Abstract
BackgroundEvidence is accumulating to characterise the key differences between systemic sclerosis (SSc) and rheumatoid arthritis (RA), which are similar but distinct systemic autoimmune diseases. However, the differences at the genetic level are not yet clear. Therefore, the aim of the present study was to identify key differential genes between patients with SSc and RA.MethodsThe Gene Expression Omnibus database was used to identify differentially expressed genes (DEGs) between SSc and RA biopsies. The DEGs were then functionally annotated using Gene Ontology (GO) terms and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways with the Database for Annotation, Visualization and Integrated Discovery (DAVID) tools. A protein–protein interaction (PPI) network was constructed with Cytoscape software. The Molecular Complex Detection (MCODE) plugin was also used to evaluate the biological importance of the constructed gene modules.ResultsA total of 13,556 DEGs were identified between the five SSc patients and seven RA patients, including 13,465 up-regulated genes and 91 down-regulated genes. Interestingly, the most significantly enriched GO terms of up- and down-regulated genes were related to extracellular involvement and immune activity, respectively, and the top six highly enriched KEGG pathways were related to the same processes. In the PPI network, the top 10 hub nodes and top four modules harboured the most relevant genes contributing to the differences between SSc and RA, including key genes such as IL6, EGF, JUN, FGF2, BMP2, FOS, BMP4, LRRK2, CTNNB1, EP300, CD79, and CXCL13.ConclusionsThese genes such as IL6, EGF, JUN, FGF2, BMP2, FOS, BMP4, LRRK2, CTNNB1, EP300, CD79, and CXCL13 can serve as new targets for focused research on the distinct molecular pathogenesis of SSc and RA. Furthermore, these genes could serve as potential biomarkers for differential diagnoses or therapeutic targets for treatment.
Highlights
Systemic sclerosis (SSc) is an autoimmune disease [1] that is often characterised by joint involvement, especially arthritis [2]
differentially expressed genes (DEGs) identification by microarray expression profiling Using the Gene Expression Omnibus (GEO) GSE93698 dataset of microarray data, we identified a total of 13,556 DEGs (p < 0.05 and |logFC| > 1) between systemic sclerosis (SSc) and rheumatoid arthritis (RA) samples, including 13,465 up-regulated genes and 91 down-regulated genes
Gene Ontology (GO) functional enrichment To investigate the functions of the large range of gene signatures obtained, we performed GO enrichment analysis from the GO database [19] including terms of the biological process, molecular function, and cellular component categories for the top 1000 up-regulated genes and 91 down-regulated genes (Fig. 2)
Summary
Systemic sclerosis (SSc) is an autoimmune disease [1] that is often characterised by joint involvement, especially arthritis [2]. SSc and RA are diagnosed by auxiliary approaches such as clinical manifestations, biochemical indicators, and X-ray findings [5, 6] Since they are both autoimmune diseases with similar clinical signs and symptoms, especially joint involvement, it is not easy to distinguish between them in some cases with uncharacteristic signs and symptoms. Gene expression profiling with microarrays is regarded as a standard method for identifying differentially expressed genes (DEGs) and potential biological pathways associated with SSc [7] and RA [8]. Evidence is accumulating to characterise the key differences between systemic sclerosis (SSc) and rheumatoid arthritis (RA), which are similar but distinct systemic autoimmune diseases.
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