Session Type: Poster Session (Monday)
Session Time: 9:00AM-11:00AM
Background/Purpose: Systemic lupus erythematosus (SLE) is a common autoimmune disease. The occurrence and development of SLE is a result of multiple factors, but its exact pathogenesis has not been fully elucidated.
Methods: Here, we used bioinformatics to analyze and identify the key pathogenic genes of SLE and to reveal the underlying pathogenic molecular mechanism.
Results: The expression profiles of GDS4185, GDS4888, GDS4889 and GDS4890 were downloaded from the Gene Expression Omnibus (GEO) database, which contained 99 samples, including 42 cases of SLE samples and 57 cases of normal samples. The four microarray datasets were integrated to get differentially expressed genes (DEGs). The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichments of DEGs were operated by DAVID and KOBAS online calculuses, respectively. The protein–protein interaction (PPI) networks of the DEGs were created from the STRING database. A total of 839 DEGs were extracted from the four GEO datasets, of which 289 genes were upregulated and 550 genes were downregulated. GO analysis indicated that the biological functions of DEGs focused primarily on response to virus, type I interferon signaling pathway and cellular protein metabolic process. The main cellular components include perinuclear region of cytoplasm, focal adhesion and cell-cell adherens junction. The molecular functions include protein binding, double-stranded RNA binding and actin filament binding. KEGG pathway analysis showed that these DEGs were mainly involved in the Osteoclast differentiation, HTLV-I infection, Measles, FoxO signaling pathway, Herpes simplex infection, Primary immunodeficiency and Jak-STAT signaling pathway. The 14 most closely related genes among DEGs were identified from the PPI network.The 14 genes are: HERC5, TP53, CDC20, GNB2, GNB4, PPP2R1A, GNAI2, PMCH, SOCS3, HERC6, STAT1, SOCS1, ISG15, IFIT3.
Conclusion: This research suggests that screening for DEGs and pathways in SLE using integrated bioinformatics methods could help us realize the molecular mechanism underlying the development of SLE, be of clinical implication for the early diagnosis and prevention of SLE, and afford reliable targets for the treatment of SLE.
To cite this abstract in AMA style:Liu W, Wu Y, Zhao W. Identification of Differentially Expressed Genes and Signaling Pathways in Systemic Lupus Erythematosus by Integrated Bioinformatics Analysis [abstract]. Arthritis Rheumatol. 2019; 71 (suppl 10). https://acrabstracts.org/abstract/identification-of-differentially-expressed-genes-and-signaling-pathways-in-systemic-lupus-erythematosus-by-integrated-bioinformatics-analysis/. Accessed April 2, 2020.
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