Abstract
BACKGROUND: Psoriasis is a chronic inflammatory skin disorder with complex molecular mechanisms. While previous studies have demonstrated altered levels of arachidonic acid and its metabolites in psoriatic lesions, the specific roles of arachidonic acid metabolism (AAM) genes in the molecular pathogenesis and immune dysregulation of psoriasis remain poorly understood. This study aimed to investigate the role of AAM genes in the pathogenesis and immune dysregulation of psoriasis using an integrative bioinformatics approach. METHODS: Gene expression data from psoriasis patients and healthy controls were obtained from the Gene Expression Omnibus database and analyzed. Differentially expressed genes were identified, and functional enrichment analyses were performed. Weighted gene co-expression network analysis (WGCNA) and machine learning techniques were employed to identify psoriasis associated AAM genes. Single-sample gene set enrichment analysis (ssGSEA) and immune cell composition analysis were conducted to explore functional implications. Transcription factor prediction analysis was performed to identify potential regulators of key AAM genes. RESULTS: Differential expression analysis revealed 469 dysregulated genes in psoriasis, with functional enrichment highlighting the involvement of epidermis development, immune response, and inflammation. WGCNA and machine learning approaches identified ABCC1, PLA2G3, CYP2J2, and GPX2 as key AAM genes. ssGSEA showed elevated inflammation and immune response in psoriasis, with key AAM genes correlating with specific pathways. Immune cell composition analysis revealed increased infiltration of inflammatory cells in psoriatic skin. Transcription factor prediction analysis identified shared transcription factors for the key AAM genes, suggesting coordinated regulation of their expression in psoriasis. CONCLUSION: This integrative analysis identified key AAM genes associated with psoriasis pathogenesis and immune dysregulation, providing novel insights into the molecular basis of psoriasis. The findings highlight potential therapeutic targets and biomarkers, which could lead to improved diagnosis and treatment strategies for this chronic inflammatory skin disorder.