Affiliation:
1. Institute of Pediatrics, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, No. 9 Jinsui Road, Guangzhou, 510623 Guangdong, China
2. Department of Pediatric Cardiology, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou, 510623 Guangdong, China
3. Department of Medical Biochemistry and Molecular Biology, School of Medicine, Jinan University, Guangzhou, 510632 Guangdong, China
Abstract
Kawasaki disease (KD) is an acute systemic vasculitis of childhood with prolonged fever, and the diagnosis of KD is mainly based on clinical criteria, which is prone to misdiagnosis with other febrile infectious (FI) diseases. Currently, there remain no effective molecular markers for KD diagnosis. In this study, we aimed to use a relative-expression-based method k-TSP and resampling framework to identify robust gene pair signatures to distinguish KD from bacterial and virus febrile infectious diseases. Our study pool consisted of 808 childhood patients from several studies and assigned to three groups, namely, the discovery set (n=224), validation set-1 (n=197), and validation set-2 (n=387). We had identified 60 biologically relevant gene pairs and developed a top-ranked gene pair classifier (TRGP) using the first seven signatures, with the area under the receiver-operating characteristic curves (AUROC) of 0.947 (95% CI, 0.918-0.976), a sensitivity of 0.936 (95% CI, 0.872-0.987), and a specificity of 0.774 (95% CI, 0.705-0.836) in the discovery set. In the validation set-1, the TRGP classifier distinguished KD from FI with AUROC of 0.955 (95% CI, 0.919-0.991), a sensitivity of 0.959 (95% CI, 0.925-0.986), and a specificity of 0.863 (95% CI, 0.764-0.961). In the validation set-2, the predictive performance of classification was with an AUROC of 0.796 (95% CI, 0.747-0.845), a sensitivity of 0.797 (95% CI, 0.720-0.864), and a specificity of 0.661 (95% CI, 0.606-0.717). Our study reveals that gene pair signatures are robust across diverse studies and can be utilized as objective biomarkers to distinguish KD from FI, helping to develop a fast, simple, and effective molecular approach to improve the diagnosis of KD.
Funder
Guangzhou Institute of Pediatrics/Guangzhou Women and Children’s Medical Center
Subject
General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,General Medicine
Cited by
2 articles.
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1. Improvements in Diagnosing Kawasaki Disease using Machine Learning Algorithms;2024 4th International Conference on Pervasive Computing and Social Networking (ICPCSN);2024-05-03
2. Host Blood Gene Signatures Can Detect the Progression to Severe and Cerebral Malaria;Frontiers in Cellular and Infection Microbiology;2021-10-22