Department of Clinical Laboratory, Key Clinical Laboratory of Henan Province, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China.
Department of Clinical Laboratory, Henan Provincial Infectious Disease Hospital, Zhengzhou, 450000, China.
J Clin Lab Anal. 2021 Aug;35(8):e23871. doi: 10.1002/jcla.23871. Epub 2021 Jun 9.
To verify the differential expression of miR-30c and miR-142-3p between tuberculosis patients and healthy controls and to investigate the performance of microRNA (miRNA) and subsequently models for the diagnosis of tuberculosis (TB).
We followed up 460 subjects suspected of TB, and finally enrolled 132 patients, including 60 TB patients, 24 non-TB disease controls (TB-DCs), and 48 healthy controls (HCs). The differential expression of miR-30c and miR-142-3p in serum samples of the TB patients, TB-DCs, and HCs were identified by reverse transcription-quantitative real-time PCR. Diagnostic models were developed by analyzing the characteristics of miRNA and electronic health records (EHRs). These models evaluated by the area under the curves (AUC) and calibration curves were presented as nomograms.
There were differential expression of miR-30c and miR-142-3p between TB patients and HCs (p < 0.05). Individual miRNA has a limited diagnostic value for TB. However, diagnostic performance has been both significantly improved when we integrated miR-142-3p and ordinary EHRs to develop two models for the diagnosis of tuberculosis. The AUC of the model for distinguishing tuberculosis patients from healthy controls has increased from 0.75 (95% CI: 0.66-0.84) to 0.96 (95% CI: 0.92-0.99) and the model for distinguishing tuberculosis patients from non-TB disease controls has increased from 0.67 (95% CI: 0.55-0.79) to 0.94 (95% CI: 0.89-0.99).
Integrating serum miR-142-3p and EHRs is a good strategy for improving TB diagnosis.
验证结核患者与健康对照者血清中 miR-30c 和 miR-142-3p 的差异表达,并探讨 microRNA(miRNA)及其随后的模型在结核(TB)诊断中的性能。
我们对 460 例疑似结核患者进行了随访,最终纳入 132 例患者,包括 60 例 TB 患者、24 例非结核疾病对照者(TB-DC)和 48 例健康对照者(HC)。采用逆转录定量实时 PCR 检测 TB 患者、TB-DC 和 HCs 血清样本中 miR-30c 和 miR-142-3p 的差异表达。通过分析 miRNA 和电子健康记录(EHRs)的特征,构建诊断模型。通过曲线下面积(AUC)和校准曲线评估这些模型,并以列线图的形式呈现。
TB 患者与 HCs 之间存在 miR-30c 和 miR-142-3p 的差异表达(p<0.05)。单独的 miRNA 对 TB 的诊断价值有限。然而,当我们整合 miR-142-3p 和普通 EHRs 来建立两个用于诊断结核的模型时,诊断性能得到了显著提高。用于区分结核患者与健康对照者的模型的 AUC 从 0.75(95%CI:0.66-0.84)增加到 0.96(95%CI:0.92-0.99),用于区分结核患者与非结核疾病对照者的模型的 AUC 从 0.67(95%CI:0.55-0.79)增加到 0.94(95%CI:0.89-0.99)。
整合血清 miR-142-3p 和 EHRs 是提高 TB 诊断的良好策略。