DNA methylation (DNAm) plays an important role in the pathogenesis of
psoriasis through regulating
mRNA expressions. This study aimed to identify hub genes regulated by DNAm as
biomarkers of
psoriasis. Psoriatic skin tissues gene expression and methylation datasets were downloaded from Gene Expression Omnibus (GEO) database. Subsequently, multiple computational approaches, including immune infiltration analysis, enrichment analysis,
protein-
protein interaction (PPI) network establishment, and machine learning algorithm analysis (lasso, random forest, and SVM-RFE), were performed to analyze the regulatory networks, to recognize hub genes, and to clarify the pathogenesis of
psoriasis. Finally, the hypermethylated genes were used to immune cell infiltration analysis, which revealed that
psoriasis skin tissues were mainly composed of activated dendritic cells, resting mast cells, T follicular helper cells (cTfh), etc. Differentially expressed-methylated genes (DEMGs) were identified and partitioned into four subgroups and the 97 significantly hypermethylated and downregulated (hyper-down) genes accounted for the highest proportion (47%). Hyper-down genes were mainly enriched in
glucose homeostasis,
AMP-activated protein kinase (AMPK) signaling pathway,
lipid storage disease, partial
lipodystrophy, and
insulin resistance. Furthermore,
insulin receptor substrate 1 (IRS1), Rho
guanine nucleotide exchange factor 10 (ARHGEF10) and
retinoic acid induced 14 (RAI14) were identified as potential targets. These findings provided new ideas for future studies of
psoriasis on the occurrence and the molecular mechanisms.