Department of General Surgery, Huaihe Hospital of Henan University, Henan University, Kaifeng, Henan, China.
School of Basic Medical Sciences, Henan University, Kaifeng, Henan, China.
Ann Med. 2024 Dec;56(1):2380797. doi: 10.1080/07853890.2024.2380797. Epub 2024 Jul 25.
Current diagnostic methods cannot effectively distinguish between latent tuberculosis infection (LTBI) and active tuberculosis (ATB). This study aims to explore novel non-invasive diagnostic biomarkers for LTBI and to elucidate possible molecular mechanisms of LTBI pathogenesis.
Three GEO datasets (GSE19439, GSE19444, and GSE62525) were utilized to analyze the differentially expressed genes (DEGs). Functional enrichment studies were then performed on these DEGs. To ascertain potential diagnostic biomarkers, we utilized two different machine learning techniques: LASSO and RF. ROC curves were constructed in both the training and validation datasets to assess the diagnostic efficacy. The expression of identified biomarkers was verified by RT-qPCR in our own Chinese cohort. Using CIBERSORT, we estimated the abundances of 22 immune cell types in LTBI group, and subsequently analyzed the relationship between biomarker expression and immune cell infiltration.
166 DEGs were identified between ATB and LTBI groups, which are primarily associated with immune responses, inflammatory signaling pathways, and infection factors. Following that, 22 candidate diagnostic biomarkers for LTBI were selected in the machine learning process. Three up-regulated genes, , , and , whose expression levels were not previously reported in TB, were validated using the training and validation cohort datasets. In our own Chinese cohort, we also found that and showed good diagnostic effect using RT-qPCR method. Finally, we revealed the specific infiltration features of immune cells in LTBI and observed a notable correlation between potential marker expression and immune cells.
and emerged as candidate diagnostic biomarkers for LTBI, following the elucidation of the key immune cell types involved. Our findings will contribute to providing a potential target for early noninvasive diagnosis of LTBI patients.
目前的诊断方法无法有效区分潜伏性结核感染（LTBI）和活动性结核（ATB）。本研究旨在探索 LTBI 的新型非侵入性诊断生物标志物，并阐明 LTBI 发病机制的可能分子机制。
本研究使用了三个 GEO 数据集（GSE19439、GSE19444 和 GSE62525）来分析差异表达基因（DEGs）。然后对这些 DEGs 进行功能富集研究。为了确定潜在的诊断生物标志物，我们使用了两种不同的机器学习技术：LASSO 和 RF。在训练和验证数据集上构建 ROC 曲线，以评估诊断效能。通过 RT-qPCR 在我们自己的中国队列中验证了鉴定生物标志物的表达。使用 CIBERSORT，我们估计了 LTBI 组中 22 种免疫细胞类型的丰度，随后分析了生物标志物表达与免疫细胞浸润之间的关系。
在 ATB 和 LTBI 组之间鉴定出 166 个 DEGs，这些基因主要与免疫反应、炎症信号通路和感染因子有关。随后，在机器学习过程中选择了 22 个 LTBI 的候选诊断生物标志物。三个上调基因、、和，它们在 TB 中的表达水平以前没有报道过，在训练和验证队列数据集上得到了验证。在我们自己的中国队列中，我们还发现使用 RT-qPCR 方法，和也表现出良好的诊断效果。最后，我们揭示了 LTBI 中特定免疫细胞的浸润特征，并观察到潜在标志物表达与免疫细胞之间存在显著相关性。
和可能成为 LTBI 的候选诊断生物标志物，同时阐明了相关的关键免疫细胞类型。我们的研究结果将有助于为 LTBI 患者的早期非侵入性诊断提供潜在的目标。
