Drug Clinical Trial Center, Gansu Wuwei Tumor Hospital, Wuwei, Gansu, People's Republic of China.
Microbiol Immunol. 2022 Jun;66(6):317-323. doi: 10.1111/1348-0421.12983. Epub 2022 May 27.
Improved diagnostic tests for tuberculosis (TB) among people with human immunodeficiency virus (HIV) are urgently required. We hypothesized that methylation-driven genes (MDGs) of host blood could be used to diagnose patients co-infected with HIV/TB. In this study, we identified three MDGs among patients with HIV monoinfection and those with HIV/TB co-infection using the R package MethylMix. We then developed a deep learning model by screening these three MDGs, which distinguished HIV/TB co-infection from HIV monoinfection with a sensitivity of 95.2% and a specificity of 88.3%. On the two independent data sets, the sensitivity and specificity were 80%-92.8% and 72.7%-87.5%, respectively. Besides, our deep learning model accurately classified TB (sensitivity, 75.0%-100%; specificity, 91.3%-98.1%) and other respiratory disorders (sensitivity, 72.7%-75.0%; specificity, 70.9%-72.7%). This study will contribute to improve molecular diagnosis for HIV/TB co-infection.
迫切需要改进针对人类免疫缺陷病毒 (HIV) 感染者的结核病 (TB) 诊断检测方法。我们假设宿主血液的甲基化驱动基因 (MDG) 可用于诊断 HIV/TB 合并感染的患者。在这项研究中，我们使用 R 包 MethylMix 从 HIV 单一感染患者和 HIV/TB 合并感染患者中鉴定出三个 MDG。然后，我们通过筛选这三个 MDG 开发了一个深度学习模型，该模型能够以 95.2%的敏感性和 88.3%的特异性区分 HIV/TB 合并感染与 HIV 单一感染。在两个独立的数据集中，敏感性和特异性分别为 80%-92.8%和 72.7%-87.5%。此外，我们的深度学习模型能够准确地对 TB（敏感性为 75.0%-100%；特异性为 91.3%-98.1%）和其他呼吸道疾病（敏感性为 72.7%-75.0%；特异性为 70.9%-72.7%）进行分类。这项研究将有助于改善 HIV/TB 合并感染的分子诊断。
