Identifying novel biomarkers of the pediatric influenza infection by weighted co-expression network analysis

Abstract

Background: Despite the high yearly prevalence of Influenza, the pathogenesis mechanism and involved genes have not been fully known. Finding the patterns and mapping the complex interactions between different genes help us to find the possible biomarkers and treatment targets. Methods: Herein, weighted gene co-expression network analysis (WGCNA) was employed to construct a co-expression network among genes identified by microarray analysis of the pediatric influenza-infected samples. Results: Three of the 38 modules were found as the most related modules to influenza infection. At a functional level, we found that the genes in these modules regulate the immune responses, protein targeting, and defense to virus. Moreover, the analysis of differentially expressed genes disclosed 719 DEGs between the normal and infected subjects. The comprehensive investigation of genes in the module involved in immune system and viral defense (yellow module) revealed that SP110, HERC5, SAMD9L, RTP4, C19orf66, HELZ2, EPSTI1, and PHF11 which were also identified as DEGs (except C19orf66) have the potential to be as the biomarkers and also drug targeting for the treatment of pediatric influenza. Conclusions: The WGCN analysis revealed co-expressed genes which were involved in the innate immune system and defense to virus. The differentially expressed genes in the identified modules can be considered for designing drug targets. Moreover, modules can help to find pathogenesis routes in the future.