Bioinformatic Analysis of the Potential Common Pathogenic Mechanisms for Psoriasis and Metabolic Syndrome

Abstract

The pathogeneses of psoriasis and metabolic syndrome are closely related; however, the underlying biological mechanisms are yet to be clarified. A psoriasis training set was downloaded from the Gene Expression Omnibus database and analyzed to identify the differentially expressed genes (|logFC|> 1 and adjust P < 0.05). Differentially expressed genes for metabolic syndrome were obtained from the GeneCards, Online Mendelian Inheritance in Man, and DisGeNET databases, and crosstalk genes were obtained for multiple enrichment analysis after identifying the disease intersection. Characteristic crosstalk genes were screened using the least absolute shrinkage and selection operator regression model and random forest tree model, and the genes with area under the receiver operating characteristic curve > 0.7 were selected for validation by the two validation sets. Differential analyses of immune cell infiltration were performed on psoriasis lesion and control samples using the CIBERSORT and ImmuCellAI methods, and correlation analyses were performed between the screened signature crosstalk genes and immune cell infiltration. Significant crosstalk genes were analyzed based on the psoriasis area and severity index and on the responses to biological agents. We found five signature genes (NLRX1, KYNU, ABCC1, BTC, and SERPINB4) were screened based on two machine learning algorithms, and NLRX1 was validated. The infiltration of multiple immune cells in psoriatic lesions and non-lesions was associated with NLRX1 expression. NLRX1 was found to be associated with psoriasis severity and response rate after the use of biologics. NLRX1 could be a significant crosstalk gene for psoriasis and metabolic syndrome.

Keywords: Immune cell infiltration; Machine learning; Metabolic syndrome; Psoriasis.