Exploration of biomarkers of psoriasis through combined multiomics analysis

Abstract

Background : Aberrant DNA methylation patterns are of increasing interest in the study of psoriasis mechanisms. This study aims to screen potential diagnostic indicators affected by DNA methylation for psoriasis based on bioinformatics using multiple machine learning algorithms and to preliminarily explore its molecular mechanisms. Methods : GSE13355, GSE14905, and GSE73894 were collected from the gene expression omnibus (GEO) database. Differentially expressed genes (DEGs) and differentially methylated region (DMR)-genes between psoriasis and control samples were combined to obtain differentially expressed methylated genes. Subsequently, protein-protein interaction (PPI) network establishment, multiple machine learning algorithm analysis, including, the least absolute shrinkage and selection operator (LASSO), Random Forest (RF), and Support Vector Machine (SVM), receiver operating characteristic (ROC) curve analysis, and single-gene gene set enrichment analysis (GSEA) were performed to analyze the interaction networks, to recognize hub genes, and to clarify the pathogenesis of psoriasis. The druggable genes were predicted using DGIdb. The expression of GJB2 in psoriasis lesions and healthy controls was detected by immunohistochemistry (IHC) and quantitative real-time PCR(RT-qPCR). Results: In this study, a total of 767 DEGs and 896 DMR-genes were obtained. Functional enrichment showed that they were significantly associated with skin development, skin barrier function, immune/inflammatory response, and cell cycle. The combined transcriptomic and DNA methylation data resulted in 33 differentially expressed methylated genes, of which The gap junction beta 2 (GJB2) was the final identified hub gene for psoriasis, with the robust diagnostic power. IHC and RT-qPCR showed that GJB2 was significant higher in psoriasis than that in healthy controls. Single gene GSEA suggested that GJB2 may be involved in the development and progression of psoriasis by disrupting the body's immune system, mediating the cell cycle, and destroying the skin barrier, in addition to possibly inducing diseases related to the skeletal aspects of psoriasis. Moreover, OCTANOL and CARBENOXOLONE were identified as promising compounds through the DGIdb database. Conclusion: Our findings suggest that the abnormal expression of GJB2 may play a critical role in psoriasis development and progression. The genes identified in our study may serve as a diagnostic indicator and therapeutic target in psoriasis.

Exploration of Biomarkers of Psoriasis through Combined Multiomics Analysis

“…At the same time, we conducted a single-gene GSEA on GJB2, and the result suggested that it might be involved in the development and progression of psoriasis by disrupting the body's immune system, mediating the cell cycle, and destroying the skin barrier. Finally, the expression and methylation level of GJB2 in psoriasis were verified by the external dataset and qRCR [43]. Gap junction beta 2 (GJB2) gene is located in 13q11-12 region, with a total length of 5.5 kb, encoding gap junction protein connexin 26 (Cx26) [44].…”

Omics and Multi-Omics Analysis for the Early Identification and Improved Outcome of Patients with Psoriatic Arthritis

“…In addition to feature selection, class imbalances are common challenges in multi-omics analysis, but can be solved by sampling or cost-sensitive learning [ 122 ]. Commonly used algorithms such as random forests, support vector machines and the k-nearest neighbor algorithm can provide insight into the underlying structures of datasets and can be developed into powerful models for the support of clinical decision making [ 123 , 124 , 125 ]. In order to develop further hypotheses and integrate data with the literature, pathway analysis can embed the data in a broader context [ 126 ].…”