准确的脊柱结核（TB）诊断对于足够治疗和管理疾病至关重要。鉴于需求需要更多的诊断工具，本研究旨在探索宿主血清miRNA生物标志物在诊断和区分脊柱结核（STB）与肺结核（PTB）以及不同来源的其他脊柱疾病（SDD）中的应用。在病例控制研究中，共自愿招募了423名受试者，在4个临床中心分别有157例STB、83例SDD、30例活动性PTB和153例健康对照（CONT）。为了发现STB特异性miRNA生物标志物，本研究采用Exiqon miRNA PCR阵列平台对12例STB和8例CONT进行了高通量miRNA分析研究。生物信息学研究确定3种血浆miRNA组合（hsa-miR-506-3p、hsa-miR-543、hsa-miR-195-5p）可能是STB的候选生物标志物。随后的训练研究使用多元Logistic回归在训练数据集（包括CONT（n=100）和STB（n=100））中开发了诊断模型，Youden的J指数确定了最佳分类阈值。接收操作特征（ROC）曲线分析表明，3种血浆miRNA生物标志物组合的曲线下面积（AUC）=0.87，敏感度=80.5%，特异性=80.0％。为了探索从PDB和其他SDD中区分脊柱TB的可能潜力，将具有相同分类阈值的诊断模型应用于独立验证数据集的分析中，包括CONT（n=45），STB（n=45），布鲁氏菌性脊柱炎（BS，n=30），PTB（n=30），脊柱肿瘤（ST，n=30）和化脓性脊柱炎（PS，n=23）。结果表明，基于三种miRNA生物标志物的诊断模型可以以敏感度=80％，特异度=96％，阳性预测值（PPV）=84％，阴性预测值（NPV）=94％的总准确率区分STB与其他SDD组。这些结果表明，这种3种血浆miRNA生物标志物组合可以有效地区分STB和其他脊柱破坏性疾病和肺结核。本研究表明，基于3种血浆miRNA生物标志物组合（hsa-miR-506-3p、hsa-miR-543、hsa-miR-195-5p）的诊断模型可用于医学指导，以区分STB与其他脊柱破坏性疾病和肺结核。Copyright © 2023 Liang, Jin, Huang, Yin, Liu, Liu, Song, Wang and Fei.

Accurate spinal tuberculosis (TB) diagnosis is of utmost importance for adequately treating and managing the disease. Given the need for additional diagnostic tools, this study aimed to investigate the utility of host serum miRNA biomarkers for diagnosing and distinguishing spinal tuberculosis (STB) from pulmonary tuberculosis (PTB) and other spinal diseases of different origins (SDD). For a case-controlled investigation, a total of 423 subjects were voluntarily recruited, with 157 cases of STB, 83 cases of SDD, 30 cases of active PTB, and 153 cases of healthy controls (CONT) in 4 clinical centers. To discover the STB-specific miRNA biosignature, a high-throughput miRNA profiling study was performed in the pilot study with 12 cases of STB and 8 cases of CONT using the Exiqon miRNA PCR array platform. A bioinformatics study identified that the 3-plasma miRNA combination (hsa-miR-506-3p, hsa-miR-543, hsa-miR-195-5p) might serve as a candidate biomarker for STB. The subsequent training study developed the diagnostic model using multivariate logistic regression in training data sets, including CONT(n=100) and STB (n=100). Youden's J index determined the optimal classification threshold. Receiver Operating Characteristic (ROC) curve analysis showed that 3-plasma miRNA biomarker signatures have an area under the curve (AUC) = 0.87, sensitivity = 80.5%, and specificity = 80.0%. To explore the possible potential to distinguish spinal TB from PDB and other SDD, the diagnostic model with the same classification threshold was applied to the analysis of the independent validation data set, including CONT(n=45), STB(n=45), brucellosis spondylitis (BS, n=30), PTB (n=30), spinal tumor (ST, n=30) and pyogenic spondylitis (PS, n=23). The results showed diagnostic model based on three miRNA signatures could discriminate the STB from other SDD groups with sensitivity=80%, specificity=96%, Positive Predictive Value (PPV)=84%, Negative Predictive Value (NPV)=94%, the total accuracy rate of 92%. These results indicate that this 3-plasma miRNA biomarker signature could effectively discriminate the STB from other spinal destructive diseases and pulmonary tuberculosis. The present study shows that the diagnostic model based on 3-plasma miRNA biomarker signature (hsa-miR-506-3p, hsa-miR-543, hsa-miR-195-5p) may be used for medical guidance to discriminate the STB from other spinal destructive disease and pulmonary tuberculosis.Copyright © 2023 Liang, Jin, Huang, Yin, Liu, Liu, Song, Wang and Fei.