Bioinformatic analysis to identify and cellular experiments to validate autophagy-related genes in psoriasis

Purpose: To explore differentially expressed genes (DEGs) associated with autophagy in psoriasis using bioinformatics analysis and verify them in an M5-induced psoriatic cell model. Methods: We obtained gene expression microarray data from patients with psoriasis and normal skin tissues from the dataset GSE78097 of the NCBI Gene Expression Omnibus (GEO) database. R software was used to identify DEGs associated with autophagy in psoriasis. Proteinprotein interaction (PPI) and correlation analyses were used to show interactions between certain genes. Their potential biological roles were determined using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Finally, all the DEGs associated with autophagy in psoriasis were validated in a psoriatic cell model by RT-qPCR. Results: 28 DEGs associated with autophagy were identified. These genes were linked to one another, and the most connected hub gene was VEGFA, according to PPI analysis. GO and KEGG enrichment analyses revealed various biological pathways associated with autophagy. The RT-qPCR findings of the expression of 18 genes in the psoriatic cell model confirmed the bioinformatics analysis results. The five genes with the most significant differences were IL24, CCL2, NAMPT, PPP1R15A, and SPHK1. Conclusion: We identified DEGs associated with autophagy in patients with psoriasis. IL24, CCL2, NAMPT, PPP1R15A, and SPHK1 were identified as important genes that may influence psoriasis development through the regulation of autophagy.