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Background <p>Tuberculosis (TB) is a top infectious disease killer; underdiagnosis fuels its spread. Thus, finding perspective non-invasive biomarkers for early tuberculosis (TB) is very essential. Extracellular vesicles (EVs) microRNAs (miRNAs) are promising non-invasive biomarkers for tuberculosis (TB) detection.</p> Methods <p>To investigate the feasibility of early diagnosis of TB, we examined plasma-derived EV-miRNAs obtained from patients with active TB (n = 25), asymptomatic tuberculosis (aTB) (n = 25), and latent tuberculosis infection (LTBI) (n = 25). Initially, EVs were isolated from plasma samples using ultracentrifugation and were characterized via nanoparticle tracking analysis and immunoblotting for surface markers. Additionally, we characterized miRNA profiles using RNA sequencing to identify TB-specific EV-miRNAs. EV-miRNAs were further validated by droplet digital PCR (ddPCR).</p> Results <p>Seven differentially expressed miRNAs among the clinical subgroups (fold change &gt; 2, <i>P</i> &lt; 0.05) showed the most significant TB-specific expression patterns. Among these, three miRNAs exhibited significant differences. Furthermore, hsa-miR-451a was upregulated and two miRNAs (hsa-miR-1908-5p and hsa-miR-1268b) were downregulated. The optimized three miRNAs panel exhibited superior diagnostic accuracy with area under the curve (AUC) values of 0.970 (95% confidence interval [CI] 0.928–1.000) for discriminating active TB from healthy controls (HCs) and 0.971 (95% CI 0.923–1.000) for distinguishing LTBI from HCs. A combination of two miRNAs (hsa-miR-451a and hsa-miR-1268b) exhibited a good capacity to distinguish patients with aTB from HCs, with an AUC of 0.880 (95% CI 0.771–0.989).</p> Conclusion <p>This multigroup cohort study established a novel EV-miRNAs signature and demonstrated its clinical utility for TB staging and early detection.</p>

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ddPCR-based profiling of plasma EV-miRNA identifies a noninvasive biomarker panel for tuberculosis

  • 期刊论文

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  • 发表日期：

    2025年11月26日

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  • Zhuhua Wu,
  • Jing Liang,
  • Yuchuan Zhao,
  • Jianming Hong,
  • Yongping Xie,
  • Yongzhao Liu,
  • Shende Huang,
  • Jingwen Lai,
  • Xiaoyu Lai,
  • Ran Wei,
  • Jiawen Wang,
  • Weilong Liu,
  • Xunxun Chen

摘要

Background

Tuberculosis (TB) is a top infectious disease killer; underdiagnosis fuels its spread. Thus, finding perspective non-invasive biomarkers for early tuberculosis (TB) is very essential. Extracellular vesicles (EVs) microRNAs (miRNAs) are promising non-invasive biomarkers for tuberculosis (TB) detection.

Methods

To investigate the feasibility of early diagnosis of TB, we examined plasma-derived EV-miRNAs obtained from patients with active TB (n = 25), asymptomatic tuberculosis (aTB) (n = 25), and latent tuberculosis infection (LTBI) (n = 25). Initially, EVs were isolated from plasma samples using ultracentrifugation and were characterized via nanoparticle tracking analysis and immunoblotting for surface markers. Additionally, we characterized miRNA profiles using RNA sequencing to identify TB-specific EV-miRNAs. EV-miRNAs were further validated by droplet digital PCR (ddPCR).

Results

Seven differentially expressed miRNAs among the clinical subgroups (fold change > 2, P < 0.05) showed the most significant TB-specific expression patterns. Among these, three miRNAs exhibited significant differences. Furthermore, hsa-miR-451a was upregulated and two miRNAs (hsa-miR-1908-5p and hsa-miR-1268b) were downregulated. The optimized three miRNAs panel exhibited superior diagnostic accuracy with area under the curve (AUC) values of 0.970 (95% confidence interval [CI] 0.928–1.000) for discriminating active TB from healthy controls (HCs) and 0.971 (95% CI 0.923–1.000) for distinguishing LTBI from HCs. A combination of two miRNAs (hsa-miR-451a and hsa-miR-1268b) exhibited a good capacity to distinguish patients with aTB from HCs, with an AUC of 0.880 (95% CI 0.771–0.989).

Conclusion

This multigroup cohort study established a novel EV-miRNAs signature and demonstrated its clinical utility for TB staging and early detection.