Integrated Bioinformatic Analysis of Key Biomarkers and Signaling Pathways in Psoriasis

Abstract Background: Psoriasis is a relatively common autoimmune inflammatory skin disease with a chronic etiology. The present study was designed to detect novel biomarkers and pathways associated with psoriasis incidence. Methods: Differentially expressed genes (DEGs) associated with psoriasis in the Gene Expression Omnibus (GEO) database were identified, and their functional roles and interactions were then annotated and evaluated through GO, KEGG, and gene set variation (GSVA) analyses. In addition, the STRING database was leveraged to construct a protein-protein interaction (PPI) network, and key hub genes from this network were validated as being relevant through receiver operating characteristic (ROC) curve analyses of three additional GEO datasets. The CIBERSORT database was additionally used to assess the relationship between these gene expression-related findings and immune cell infiltration. Results: In total 197 psoriasis-related DEGs were identified and found to primarily be associated with the NOD-like receptor, IL-17, and cytokine-cytokine receptor interaction signaling pathways. GSVA revealed significant differences between normal and lesional groups (P < 0.05), while PPI network analyses identified CXCL10 as the hub gene with the highest degree value, whereas IRF7, IFIT3, OAS1, GBP1, and ISG15 were promising candidate genes for the therapeutic treatment of psoriasis. ROC analyses confirmed that these 6 hub genes exhibited good diagnostic efficacy (AUC > 70%), and were predicted to be associated with increased sensitivity to 10 drugs (P < 0.01). The CIBERSORT database further predicted that these hub genes were associated with infiltration by 22 different immune cell types. Conclusion: These results offer a robust foundation for future studies of the molecular basis for psoriasis, potentially guiding efforts to treat this common and disruptive disease.