Unraveling the roles of IFIT3 gene and immune-metabolic pathways in psoriasis: a bioinformatics exploration for diagnostic markers and therapeutic targets - Chen, Guangshan; Chen, Xi; Duan, Xingwu; Zhang, Runtian; Bai, Chunxiao (Frontiers in Molecular Biosciences, 2024)
Background The functions and related signal pathways of the IFIT3 gene in the skin lesions of patients with psoriasis were explored through bioinformatics methods to determine the potential specific molecular markers of psoriasis. Methods The “limma” R package was used to analyze three datasets from the Gene Expression Omnibus database (GSE13355, GSE30999 and GSE106992), and the differential genes were screened. The STRING database was used for gene ontology (GO) enrichment analysis, Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis, and protein–protein interaction network integration. Then, the IFIT3 subnetwork was extracted and analyzed by gene set enrichment analysis (GSEA) using the Metascape database to verify the effectiveness of gene differentiation and disease tissue identification. Results In this study, 426 differential genes were obtained, of which 322 were significantly upregulated and 104 were significantly downregulated. GO enrichment analysis showed that the differential genes were mainly involved in immunity and metabolism; the KEGG pathway enrichment analysis mainly included the chemokine signal pathway, PPAR signal pathway, and IL-17 signal pathway, among others. Based on the IFIT3 subnetwork analysis, it was found that IFIT3 was mainly involved in the biological processes of viruses, bacteria, and other microorganisms. The pathways obtained by GSEA were mainly related to immunity, metabolism, and antiviral activities. IFIT3 was highly expressed in psoriatic lesions and may thus be helpful in the diagnosis of psoriasis. Conclusion The differential genes, biological processes, and signal pathways of psoriasis, especially information related to and diagnostic efficiency of the IFIT3 gene, were obtained by bioinformatics analysis. These results are expected to provide the theoretical basis and new directions for exploring the pathogenesis of psoriasis, in addition to helping with finding diagnostic markers and developing drug treatment targets.