Liu Jiaxing, Li Ye, Liu Ting, Shi Yuru, Wang Yun, Wu Jing, Qi Yingjie
Department of Clinical Laboratory, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, Jiangsu Province, 210008, People's Republic of China.
Department of Laboratory Medicine, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230000, People's Republic of China.
Infect Drug Resist. 2023 Jun 16;16:3847-3859. doi: 10.2147/IDR.S412116. eCollection 2023.
(Mtb) survives inside a human host for a long time in the form of latent tuberculosis infection (LTBI). Latent infection of tuberculosis has the opportunity of developing into active tuberculosis (ATB), which has greatly endangered human health. The existing diagnostic methods cannot effectively distinguish LTBI from ATB. Therefore, more effective diagnostic biomarkers and methods are urgently needed.
Here, we screened the GEO data set, conducted joint differential analysis and target gene enrichment analysis, after filtering the disease-related database, we screened the differential miRNA related to TB. The qPCR was used to verify the miRNAs in 84 serum samples. Different combinations of biomarkers were evaluated by logistic regression to obtain a biomarker panel with good performance for diagnosing LTBI.
A panel with two miRNAs (hsa-let-7d-5p, hsa-miR-140-5p) was established to differentiate LTBI from ATB. Receiver operating characteristic (ROC) curve showed that the area under the curve (AUC) are 0.930 (sensitivity = 100%, specificity = 88.5%) and 0.923 (sensitivity = 100%, specificity = 92.3%) with the biomarker panel for the training set and test set respectively.
The findings indicated that the logistic regression model built by let-7d-5p and miR-140-5p has the ability to distinguish LTBI from active TB patients.
结核分枝杆菌（Mtb）以潜伏性结核感染（LTBI）的形式在人类宿主体内存活很长时间。潜伏性结核感染有发展为活动性结核病（ATB）的可能，这已对人类健康构成极大威胁。现有的诊断方法无法有效区分LTBI和ATB。因此，迫切需要更有效的诊断生物标志物和方法。
在此，我们筛选了GEO数据集，进行联合差异分析和靶基因富集分析，在筛选疾病相关数据库后，我们筛选出与结核病相关的差异微小RNA（miRNA）。采用qPCR对84份血清样本中的miRNA进行验证。通过逻辑回归评估生物标志物的不同组合，以获得对诊断LTBI具有良好性能的生物标志物组合。
建立了一个包含两种miRNA（hsa-let-7d-5p、hsa-miR-140-5p）的组合来区分LTBI和ATB。受试者操作特征（ROC）曲线显示，训练集和测试集使用该生物标志物组合时，曲线下面积（AUC）分别为0.930（敏感性 = 100%，特异性 = 88.5%）和0.923（敏感性 = 100%，特异性 = 92.3%）。
研究结果表明，由let-7d-5p和miR-140-5p构建的逻辑回归模型有能力区分LTBI和活动性结核病患者。
