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

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). 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.

Integrating serum microRNAs and electronic health records improved the diagnosis of tuberculosis

“…Therefore, more interests should be given to MPV in the diagnosis of TB. As nomogram for individualized prediction of incident tuberculosis and multidrug‐resistant tuberculosis is convenient, 20 , 41 , 42 two nomograms were produced subsequently, with good discrimination (through AUC) and calibration (via Hosmer–Lemeshow and test calibration curve) (Figure 6 ). However, these results were not very encouraging due to the limitation of sample size, and further study should verify the nomograms and confirm its universality.…”

“… 17 , 18 But the absence of validation limits the applicability of the sequencing findings. Moreover, recent evidence suggested that integrating miRNAs and EHRs could improve the diagnosis for TB patients, 19 , 20 but there are a few reports on this yet.…”

Screening and identification of differentially expressed long non-coding RNAs in multidrug-resistant tuberculosis

“…Each sample was run in triplicate, and the average Cq was calculated. The relative expression of each lncRNA was calculated using the 2-ΔΔCq method and normalized against internal controls ( Hu et al, 2020 ).…”

Long Non-coding RNAs in Tuberculosis: From Immunity to Biomarkers

“…Therefore, A study validated the lncRNAs and corresponding predictive models to effectively diagnose these patients, finding differentially expressed lncRNAs (ENST00000497872, n333737, n335265) between PTB patients and the healthy group. These lncRNAs might be used as putative biomarkers to facilitate the early identification of PTB cases among suspected patients with negative microbiological evidence ( Table 2 ; Hu et al, 2020 ). In addition, lncRNAs (ENST00000429730.1 and MSTRG.93125.4) were upregulated in lung tissues with high metabolic activity demonstrated by fluorine-18-fluorodeoxyglucose positron emission tomography/computed tomography from sputum-negative TB patients compared with low metabolic activity.…”