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

Author:

Hu Xuejiao12,Liao Shun34,Bai Hao1,Gupta Shubham45,Zhou Yi1,Zhou Juan1,Jiao Lin1,Wu Lijuan1,Wang Minjin1,Chen Xuerong6,Zhou Yanhong1,Lu Xiaojun1,Hu Tony Y.7,Zhang Zhaolei345,Ying Binwu1ORCID

Affiliation:

1. Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, People’s Republic of China

2. Division of Laboratory Medicine, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, People’s Republic of China

3. Department of Computer Science, University of Toronto, Toronto, Ontario, Canada

4. Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada

5. The Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada

6. Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, People’s Republic of China

7. Center for Cellular and Molecular Diagnostics, Department of Biochemistry and Molecular Biology, School of Medicine, Tulane University, New Orleans, Louisiana, USA

Abstract

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).

Funder

Natural Science and Engineering Council of Canada

National Natural Science Foundation of China

Publisher

American Society for Microbiology

Subject

Microbiology (medical)

Reference35 articles.

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