Bioinformatics analyses of comorbid mechanisms between psoriasis and type 2 diabetes mellitus.

Epidemiological association between psoriasis and T2DM suggests shared pathophysiology that are to be explored. Microarray expression profiles for psoriasis and T2DM were obtained from the Gene Expression Omnibus (GEO) database. The "limma" package in R software was used to screen the differentially expressed genes (DEG). GO and KEGG enrichment analysis were further conducted to explore the functions of co-DEGs. By intesecting genes of the key disease-related modules from WGCNA with co-DEGs, candidate co-driver genes were identified and their PPI network was constructed. Hub genes with good diagnostic potential were obtained by ROC analysis and their expression was further compared in validation datasets as well as clinical samples. The crucial co-driver genes, identified by a consistently differential expression pattern, were further subjected to a series of analyses, including Gene Set Enrichment Analysis (GSEA), immune cell infiltration analysis, gene-chemistry networks analysis, gene-transcription factors (TF) network analysis, and gene-miRNA regulatory network analysis. In our study, 71 co-DEGs were identifed from psoriasis and T2DM training datasets. KEGG analysis revealed enrichment of pathways including toll-like receptor signaling pathway, cytokine-cytokine receptor interactions, chemokine signaling pathway, NOD-like receptor signaling pathway and cytosolic DNA-sensing pathway. By intersecting the critical WCGNA modules with co-DEGs, 33 candidate co-driver genes were obtained. 11 of them showed interactions with others on PPI network and 7 revealed good diagnostic value with AUC > 0.7 by ROC analysis. 4 genes, namely BEX5, EPHX2, GPRASP1, and RBP4 were finally identified as crucial co-driver genes with a consistent differential expression pattern in both training and validation datasets as well as validation experiments using clinical samples. GSEA analysis revealed that these crucial co-driver genes were involved in cytokine receptor interaction, proteasome, ribosome, apoptosis and so on. Immune cell infiltration and correlation analyses highlighted their roles in the immune microenvironment. Lastly, these genes targeted 76 skin and metabolic diseases and 135 chemicals were predicted to exert an modulatory effect of their expression. 13 TFs and 79 miRNAs were identified to modulate their expression. The integrated bioinformatics analysis conducted in our study identified co-DEGs and enriched immune-inflammatory pathways, providing novel insights into the pathogenesis underlying the comorbidity of psoriasis and T2DM. The crucial co-driver genes warrants further experimental validation and exploration to unveal the common pathophysiology.