Abstract
Host response biomarkers offer a promising alternative diagnostic solution for identifying acute respiratory infection (ARI) cases involving influenza infection. However, most of the published panels involve multiple genes, which is problematic in clinical settings because polymerase chain reaction (PCR)-based technology is the most widely used genomic technology in these settings, and it can only be used to measure a small number of targets. This study aimed to identify a single-gene biomarker with a high diagnostic accuracy by using integrated bioinformatics analysis with XGBoost. The gene expression profiles in dataset GSE68310 were used to construct a co-expression network using weighted correlation network analysis (WGCNA). Fourteen hub genes related to influenza infection (blue module) that were common to both the co-expression network and the protein–protein interaction network were identified. Thereafter, a single hub gene was selected using XGBoost, with feature selection conducted using recursive feature elimination with cross-validation (RFECV). The identified biomarker was oligoadenylate synthetases-like (OASL). The robustness of this biomarker was further examined using three external datasets. OASL expression profiling triggered by various infections was different enough to discriminate between influenza and non-influenza ARI infections. Thus, this study presented a workflow to identify a single-gene classifier across multiple datasets. Moreover, OASL was revealed as a biomarker that could identify influenza patients from among those with flu-like ARI. OASL has great potential for improving influenza diagnosis accuracy in ARI patients in the clinical setting.
Highlights
Acute respiratory infection (ARI) is responsible for significant levels of morbidity and mortality worldwide related to infectious diseases
Panels with multiple genes are problematic for infection diagnostics, as the most widely used genomic technology in clinical settings is polymerase chain reaction (PCR)-based technologies, which can only be used to assess a handful of targets
An integrated bioinformatics analysis with machine learning was performed in this study to identify a hub gene that was specific to influenza infection
Summary
Acute respiratory infection (ARI) is responsible for significant levels of morbidity and mortality worldwide related to infectious diseases. Viruses and bacteria are the main causes of ARI. Influenza virus kills more people than other viruses. It has been estimated that there were 250,000–500,000 additional deaths during the first 12 months of the global circulation of the 2009 pandemic H1N1 influenza A virus (Dawood et al, 2012). Better diagnostics for ARI (with or without influenza virus) are urgently needed in both inpatient and outpatient settings. OASL Discriminated Influenza Infection discriminating between influenza and non-influenza flu-like illnesses on clinical grounds is often difficult, because these ARIs share similar clinical features (e.g., cough and fever)
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