Abstract
Respiratory syncytial virus (RSV) is the most common pathogen causing acute lower respiratory tract infection in infants and children. Due to limited knowledge of the pathological and molecular mechanisms of immunodeficiency underlying RSV disease, there is currently a lack of an approved and effective RSV vaccine to combat RSV infections. This study aimed to identify genes associated with immune dysfunction using bioinformatics methods to gain insights into the role of dysregulated immune genes in RSV disease progression, and to predict potential therapeutic drugs by targeting dysregulated immune-related genes. 423 immune-related differential genes (DEIRGs) were filtered from the blood samples of 87 healthy individuals and 170 RSV patients. According to CIBERSORT analysis, the blood of RSV patients showed increased infiltration of various immune cells. Subsequently, ten immune-related hub genes were screened via Protein–Protein Interaction Networks. Six signature immune-related genes (RPS2, RPS5, RPS13, RPS14, RPS18, and RPS4X) as candidate characteristic genes for the diagnostic model were identified by Lasso regression. The AUC value of the ROC curve of the six signature genes was 0.884. This result, intriguingly, suggested that all six immune-related genes with a good internal validation effect were ribosome family genes. Finally, through molecular docking analyses targeting these differential immune genes, ADO and fluperlapine were found to have high stable binding to major proteins of important immune-related genes in nine drug-protein interactions. Overall, the present study screened immune-related genes that are dysregulated in the development of RSV disease to investigate the pathogenesis of RSV infection from the standpoint of immune disorders. Unexpectedly, bioinformatics analysis revealed that ribosome family genes may be involved in the immune dysregulation of RSV disease, and these genes as targets formed the basis for potential drug modification candidates in RSV disease.
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Data Availability
All datasets provided in this study can be found in the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/).
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This work was supported by the Guangxi Natural Science Foundation (Grant No. 2020GXNSFDA238003).
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LL conceived and designed the study, analyzed the data, wrote the paper; ZL prepared the figures and/or tables; CL conceived and designed the study and reviewed the draft paper. All authors read and approved the final manuscript.
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Lin, L., Liao, Z. & Li, C. Exploring Ribosomal Genes as Potential Biomarkers of the Immune Microenvironment in Respiratory Syncytial Virus Infection. Biochem Genet (2024). https://doi.org/10.1007/s10528-024-10778-6
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DOI: https://doi.org/10.1007/s10528-024-10778-6