Psoriasis, a chronic and recurring disease, is closely associated with lipid metabolism. This study aims to identify differentially expressed genes (DEGs) and their functional pathways associated with psoriasis to uncover new therapeutic targets. We leveraged Gene Expression Omnibus (GEO) datasets for DEGs analysis in psoriasis. Gene Set Enrichment Analysis (GSEA), Gene Set Variation Analysis (GSVA), Weighted Gene Co-expression Network Analysis (WGCNA), and single-sample GSEA (ssGSEA) were used to investigate the roles of lipid metabolism genes in psoriasis progression and immune alterations. An RBP-mRNA network revealed post-transcriptional regulatory mechanisms. Our findings revealed 3,839 DEGs, including 1,775 upregulated and 2,064 downregulated genes in psoriatic samples compared to controls. Enrichment analysis showed that immune-related pathways such as cytoplasmic DNA sensing pathways and NOD-like receptor signaling pathways were significantly enriched. WGCNA identified modules highly correlated with lipid metabolism and screened out key genes such as AACS, HSD11B1 and GATA6. ROC curve analysis showed that these genes had a high discriminatory ability (AUC > 0.85) within the analyzed dataset, suggesting their potential as novel biomarkers. Immune infiltration analysis revealed significant differences in 27 types of immune cells between patients with psoriasis and the control group. This study provides new clues for the molecular mechanism of psoriasis and potential therapeutic targets.