ObjectiveTo investigate key differentially expressed genes (DEGs) in peripheral blood of idiopathic epilepsy patients, as well as their biological functions, cellular localization, involved signaling pathways, through bioinformatics analysis. So to provide new insights for the pathogenesis and prevention of idiopathic epilepsy.MethodsFirstly, we screened and downloaded microarray data including 6 peripheral blood samples of drug-naive patients with idiopathic epilepsy, 8 peripheral blood samples of responders of idiopathic epilepsy treated with Valproate (VPA), and 10 peripheral blood samples of non-responders of idiopathic epilepsy treated with VPA from Gene Expression Omnibus (GEO) data series GSE143272, which Public in January 2020. Secondly, we identified DEGs via the limma package and others in R software. Then we had gotten 74 DEGs, and subsequently conducted gene ontology and pathway enrichment analysis, PPI network analysis and hub gene analysis, using multiple methods containing DAVID, STRING, and Cytohubba in Cytoscape.ResultsWe had identified significant hub DEGs, including TREML3P, KCNJ15, ORM1, RNA28S5, ELANE, RETN, ARG1, LCN2, SLPI, HP, PGLYRP1, BPI, DEFA4, TCN1, MPO, MMP9, CTSG, CXCL8, RNASE3, RNASE2, S100A12, DEFA1B, DEFA1, DEFA3, CEACAM8, MS4A3, PTGS2, PI3, CCL3. The biological processes involved in these DEGs include immune response, inflammatory response, chemotaxis, etc. While, the molecular function is focused on peroxidase activity, chemokine activity, etc. Moreover, KEGG pathway enrichment analysis shows that DEGs were mainly involved in cytokine-cytokine receptor interaction, Toll-like receptor signaling pathway, chemokine signaling pathway and so on.ConclusionThese important key DEGs may be involved in the onset and development of idiopathic epilepsy through a variety of signaling pathways and complex mechanisms.
Objective To screen the differentially expressed genes and pathways involved in rosacea using bioinformatics analysis. Methods The GSE65914 gene chipset was collected from the Gene Expression Omnibus (up to July 12th, 2021). It was searched according to the keyword “rosacea”. The data was analyzed by GEO2R platform. The common differential genes of three subtypes of rosacea were screened out. The online DAVID analysis tool was used to perform the gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Protein-protein interaction networks of differentially expressed genes were made by String and Cytoscape. The key modules and genes were screened by Mcode and Cytohubba. Results A total of 957 common differential genes were identified, including 533 up-regulated genes and 424 down-regulated genes. GO enrichment analysis showed that these genes were mainly involved in immune response, inflammatory response, intercellular signal transduction, positive regulation of T cell proliferation, chemokine signaling pathways, cell surface receptor signaling pathways, cellular response to interferon-γ, and other biological processes. KEGG pathway enrichment analysis mainly included cytokine-cytokine receptor interaction, rheumatoid arthritis, chemokine signaling pathway, PPAR signaling pathway, Toll-like receptor signaling pathway, nuclear transcription factor-κB signaling pathway, tumor necrosis factor signaling pathway and other signaling pathways. Cytohubba analysis revealed 10 key genes, including PTPRC, MMP9, CCR5, IL1B, TLR2, STAT1, CXCR4, CXCL10, CCL5 and VCAM1. Conclusion The key genes and related pathways may play an important role in the pathogenesis of rosacea.