Then CIBERSORT algorithm was used to calculate the immune cell compositions, followed by the WGCNA analysis to identify the module genes associated with immune cell infiltration.  Results: The CIBERSORT algorithm showed that neutrophil expression was significantly higher in IVIG-resistant patients compared to IVIG-responsive patients. 
CIBERSORT
Then CIBERSORT algorithm was used to calculate the immune cell compositions, followed by the WGCNA analysis to identify the module genes associated with immune cell infiltration.  Next, we got differentially expressed neutrophil-related genes by intersecting DEIGs with neutrophil-related module genes obtained by WGCNA, for further analysis. 
WGCNA
Methods: In this study, we downloaded the expression profiles from the GSE48498 and GSE16797 datasets in the Gene Expression Omnibus database, analyzed differentially expressed genes (DEGs), and intersected the DEGs with the immune-related genes downloaded from the ImmPort database to obtain differentially expressed immune-related genes (DEIGs). 
Gene Expression Omnibus database
Methods: In this study, we downloaded the expression profiles from the GSE48498 and GSE16797 datasets in the Gene Expression Omnibus database, analyzed differentially expressed genes (DEGs), and intersected the DEGs with the immune-related genes downloaded from the ImmPort database to obtain differentially expressed immune-related genes (DEIGs). 
ImmPort database
Then we combined the PPI network in the STRING database with the MCODE plugin in Cytoscape and identified 6 hub genes (TLR8, AQP9, CXCR1, FPR2, HCK, and IL1R2), which had good diagnostic performance in IVIG resistance according to ROC analysis. 
STRING database
Then we combined the PPI network in the STRING database with the MCODE plugin in Cytoscape and identified 6 hub genes (TLR8, AQP9, CXCR1, FPR2, HCK, and IL1R2), which had good diagnostic performance in IVIG resistance according to ROC analysis. 
Cytoscape