Identification of IFIT3 as potential biomarker in Psoriasis based on integrated bioinformatics analysis

Abstract Background: To explore the function and related signal pathway of IFIT3 gene in skin lesions of patients with psoriasis by bioinformatics methods, and to find the potential specific molecular markers of psoriasis. Methods: The limma R package was used to analyze the three data sets of GSE13355, GSE30999 and GSE106992 from the GEO (Gene Expression Omnibus) and the differential genes were screened. The STRING database was used for GO (Gene ontology) enrichment analysis, KEGG (Kyoto encyclopedia of genes and genomes) pathway analysis and protein-protein interaction (PPI) network integration. After that, the IFIT3 subnetwork was extracted and analyzed by GSEA (gene set enrichment analysis) using Metascape database and the effectiveness of gene differentiation and disease tissue identification was verified. Results: In the study, 426 differential genes were obtained, of which 322 were significantly up-regulated and 104 were significantly down-regulated. GO enrichment analysis showed that differential genes were mainly involved in the process of immunity and metabolism, KEGG pathway enrichment analysis mainly included chemokine signal pathway, PPAR signal pathway, IL-17 signal pathway and so on. According to the analysis of IFIT3 subnetwork, it was found that IFIT3 was mainly involved in the biological processes of viruses, bacteria and other microorganisms. The pathways obtained by GESA were mainly related to immune, metabolic and antiviral pathways. IFIT3 was highly expressed in psoriatic lesions and it may be helpful in the diagnosis of psoriasis. Conclusion: The differential genes, biological processes and signal pathways of psoriasis, especially the related information and diagnostic efficiency of IFIT3 gene, were obtained by bioinformatics analysis. These results may provide theoretical basis and new direction for exploring the pathogenesis of psoriasis, finding diagnostic markers and developing drug treatment targets.