A host-based two-gene model for the identification of bacterial infection in general clinical settings. (April 2021)

• Record Type: Journal Article
• Authors: Lei, Hongxing; Xu, Xiaoyue; Wang, Chi; Xue, Dandan; Wang, Chengbin; Chen, Jiankui
• Is Part Of: International journal of infectious diseases. Volume 105(2021)
• Page Start: 662
• Page End: 667
• Publication Date: 2021-04
• Subjects: Bacterial infection -- Host transcriptional response -- Diagnosis -- PCT -- CRP -- Gene model; Communicable diseases -- Periodicals; Communicable Diseases -- Periodicals; Communicable diseases; Periodicals; Electronic journals; 616.9
• DOI: 10.1016/j.ijid.2021.02.112
• Languages: English
• ISSNs: 1201-9712

Abstract

Highlights: Candidate genes for bacterial infection derived from public transcriptome data. Validated with real-time quantitative polymerase chain reaction on 993 blood samples. Two genes ( S100A12 and CD177 ) selected to build a simple model for diagnosis. Model more sensitive than established procalcitonin and C-reactive protein cutoffs. Abstract: Objectives: In this study, we aimed to develop a simple gene model to identify bacterial infection, which can be implemented in general clinical settings. Methods: We used a clinically availablereal-time quantitative polymerase chain reaction platform to conduct focused gene expression assays on clinical blood samples. Samples were collected from 2 tertiary hospitals. Results: We found that the 8 candidate genes for bacterial infection were significantly dysregulated in bacterial infection and displayed good performance in group classification, whereas the 2 genes for viral infection displayed poor performance. A two-gene model ( S100A12 and CD177 ) displayed 93.0% sensitivity and 93.7% specificity in the modeling stage. In the independent validation stage, 87.8% sensitivity and 96.6% specificity were achieved in one set of case-control groups, and 93.6% sensitivity and 97.1% specificity in another set. Conclusions: We have validated the signature genes for bacterial infection and developed a two-gene model to identify bacterial infection in general clinical settings.