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. Key Messages What is the key question? Does integrating immune-related lncRNA signatures and electronic health records (EHRs) promote the early identification of PTB patients who are symptomatic but lack microbiologic evidence of Mycobacterium tuberculosis (MTB)? What is the bottom line? We found three long non-coding RNAs (lncRNAs), i.e., ENST00000497872 , n 333737 and n335265 , were potential diagnostic biomarkers for clinically diagnosed PTB patients; and we further developed and validated a novel nomogram incorporating these three lncRNAs and six electronic health records (EHRs), which were readily obtainable even in a resource-constrained setting and achieved a c-statistic of 0.89, sensitivity of 0.86 and specificity of 0.82 in a separate validation cohort. Why read on? This study focuses on the challenge of accurately diagnosing PTB patients with negative MTB microbiological evidence and serves as the first proof-of-concept that integrating lncRNA signatures and EHR data could be a more promising diagnostic approach for clinically diagnosed PTB patients. SUMMARY This study developed and validated a novel nomogram that incorporated three lncRNAs and six EHR fields could be a useful predictive tool in identifying PTB patients who lack MTB microbiologic evidence.

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