Long Noncoding RNA and Predictive Model To Improve Diagnosis of Clinically Diagnosed Pulmonary Tuberculosis.

Autor:	Hu X; Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, People's Republic of China.; Division of Laboratory Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, People's Republic of China., Liao S; Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.; Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada., Bai H; Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, People's Republic of China., Gupta S; Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.; The Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada., Zhou Y; Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, People's Republic of China., Zhou J; Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, People's Republic of China., Jiao L; Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, People's Republic of China., Wu L; Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, People's Republic of China., Wang M; Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, People's Republic of China., Chen X; Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, People's Republic of China., Zhou Y; Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, People's Republic of China., Lu X; Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, People's Republic of China., Hu TY; Center for Cellular and Molecular Diagnostics, Department of Biochemistry and Molecular Biology, School of Medicine, Tulane University, New Orleans, Louisiana, USA., Zhang Z; Department of Computer Science, University of Toronto, Toronto, Ontario, Canada zhaolei.zhang@utoronto.ca docbwy@126.com.; Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.; The Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada., Ying B; Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, People's Republic of China zhaolei.zhang@utoronto.ca docbwy@126.com.
Jazyk:	angličtina
Zdroj:	Journal of clinical microbiology [J Clin Microbiol] 2020 Jun 24; Vol. 58 (7). Date of Electronic Publication: 2020 Jun 24 (Print Publication: 2020).
DOI:	10.1128/JCM.01973-19
Abstrakt:	Clinically diagnosed pulmonary tuberculosis (PTB) patients lack microbiological evidence of Mycobacterium tuberculosis , and misdiagnosis or delayed diagnosis often occurs as a consequence. We investigated the potential of long noncoding RNAs (lncRNAs) and corresponding predictive models to diagnose these patients. We enrolled 1,764 subjects, including clinically diagnosed PTB patients, microbiologically confirmed PTB cases, non-TB disease controls, and healthy controls, in three cohorts (screening, selection, and validation). Candidate lncRNAs differentially expressed in blood samples of the PTB and healthy control groups were identified by microarray and reverse transcription-quantitative PCR (qRT-PCR) in the screening cohort. Logistic regression models were developed using lncRNAs and/or electronic health records (EHRs) from clinically diagnosed PTB patients and non-TB disease controls in the selection cohort. These models were evaluated by area under the concentration-time curve (AUC) and decision curve analyses, and the optimal model was presented as a Web-based nomogram, which was evaluated in the validation cohort. Three differentially expressed lncRNAs ( ENST00000497872 , n333737 , and n335265 ) were identified. The optimal model (i.e., nomogram) incorporated these three lncRNAs and six EHRs (age, hemoglobin, weight loss, low-grade fever, calcification detected by computed tomography [CT calcification], and interferon gamma release assay for tuberculosis [TB-IGRA]). The nomogram showed an AUC of 0.89, a sensitivity of 0.86, and a specificity of 0.82 in differentiating clinically diagnosed PTB cases from non-TB disease controls of the validation cohort, which demonstrated better discrimination and clinical net benefit than the EHR model. The nomogram also had a discriminative power (AUC, 0.90; sensitivity, 0.85; specificity, 0.81) in identifying microbiologically confirmed PTB patients. lncRNAs and the user-friendly nomogram could facilitate the early identification of PTB cases among suspected patients with negative M. tuberculosis microbiological evidence. (Copyright © 2020 Hu et al.)
Databáze:	MEDLINE
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