2023, Vol. 27 ›› Issue (11): 1787-1795

Rheumatoid arthritis Hub genes and their significance with gene set enrichment analysis and weighted gene co-expression network analysis methods

Sun Yumin1, Song Jiexin2, Yang Yanmei3, Yang Dongliang4   

Abstract: BACKGROUND: Rheumatoid arthritis patients are still negative for 30% of traditional serum markers detected by the existing immunological methods. In recent years, serum biomarkers have been found to have low sensitivity and specificity for the diagnosis of rheumatoid arthritis, which are not suitable for clinical promotion.
OBJECTIVE: To analyze rheumatoid arthritis Hub genes and the significance with gene set enrichment analysis and weighted gene co-expression network analysis methods.
METHODS: Data sets GSE1919 and GSE55235 were downloaded from GEO database, differential expression genes were initially screened by R software "limma" package and "clusterProfiler" package for the weighted gene co-expression network analysis, and intersected genes were selected as candidate Hub genes. The results were imported into String database to construct a protein-protein interaction network. A Cytoscape plugin, cytoHubba, was used to screen hub genes and perform gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. At the same time, the two data sets were analyzed by the GSEA method.
RESULTS AND CONCLUSION: A total of 10 Hub genes were identified, and their GO functions were concentrated in lymphocyte differentiation, T cell differentiation, plasma membrane signal receptor complex, major histocompatibility complex protein binding, and other biological functions. KEGG pathway was enriched in T cell receptor signaling pathway, Th1/Th2 cell differentiation, Th17 cell differentiation, tumor cell programmed death-ligand 1 expression and programmed death receptor 1 checkpoint pathway, and primary immune deficiency pathway. Gene set enrichment analysis showed that three pathways such as bronchial asthma, autoimmune thyroid disease, and cell adhesion molecules were significantly up-regulated in rheumatoid arthritis samples from two datasets. The receiver operating characteristic curve showed that five genes (CD27, IL2RG, CD8A, LCK, and NKG7) had good specificity and sensitivity (AUC > 0.85) for the diagnosis of rheumatoid arthritis. Altogether, CD4 may be a multipotent coordinator of the gene network, performing important functions in the immune response.
Key words: arthritis, rheumatoid, synovium, gene set enrichment analysis, weighted gene co-expression network analysis