Differential expression of long non-coding RNAs in patients with tuberculosis infection

et al. 2019

Preprint

Background: Clinically diagnosed pulmonary tuberculosis (PTB) patients lack Mycobacterium tuberculosis (MTB) microbiologic evidence, and misdiagnosis or delayed diagnosis often occurs as a consequence. We investigated the potential of lncRNAs and corresponding predictive models to diagnose these patients. Methods: We enrolled 1372 subjects, including clinically diagnosed PTB patients, 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 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 AUC and decision curve analysis, and the optimal model was presented as a Web-based nomogram, which was evaluated in the Validation Cohort. The biological function of lncRNAs was interrogated using ELISA, lactate dehydrogenase release analysis and flow cytometry. Results: Three differentially expressed lncRNAs (ENST00000497872, n333737, n335265) were identified. The optimal model (i.e., nomogram) incorporated these three lncRNAs and six EHR variables (age, hemoglobin, weight loss, low-grade fever, CT calcification and TB-IGRA). The nomogram showed an AUC of 0.89, sensitivity of 0.86 and specificity of 0.82 in the Validation Cohort, which demonstrated better discrimination and clinical net benefit than the EHR model. ENST00000497872 may regulate inflammatory cytokine production, cell death and apoptosis during MTB infection. Conclusion: LncRNAs and the user-friendly nomogram could facilitate the early identification of PTB cases among suspected patients with negative MTB microbiologic evidence.

Section: Discussionsupporting

confidence: 89%

Section: Why Read On?mentioning

confidence: 99%

LncRNA and predictive model to improve the diagnosis of clinically diagnosed pulmonary tuberculosis

Chen

et al. 2019

Preprint

“…The lncRNA that was most 236 significantly enriched in the PTB group of this study, ENST00000497872 (chr14:105703964-237 105704602), is located close to IGHA1 (chr14: 105703995-105708665), and functional 238 analyses indicated that expression of this lncRNA was involved in the regulation of 239 inflammatory cytokine production and cell apoptosis in MTB-infected macrophages, 240 15 although further studies are needed to investigate the mechanisms responsible. Consistent 241 with published lncRNA data [8][9][10][11][12]31 , this data provide new evidence that lncRNAs could 242 participate in TB immunoregulation and serve as promising biomarkers for TB diagnosis. 243…”

Section: Functional Studies Of Enst00000497872 221supporting

confidence: 79%

“…Long noncoding RNAs (lncRNAs) can function as critical regulators of inflammatory 13 responses to infection, especially for T-cell responses 8,9 . Increasing evidence indicates that 14 blood lncRNA expression profiles are closely associated with TB disease [10][11][12] , suggesting 15 lncRNAs could function as potential noninvasive biomarkers for TB detection. However, 16…”

Section: Introductionmentioning

confidence: 99%

LncRNA and predictive model to improve the diagnosis of clinically diagnosed pulmonary tuberculosis

Chen

et al. 2019

Preprint

ABSTRACTBackgroundClinically diagnosed pulmonary tuberculosis (PTB) patients lack Mycobacterium tuberculosis (MTB) microbiologic evidence, and misdiagnosis or delayed diagnosis often occurs as a consequence. We investigated the potential of lncRNAs and corresponding predictive models to diagnose these patients.MethodsWe enrolled 1372 subjects, including clinically diagnosed PTB patients, 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 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 AUC and decision curve analysis, and the optimal model was presented as a Web-based nomogram, which was evaluated in the Validation Cohort. The biological function of lncRNAs was interrogated using ELISA, lactate dehydrogenase release analysis and flow cytometry.ResultsThree differentially expressed lncRNAs (ENST00000497872, n333737, n335265) were identified. The optimal model (i.e., nomogram) incorporated these three lncRNAs and six EHR variables (age, hemoglobin, weight loss, low-grade fever, CT calcification and TB-IGRA). The nomogram showed an AUC of 0.89, sensitivity of 0.86 and specificity of 0.82 in the Validation Cohort, which demonstrated better discrimination and clinical net benefit than the EHR model. ENST00000497872 may regulate inflammatory cytokine production, cell death and apoptosis during MTB infection.ConclusionsLncRNAs and the user-friendly nomogram could facilitate the early identification of PTB cases among suspected patients with negative MTB microbiologic evidence.

“…Long noncoding RNAs (lncRNAs) can function as critical regulators of inflammatory responses to infection, especially for T-cell responses 8,9 . Increasing evidence indicates that blood lncRNA expression profiles are closely associated with TB disease [10][11][12] , suggesting lncRNAs could function as potential noninvasive biomarkers for TB detection. However, previous studies have suffered from small sample size (ranging from 66 to 510) and lack independent validation.…”

Section: Introductionmentioning

confidence: 99%

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

Bai

et al. 2020

J Clin Microbiol

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.