Identification of potential immune/diagnosis related gene-immunocyte subtype networks in extracellular immune response to respiratory syncytial virus infection

• Published in: Virus research Vol. 321; p. 198906
• Main Authors: Wang, Baohong, Liu, Hongbo
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier 01.11.2022
• Subjects: Arachidonic Acid; Chemokines; Child; Computational Biology; Cytokines - genetics; Diagnose; Gene Expression Profiling; Gene Regulatory Networks; Humans; Immunity; Machine learning; Receptors, Cytokine; Respiratory syncytial virus; Respiratory Syncytial Virus Infections - diagnosis; Respiratory Syncytial Virus Infections - genetics; Respiratory Syncytial Virus, Human - genetics; Therapeutic targets; Weighted gene co-expression network analysis; Respiratory syncytial virus; Diagnose; Immunity; Machine learning; Therapeutic targets; Weighted gene co-expression network analysis
• ISSN: 0168-1702; 1872-7492
• DOI: 10.1016/j.virusres.2022.198906

Respiratory syncytial virus (RSV) is one of the important pathogenic agents of pediatric respiratory tract infection. Weighted gene co-expression network analysis (WGCNA) is used to study autoimmune diseases, which can find potential hub genes. The diagnostic model based on hub genes and machine learning makes it possible to diagnose the extracellular immune response to RSV infection early. The aim of the present study was to identify potential immune, diagnose and treatment related genes expressed in RSV-infected cells. Firstly, gene expression data were downloaded from the Gene Expression Omnibus (GEO) to identify differentially expressed genes (DEGs). Secondly, WGCNA was performed based on DEGs to obtain hub genes related to immunity score. Thirdly, protein-protein interaction (PPI) and the immune infiltration analysis of hub immune related genes were performed. Finally, diagnostic and immune related genes were identified by machine learning, followed by functional analysis. Totally, 2063 DEGs were identified in the extracellular immune response to RSV infection. Among which, 10 key immune and diagnosis related genes were identified, including ITGA2B, GP9, ITGB3, SELP, PPBP, MPL, CXCL8, NFE2, PTGS1 and LY6G6F. Several immune/diagnosis related gene-immunocyte subtype networks were identified, such as CXCL8-Type 17 T helper cell, LY6G6F-CD56 bright natural killer cell, PPBP-activated CD4 T cell/T follicular helper cell, NFE2/PTGS1/SELP-activated dendritic cell, GP9/ITGA2B/MPL-activated CD8 T cell. ITGB3, MPL and PTGS1 could be considered as therapeutic targets. Some significantly enriched signaling pathways were identified, including hematopoietic cell lineage (involving GP9 and ITGA2B), cytokine-cytokine receptor interaction (involving MPL), chemokine signaling pathway (involving PPBP) and arachidonic acid metabolism (involving PTGS1). The 10-immune related gene signature may be used as potential diagnostic markers for the extracellular immune response to RSV infection, which may provide a new field in searching for diagnostic and therapeutic molecules in the extracellular immune response to RSV infection.