Identification of psoriasis‐associated immune marker G3BP2 through single‐cell RNA sequencing and meta analysis

Abstract

AbstractPsoriasis is a chronic skin disease with an increasing prevalence each year. However, the mechanisms underlying its onset and progression remain unclear, and effective therapeutic targets are lacking. Therefore, we employs an innovative approach by combining single‐cell RNA sequencing (scRNA‐seq) with meta‐analysis. This not only elucidates the potential mechanisms of psoriasis at the cellular level but also identifies immunoregulatory marker genes that play a statistically significant role in driving psoriasis progression through comprehensive analysis of multiple datasets. Skin tissue samples from 12 psoriasis patients underwent scRNA‐seq, followed by quality control, filtering, PCA dimensionality reduction, and tSNE clustering analysis to identify T cell subtypes and differentially expressed genes (DEGs) in psoriatic skin tissue. Next, three psoriasis datasets were standardised and merged to identify differentially expressed genes (DEGs). Subsequently, weighted gene co‐expression network analysis (WGCNA) was applied for clustering analysis of gene co‐expression network modules and to assess the correlation between these modules and DEGs. Least absolute shrinkage and selection operator (LASSO) regression and receiver operating characteristic (ROC) curve analyses were conducted to select disease‐specific genes and evaluate their diagnostic value. Single‐cell data revealed nine cell types in psoriatic skin tissue, with seven T cell subtypes identified. Intersection analysis identified ADAM8 and G3BP2 as key genes. Through the integration of scRNA‐seq and Meta analysis, we identified the immunoregulatory marker gene G3BP2, which is associated with the onset and progression of psoriasis and holds clinical significance. G3BP2 is speculated to promote the development of psoriasis by increasing the proportion of CD8+ T cells.