Introduction:

Autophagy is necessary for the progression of psoriasis.

Aim:

This study aimed to recognize possible autophagy-related genes in psoriasis via bioinformatics study to present a better standard for the clinical treatment and management of psoriasis.

Material and methods:

The GEO dataset was utilized to derive the mRNA expression profile of the database GSE78097. R software was utilized to find autophagy-associated genes that may be expressed in psoriasis. Then, a protein-protein interaction (PPI) correlation study of the differentially expressed autophagy-associated genes was carried out, and GO and KEGG enrichment analysis was used to investigate any potential signalling pathways linked.

Results:

We identified a total of 156 autophagy-related genes in 27 psoriasis and 6 healthy skin tissue samples. The PPI network diagram findings demonstrate interactions among these autophagy-associated genes. Autophagy, protein processing, apoptosis, and mitochondria processes may be crucial in the development and occurrence of psoriasis suggested by KEGG and GO enrichment analysis.

Conclusions:

Utilizing bioinformatics methods to recognize differentially expressed autophagy-related genes has further enhanced our understanding of psoriasis and provided new thinking for the study of the pathogenesis and therapeutic targets of psoriasis.