Single-Cell Sequencing Combined with Transcriptome Sequencing to Explore the Molecular Mechanisms Related to Psoriasis

Cailing E,1,* Rongying Wang,1,* Zudong Meng,1 Yulin Zou1,2 1Department of Dermatology, Renmin Hospital, Hubei University of Medicine, Shiyan, People’s Republic of China; 2Department of Dermatology, Venereology and Allergology, University Medical Center Göttingen, Göttingen, Germany*These authors contributed equally to this workCorrespondence: Yulin Zou, Email [email protected]: Psoriasis, a chronic and recurrent inflammatory skin disease, current treatments can only alleviate its symptoms. There is still no complete cure. Although increasing research supports the therapeutics to be better, the common mechanism of its occurrence is still not fully elucidated. Our study is about further explore the molecular mechanism of the occurrence of this disease.Methods: The gene expression profiles of psoriasis (GSE151177, GSE41664, GSE30999) were downloaded from the Gene Expression Omnibus (GEO) database. After identifying the common differentially expressed genes (DEGs) of psoriasis using R software, three kinds of analyses were performed, namely WGCNA, GWAS Analysis, Drug Target Prediction.Results: A total of 14 common DEGs was selected for subsequent analyses. Our Drug Target Prediction analysis revealed that the expression profiles influenced by certain drugs, including methotrexate, budesonide, amino purvalanol-a, and selumetinib, exhibited negative correlations with the disease-perturbed expression profiles. Finally, It was found that S100A4, JAML, TRAF3IP3, MIAT, IL7R, and KLRB1 were prominently expressed in the immune pathway related to allograft rejection. In the metabolic pathway, oxidative phosphorylation showed high expression levels, while the reactive oxygen species pathway was notably expressed in the signaling pathways domain.Conclusion: Our study reveals the potential drugs and pathogenesis of psoriasis. These potential pathway and hub genes may provide new ideas for further mechanism research.Keywords: psoriasis, hub genes, bioinformatics, PPI, inflammation