Gene expression profiling-based identification of different influenza patient molecular subgroups

Abstract Background Influenza infections routinely circulate throughout the world, resulting in extensive patient morbidity and mortality. Even so, the molecular mechanisms underlying patient responses to influenza remain poorly understood. The present study was thus developed with the goal of classifying influenza patients into different molecular subgroups based on gene expression profiling data and assessing the relationship between these subgroups and disease severity. Methods The GSE101702 and GSE21802 microarray datasets were downloaded from the Gene Expression Omnibus, and the influenza cases in these datasets were classified via consensus clustering into differential gene expression profile-based subgroups. Transcriptomic differences among these subgroups were assessed via weighted gene co-expression network analysis (WGCNA), and enrichment analyses for individual WGCNA modules were then performed. Results In total, 143 influenza patients were separated into two distinct molecular subgroups through a consensus clustering approach. Patients in subgroup II exhibited increased influenza disease severity independent of age. In total, 8 subgroup-specific WCGNA modules were defined, and functional enrichment analyses revealed that relative to patients in subgroup I, those in subgroup II exhibited significant upregulation of immune response and inflammation-related pathways together with ribosome biogenesis pathway downregulation. Conclusions The transcriptomic classification of influenza patients performed herein was linked to disease severity, offering insight into the molecular pathogenesis of severe influenza infections.