School of Medical Laboratory, Weifang Medical University, Weifang, China.
Respiratory Medicine, Affiliated Hospital of Weifang Medical University, Weifang, China.
J Cell Mol Med. 2023 Sep;27(17):2482-2494. doi: 10.1111/jcmm.17836. Epub 2023 Jul 6.
Around the world, tuberculosis (TB) remains one of the most common causes of morbidity and mortality. The molecular mechanism of Mycobacterium tuberculosis (Mtb) infection is still unclear. Extracellular vesicles (EVs) play a key role in the onset and progression of many disease states and can serve as effective biomarkers or therapeutic targets for the identification and treatment of TB patients. We analysed the expression profile to better clarify the EVs characteristics of TB and explored potential diagnostic markers to distinguish TB from healthy control (HC). Twenty EVs-related differentially expressed genes (DEGs) were identified, and 17 EVs-related DEGs were up-regulated and three DEGs were down-regulated in TB samples, which were related to immune cells. Using machine learning, a nine EVs-related gene signature was identified and two EVs-related subclusters were defined. The single-cell RNA sequence (scRNA-seq) analysis further confirmed that these hub genes might play important roles in TB pathogenesis. The nine EVs-related hub genes had excellent diagnostic values and accurately estimated TB progression. TB's high-risk group had significantly enriched immune-related pathways, and there were substantial variations in immunity across different groups. Furthermore, five potential drugs were predicted for TB using CMap database. Based on the EVs-related gene signature, the TB risk model was established through a comprehensive analysis of different EV patterns, which can accurately predict TB. These genes could be used as novel biomarkers to distinguish TB from HC. These findings lay the foundation for further research and design of new therapeutic interventions aimed at treating this deadly infectious disease.
在全球范围内，结核病（TB）仍然是发病率和死亡率最高的疾病之一。分枝杆菌（Mtb）感染的分子机制仍不清楚。细胞外囊泡（EVs）在许多疾病状态的发生和进展中起着关键作用，可作为鉴定和治疗结核病患者的有效生物标志物或治疗靶点。我们分析了表达谱，以更好地阐明 TB 的 EVs 特征，并探索潜在的诊断标志物，以区分 TB 与健康对照（HC）。鉴定出 20 个与 EVs 相关的差异表达基因（DEGs），其中 17 个与 EVs 相关的 DEGs 在 TB 样本中上调，3 个 DEGs 下调，这些基因与免疫细胞有关。使用机器学习，确定了一个由 9 个 EVs 相关基因组成的特征，并定义了两个 EVs 相关亚群。单细胞 RNA 序列（scRNA-seq）分析进一步证实，这些枢纽基因可能在结核病发病机制中发挥重要作用。这 9 个与 EVs 相关的枢纽基因具有出色的诊断价值，能够准确估计结核病的进展。TB 的高危组具有明显富集的免疫相关途径，不同组之间的免疫存在显著差异。此外，使用 CMap 数据库预测了五种针对 TB 的潜在药物。基于 EVs 相关基因特征，通过对不同 EV 模式的综合分析，建立了 TB 风险模型，该模型可以准确预测 TB。这些基因可作为区分 TB 与 HC 的新型生物标志物。这些发现为进一步研究和设计旨在治疗这种致命传染病的新治疗干预措施奠定了基础。
