Hu Xuejiao, Liao Shun, Bai Hao, Gupta Shubham, Zhou Yi, Zhou Juan, Jiao Lin, Wu Lijuan, Wang Minjin, Chen Xuerong, Zhou Yanhong, Lu Xiaojun, Hu Tony Y, Zhang Zhaolei, Ying Binwu
Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, People's Republic of China.
Division of Laboratory Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, People's Republic of China.
J Clin Microbiol. 2020 Jun 24;58(7). doi: 10.1128/JCM.01973-19.
Clinically diagnosed pulmonary tuberculosis (PTB) patients lack microbiological evidence of , 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 (, , and ) 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 microbiological evidence.
临床诊断的肺结核（PTB）患者缺乏微生物学证据，因此经常发生误诊或诊断延迟的情况。我们研究了长链非编码RNA（lncRNA）的潜力以及相应的预测模型来诊断这些患者。我们纳入了1764名受试者，包括临床诊断的PTB患者、微生物学确诊的PTB病例、非结核疾病对照和健康对照，分为三个队列（筛查、选择和验证）。在筛查队列中，通过微阵列和逆转录定量PCR（qRT-PCR）鉴定了在PTB和健康对照组血样中差异表达的候选lncRNA。在选择队列中，使用来自临床诊断的PTB患者和非结核疾病对照的lncRNA和/或电子健康记录（EHR）建立逻辑回归模型。通过浓度-时间曲线下面积（AUC）和决策曲线分析对这些模型进行评估，并将最佳模型呈现为基于网络的列线图，在验证队列中对其进行评估。鉴定出三种差异表达的lncRNA（……此处原文未给出具体名称）。最佳模型（即列线图）纳入了这三种lncRNA和六个EHR（年龄、血红蛋白、体重减轻、低热、计算机断层扫描检测到的钙化[CT钙化]以及结核干扰素γ释放试验[TB-IGRA]）。在验证队列中，该列线图在区分临床诊断的PTB病例与非结核疾病对照方面，AUC为0.89，敏感性为0.86，特异性为0.82，与EHR模型相比，显示出更好的辨别力和临床净效益。该列线图在识别微生物学确诊的PTB患者方面也具有判别能力（AUC为0.90，敏感性为0.85，特异性为0.81）。lncRNA和用户友好的列线图有助于在微生物学证据阴性的疑似患者中早期识别PTB病例。
