Background: Rapidly progressive glomerulonephritis (RPGN) is caused by various diseases process, thereby resulting in extensive crescent formation, which could lead to a rapid loss of kidney function. The molecular pathogenesis of RPGN remains largely unknown and requires clarification. The weighted gene co-expression network analysis (WGCNA) is a powerful bioinformatics tool to identify meaningful molecules in diseases. Methods: The dataset of GSE104948, which contains 22 RPGN and 18 normal samples, was obtained from Gene Expression Omnibus database. After data pre-processing, the WGCNA was performed to successfully cluster several significant modules. The most significant module was selected for further Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Visualization of network and screening of hub genes were performed by using Cytoscape software. Results: A total of 11 modules were clustered by WGCNA, and the most significant module-turquoise module was selected. As discovered via GO enrichment and KEGG pathway analysis, the turquoise module was mainly associated with neutrophil activation and degranulation. After visualization and calculation for the network, the PYCARD gene has higher relationship score in 2 clusters, namely, neutrophil activation and degranulation. In accordance with the literature review, the hub gene could be closely related to the inflammation response and could act as the potential therapeutic targets in RPGN. Conclusions: WGCNA in RPGN expression profiling was used for the first time in this paper. A novel hub gene closely associated with RPGN was screened out, thereby providing the brand-new molecular candidate for RPGN.

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