A host-based two-gene model for the identification of bacterial infection in general clinical settings

Abstract Objectives In this study, we aimed at developing a simple gene model for the identification of bacterial infection which can be implemented in general clinical settings. Methods We used a clinically available RT-qPCR platform to conduct focused gene expression assays on clinical blood samples. Samples were collected from two tertiary hospitals. Results We found that the eight candidate genes for bacterial infection were significantly dys-regulated in bacterial infection and displayed good performance in group classification, whereas the two genes for viral infection displayed poor performance. A two-gene model (S100A12 and CD177) displayed sensitivity of 93.0% and specificity of 93.7% in the modeling stage. In the independent validation stage, the sensitivity of 87.8% and specificity of 96.6% was achieved in one set of case-control groups, and the sensitivity and specificity were 93.6% and 97.1%, respectively, in another set of case-control groups. Conclusions Thus, we have validated the signature genes for bacterial infection and developed a two-gene model for the identification of bacterial infection in general clinical settings.