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.

Computational Analysis of the Global Effects of Ly6E in the Immune Response to Coronavirus Infection Using Gene Networks

“…Along these lines, GCNs are also considered suitable for the study of infectious diseases, as those caused by viruses to the matter at hand [ 38 ]. To do so, multiple studies have analyzed the effects of viral infection over the organism, focusing on immune response or tissue damage [ 39 , 40 ].…”

“…Using several network-based approaches on multiple knockout mouse strains, authors found that ranking genes based on their network topology made accurate predictions of the pathogenic state, thus solving a classification problem. In [ 39 ], co-expression networks were generated by microarray analysis of pediatric influenza-infected samples. Thanks to this study, genes involved in the innate immune system and defense to virus were revealed.…”

The Possible Role of MOPr-DOPr Heteromers and Its Regulatory Protein RTP4 at Sensory Neurons in Relation to Pain Perception

“…Although RTP4 was initially characterized as a GPCR chaperone protein as described above ( Saito et al, 2004 ; Behrens et al, 2006 ; Mainland and Matsunami, 2012 ), more recent publications clearly revealed its significant role in regulation of immune responses such as IFN-related antiviral immunity ( Schoggins et al, 2011 ; Nair et al, 2017 ; Dang et al, 2018 ; Zarei Ghobadi et al, 2019 ; He et al, 2020 ). Thus, it is suggested that RTP4 could be induced by type I IFNs and work as a negative regulator of interferons pathways.…”

Identifying potential drug targets and candidate drugs for COVID-19: biological networks and structural modeling approaches

“…Coronavirus and influenza are pathogens causing severe respiratory illness to humans 14 . Recent reports demonstrated that SP110, HERC5, SAMD9L, RTP4, ESPT11 genes were identified to control and improve the immune system and defense mechanism against influenza by network analysis 15 . It has the supremacy to act as biomarkers and targets for pediatric influenza mediated therapy.…”