Identifying Key Genes and Functionally Enriched Pathways in Sjögren’s Syndrome by Weighted Gene Co-Expression Network Analysis

Abstract

Purpose: Sjögren’s syndrome (SS) is an autoimmune disease characterized by dry mouth and eyes. To date, the exact molecular mechanisms of its etiology are still largely unknown. The aim of this study was to identify SS related key genes and functionally enriched pathways using the weighted gene co-expression network analysis (WGCNA). Materials and Methods: We downloaded the microarray data of 190 SS patients and 32 controls from Gene Expression Omnibus (GEO). Gene network was constructed and genes were classified into different modules using WGCNA. In addition, for the hub genes in the most related module to SS, gene ontology analysis was applied. The expression profile and diagnostic capacity (ROC curve) of interested hub genes were verified using a dataset from the GEO. Moreover, gene set enrichment analysis (GSEA) was also performed. Results: A total of 1483 differentially expressed genes were filtered. Weighted gene coexpression network was constructed and genes were classified into 17 modules. Among them, the turquoise module was most closely associated with SS, which contained 278 genes. These genes were significantly enriched in 10 Gene Ontology terms, such as response to virus, immune response, defense response, response to cytokine stimulus, and the inflammatory response. A total of 19 hub genes (GBP1, PARP9, EPSTI1, LOC400759, STAT1, STAT2, IFIH1, EIF2AK2, TDRD7, IFI44, PARP12, FLJ20035, PARP14, ISGF3G, XAF1, RSAD2,LY6E, IFI44L, and DDX58) were identified. The expression levels of the five interested genes including EIF2AK2, GBP1, PARP12, PARP14, and TDRD7 were also confirmed. ROC curve analysis determined that the above five genes’ expression can distinguish SS from controls (the area under the curve is all greater than 0.7). GSEA suggests that the SS samples with highly expressed EIF2AK2 or TDRD7 genes are correlated with inflammatory response, interferon α response, and interferon γ response. Conclusion: The present study applied WGCNA to generate a holistic view of SS and provide a basis for the identification of potential pathways and hub genes that may be involved in the development of SS.