Screening of potential biomarkers for distinguishing between latent and active tuberculosis in children using bioinformatics analysis. Issue 5 (5th February 2021)

• Record Type: Journal Article
• Authors: Shao, Meng; Wu, Fang; Zhang, Jie; Dong, Jiangtao; Zhang, Hui; Liu, Xiaoling; Liang, Su; Wu, Jiangdong; Zhang, Le; Zhang, Chunjun; Zhang, Wanjiang
• Other Names: Snowden. Jessica section editor.
• Is Part Of: Medicine. Volume 100:Issue 5(2021)
• Publication Date: 2021-02-05
• Subjects: active tuberculosis -- biomarkers -- childhood tuberculosis -- differentially expressed genes -- latent tuberculosis infection; Medicine -- Periodicals; Medicine -- Periodicals; Médecine -- Périodiques; Geneeskunde; Medicine; Periodicals; Periodicals; 610.5
• DOI: 10.1097/MD.0000000000023207
• Languages: English
• ISSNs: 0025-7974

Abstract

Abstract : Abstract: Tuberculosis (TB) is one of the leading causes of childhood morbidity and death globally. Lack of rapid, effective non-sputum diagnosis and prediction methods for TB in children are some of the challenges currently faced. In recent years, blood transcriptional profiling has provided a fresh perspective on the diagnosis and predicting the progression of tuberculosis. Meanwhile, combined with bioinformatics analysis can help to identify the differentially expressed genes (DEGs) and functional pathways involved in the different clinical stages of TB. Therefore, this study investigated potential diagnostic markers for use in distinguishing between latent tuberculosis infection (LTBI) and active TB using children's blood transcriptome data. From the Gene Expression Omnibus database, we downloaded two gene expression profile datasets (GSE39939 and GSE39940) of whole blood-derived RNA sequencing samples, reflecting transcriptional signatures between latent and active tuberculosis in children. GEO2R tool was used to screen for DEGs in LTBI and active TB in children. Database for Annotation, Visualization and Integrated Discovery tools were used to perform Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes pathway analysis. STRING and Cytoscape analyzed the protein-protein interaction network and the top 15 hub genes respectively. Receiver operating characteristics curve was used to estimate the diagnostic value of the hub genes. A total of 265 DEGs were identified, including 79 upregulated and 186 downregulated DEGs. Further, 15 core genes were picked and enrichment analysis revealed that they were highly correlated with neutrophil activation and degranulation, neutrophil-mediated immunity and in defense response. Among them TLR2, FPR2, MMP9, MPO, CEACAM8, ELANE, FCGR1A, SELP, ARG1, GNG10, HP, LCN2, LTF, ADCY3 had significant discriminatory power between LTBI and active TB, with area under the curves of 0.84, 0.84, 0.84, 0.80, 0.87, 0.78, 0.88, 0.84, 0.86, 0.82, 0.85, 0.85, 0.79, and 0.88 respectively. Our research provided several genes with high potential to be candidate gene markers for developing non-sputum diagnostic tools for childhood Tuberculosis. Abstract : Supplemental Digital Content is available in the text