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

Abstract

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.

The Role of microRNAs and Long Non-Coding RNAs in the Regulation of the Immune Response to Mycobacterium tuberculosis Infection

“…Hu et al. ( 183 ) have shown that three lncRNAs, ENST00000497872, n333737, and n335265 are differentially expressed in blood samples of active TB patients compared to healthy individuals and suggested that these may serve as potential biomarkers for clinically diagnosed PTB patients. Of these, lncRNA ENST00000497872 is located close to the immune related gene immunoglobulin heavy constant alfa 1 (IGHA1) and n333737 is located close to the immune related gene T cell receptor alfa variable 1-2 (TRAV1-2).…”

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

“…Compelling studies have proposed that models incorporating biomarkers and EHR information attain better performance for prediction of sepsis 17 and abdominal aortic aneurysm 18 . We previously reported that combining exosomal microRNAs and EHRs in the diagnosis of tuberculous meningitis (TBM) achieved AUCs of up to 0.97 versus an AUC of 0.67 obtained using EHR alone 19 . Based on these studies, we hypothesized that combining lncRNAs with well-defined EHR predictors could be used to develop improved predictive models to identify PTB cases that lack microbiologic evidence of MTB infection.…”

Monokine induced by gamma interferon for detecting pulmonary tuberculosis

“…[ 6 ] However, these approaches have various drawbacks, including the time delay for positive culture and poor sensitivity (20%–60%) of microscopy. [ 7 , 8 ] The Xpert MTB/RIF assay is recommended by the World Health Organization for the diagnosis of PTB. [ 9 ] However, this method is costly and limited in smear-negative sputum, especially in HIV-coinfected cases.…”