The Eighth Medical Center of the PLA General Hospital, Beijing, 100091, People's Republic of China.
Sci Rep. 2024 Jun 10;14(1):13345. doi: 10.1038/s41598-024-63365-5.
Tuberculosis (TB) remains a significant global health threat, necessitating effective strategies for diagnosis, prognosis, and treatment. This study employs a multi-cohort analysis approach to unravel the immune microenvironment of TB and delineate distinct subtypes within pulmonary TB (PTB) patients. Leveraging functional gene expression signatures (Fges), we identified three PTB subtypes (C1, C2, and C3) characterized by differential immune-inflammatory activity. These subtypes exhibited unique molecular features, functional disparities, and cell infiltration patterns, suggesting varying disease trajectories and treatment responses. A neural network model was developed to predict PTB progression based on a set of biomarker genes, achieving promising accuracy. Notably, despite both genders being affected by PTB, females exhibited a relatively higher risk of deterioration. Additionally, single-cell analysis provided insights into enhanced major histocompatibility complex (MHC) signaling in the rapid clearance of early pathogens in the C3 subgroup. This comprehensive approach offers valuable insights into PTB pathogenesis, facilitating personalized treatment strategies and precision medicine interventions.
结核病（TB）仍然是一个重大的全球健康威胁，需要有效的策略来进行诊断、预后和治疗。本研究采用多队列分析方法来揭示结核病的免疫微环境，并描绘出肺结核（PTB）患者中的不同亚型。利用功能基因表达特征（Fges），我们确定了三个 PTB 亚型（C1、C2 和 C3），其特征是免疫炎症活性的差异。这些亚型表现出独特的分子特征、功能差异和细胞浸润模式，表明存在不同的疾病轨迹和治疗反应。我们开发了一个神经网络模型，基于一组生物标志物基因来预测 PTB 的进展，取得了有前景的准确性。值得注意的是，尽管男女都受到 PTB 的影响，但女性的病情恶化风险相对较高。此外，单细胞分析提供了对 MHC 信号增强的深入了解，这有助于 C3 亚组中早期病原体的快速清除。这种综合方法为结核病的发病机制提供了有价值的见解，有助于制定个性化的治疗策略和精准医疗干预措施。
