Psoriasis Metabolism-Immune Regulation Network Analysis Based on Blood and Skin Tissue Transcriptome Data

To identify pivotal genes involved in the metabolic-immune crosstalk of psoriasis and evaluate their utility as biomarkers for diagnosis and targeted therapy.

A bioinformatics-based experimental study. Place and Duration of the Study: Department of Immunology, School of Medicine, Jianghan University, Wuhan, China, from September 2024 to October 2025.

Transcriptomic data from peripheral blood neutrophils (GSE106087) and skin lesions (GSE30999) were integrated. Differentially expressed genes were screened, followed by protein-protein interaction (PPI) network analysis and LASSO regression. Validation employed ROC analysis, Gene Set Enrichment Analysis (GSEA), immune infiltration assessment, qRT-PCR in an LPS-induced HaCaT model, and single-cell RNA sequencing.

CD36, IL1B, and CREB1 were identified as core regulatory factors. GSEA linked CD36 to tricarboxylic acid cycle impairment, and CREB1 to altered potassium ion transport and energy metabolism. qRT-PCR confirmed that all three genes were upregulated in vitro. Single-cell analysis showed that CD36 and IL1B expression in various cell types in psoriatic skin lesions was increased. Immune infiltration analysis showed that CD36 expression in patients with psoriasis was strongly negatively correlated with the abundance of neutrophils, whereas the abundances of CD8+ T cells and mast cells were increased.

This study outlines a psoriasis-specific metabolic-immune imbalance network with CD36, IL1B, and CREB1 as the core. Multidimensional validation supports their potential for patient stratification and targeted treatment and provides a basis for future drug development.

Psoriasis, CD36, CREB1, IL1B, LASSO regression model, Gene expression profiling, Single-cell gene expression analysis, Biomarker, Metabolic-immune network.