Pathogenesis of psoriasis complicated with atherosclerosis: a bioinformatics analysis based on transcriptomic data

Objective To identify comorbid hub genes in psoriasis and atherosclerosis. Methods Transcriptomic datasets of three psoriatic samples and three atherosclerosis samples were downloaded from the GEO database. A deep learning algorithm (Batch Normalization) was utilized to merge and batch-correct the datasets of the two diseases. The limma package was employed to intersect the differentially expressed genes in the lesions and normal tissues of both diseases. The protein-protein interaction network was constructed using the STRING database and CytoHubba plugin to identify the hub genes. Results Intersection analysis revealed 132 up-regulated genes and 114 down-regulated genes in the lesions of these two diseases. Construction of the interaction network identified 10 hub genes: MX1, OAS1, OAS2, OASL, IFIT1, RSAD2, CXCL10, IFIT3, XAF1 and IL1B, among which the first six were enriched in the type Ⅰ interferon signaling pathway. Two external validation sets independently verified the expression of CXCL10. Conclusions