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

Abstract

BackgroundClinically 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.