|
|||||
|
|
Classification and characterisation of extracellular vesicles-related tuberculosis subgroups and immune cell profiles |
|
| Authors: | Peipei Zhou Jie Shen Xiao Ge Fang Ding Hong Zhang Xinlin Huang Chao Zhao Meng Li Zhenpeng Li |
| Institution: | 1. School of Medical Laboratory, Weifang Medical University, Weifang, China
Contribution: Conceptualization (lead), Methodology (equal), Software (equal);2. School of Medical Laboratory, Weifang Medical University, Weifang, China Contribution: Methodology (equal), Software (equal), Writing - original draft (lead);3. School of Medical Laboratory, Weifang Medical University, Weifang, China Contribution: Methodology (equal), Software (supporting), Writing - original draft (supporting);4. Respiratory Medicine, Affiliated Hospital of Weifang Medical University, Weifang, China Contribution: Data curation (lead), Visualization (lead);5. School of Public Health, Weifang Medical University, Weifang, China Contribution: Writing - review & editing (equal);6. School of Medical Laboratory, Weifang Medical University, Weifang, China Contribution: ?Investigation (lead);7. Office of Academic Affairs, Weifang Medical University, Weifang, China Contribution: Writing - review & editing (equal);8. School of Medical Laboratory, Weifang Medical University, Weifang, China |
| Abstract: | 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. |
| Keywords: | biomarkers extracellular vesicles immune cell profiles single-cell RNA sequence subclusters tuberculosis |