bioRxivMolecular BiologyPreprint
Identification of key candidate genes and pathways in axial Spondyloarthritis through integrated bioinformatics analysis
Zhenzhen ZhangiD, Jing ZengiD, Haihong LiiD, Yucong ZouiD, Shuang LiangiD, Gang LiuiD
Abstract
BackgroundRadiographic axial Spondyloarthritis (r-axSpA) is the prototypic form of seronegative spondyloarthritis (SpA). In the present study, we evaluated the key genes related with r-axSpA, and then elucidated the possible molecular mechanisms of r-axSpA. Material/MethodsThe gene expression GSE13782 was downloaded from the GEO database contained five proteoglycan-induced spondylitis mice and three naive controls. The differentially expressed genes (DEGs) were identified with the Bioconductor affy package in R. Gene Ontology (GO) enrichment and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were built with the DAVID program followed by construction of a protein-protein interaction (PPI) network performed with Cytoscape. WebGestalt was performed to construct transcriptional regulatory network and microRNAs-target regulatory networks. RT-PCR and immunohistochemical staining were performed to testify the expression of hub genes, transcription factors (TFs) and microRNAs. ResultsA total of 230 DEGs were identified. PPI networks were constructed by mapping DEGs into STRING, in which 20 hub proteins were identified. KEGG pathway analyses revealed that the chemokine, NOD-like receptor, IL-17, and TNF signalling pathways were altered. GO analyses revealed that DEGs were extensively involved in the regulation of cytokine production, the immune response, the external side of the plasma membrane, and G-protein coupled chemoattractant receptor activity. The results of RT-PCR and immunohistochemical staining demonstrated that the expression of DEGs, TFs and microRNAs in our experiment were basically consistent with the predictions. ConclusionsThe results of this study offer insight into the pathomechanisms of r-axSpA and provide potential research directions.
- organism: mouse
- assay: bulk RNA-seq
- research focus: differential expression
- approach: statistical model
- Citations
- 0
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