Identification of diagnostic markers and immune cell infiltration characteristics in antineutrophil cytoplasmic antibody-associated vasculitis by weighted gene co-expression network analysis.

Mengdi Xia, Fen Zhao, Yongji Zhang, Zhihuang Zheng, Yun Zhou, Tong Liu

European journal of medical research 2022 Mar 05

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Antineutrophil cytoplasmic antibody-associated vasculitis (AAV) is a group of life-threatening systemic autoimmune diseases. The aim of this study was to determine the relationship between the AAV hub gene and immune cell infiltration, and its value for clinical disease treatment. We downloaded the microarray information of 37 AAV patients and 27 controls from Gene Expression Omnibus (GEO). Genes were classified into totally different modules exploitation weighted gene co-expression network analysis (WGCNA). AAV diagnostic indicators were screened and then assessed immune cell infiltration by the least absolute shrinkage and selection operator (LASSO) and CIBERSORT. Finally, Connectivity Map analysis was applied to predict possible AAV glomerulus injury improvement therapies. WGCNA was developed and differentially expressed genes were classified into 6 modules, the black module was most tightly correlated to AAV. Among them, TIMP1 and FCER1G were most closely related to clinical features. Resting mast cells and monocytes emerged as having the foremost distinguished variations in AAV. C3AR1 and FCER1G were involved in AAV development by immune regulation. Connectivity Map analysis indicated the most significant compound was fisetin. The present study is that the initial to spot immune cell infiltration with microarray data of glomeruli in AAV, which provides novel proof and clues for additional analysis of the molecular mechanisms. © 2022. The Author(s).

Citation

Mengdi Xia, Fen Zhao, Yongji Zhang, Zhihuang Zheng, Yun Zhou, Tong Liu. Identification of diagnostic markers and immune cell infiltration characteristics in antineutrophil cytoplasmic antibody-associated vasculitis by weighted gene co-expression network analysis. European journal of medical research. 2022 Mar 05;27(1):37