Integrated bioinformatic analysis of key biomarkers and signalling pathways in psoriasis.

Abstract

Background and aims: Psoriasis is a relatively common autoimmune inflammatory skin disease with a chronic etiology. Since psoriasis is still incurable, it is necessary to identify the molecular mechanisms of psoriasis. The present study was designed to detect novel biomarkers and pathways associated with psoriasis incidence, and provide new insights into treatment of psoriasis.

Methods and results: 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 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 signalling 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.

Conclusion: The findings of the present integrated bioinformatics may enhance our understanding of the molecular events occurring in psoriasis, and these candidate genes and pathways together may prove to be therapeutic targets for psoriasis.