and the enrichment fraction of immune-related pathways was analyzed, and the turquoise module with the highest correlation was selected. The 290 differential genes of this module were analyzed by GO and KEGG. The results showed that these genes were mainly enriched in coronavirus disease (COVID-19), ribosome, and human T cell leukemia virus 1 infection pathway. The 290 DEGs with PPI network and 28 genes of key networks were selected. ROC curve showed that 28 hub genes are potential biomarkers of SLE. Conclusion. The 28 hub genes such as RPS7, RPL19, RPS17, and RPS19 may play key roles in the advancement of SLE. The results obtained in this study can provide a reference in a certain direction for the diagnosis and treatment of SLE in the future and can also be used as a new biomarker in clinical practice or drug research.