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<p>Recent advances in unbiased metagenomic next-generation sequencing (mNGS) enable simultaneous examination of microbial and host genetic material. We developed a multimodal machine learning-based diagnostic approach to differentiate lung cancer and pulmonary infections by analyzing 402 bronchoalveolar lavage fluid (BALF) mNGS datasets, including lung cancer (<i>n</i> = 123), bacterial infections (<i>n</i> = 114), fungal infections (<i>n</i> = 79), and pulmonary tuberculosis (<i>n</i> = 86). The training cohort revealed differences in microbial profiles, bacteriophage abundance, host gene and transposable element expression, immune cell composition, and tumor fraction derived from copy number variation (CNV). The integrated model (Model VI) achieved an AUC of 0.937 (95% CI, 0.910–0.964) in the training cohort and 0.847 (95% CI, 0.776–0.918) in the test cohort. A rule-in/rule-out strategy further improved accuracy in differentiating lung cancer from tuberculosis (accuracy = 0.896), fungal (accuracy = 0.915), and bacterial (accuracy = 0.907) infections. These findings highlight the potential of mNGS-based multimodal analysis as a cost-effective tool for early and accurate differential diagnosis.</p>

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Metagenomic fingerprints in bronchoalveolar lavage differentiate pulmonary diseases

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月7日

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  • Dongsheng Han,
  • Chang Liu,
  • Bin Yang,
  • Fei Yu,
  • Huifang Liu,
  • Bin Lou,
  • Yifei Shen,
  • Hui Tang,
  • Hua Zhou,
  • Shufa Zheng,
  • Yu Chen

摘要

Recent advances in unbiased metagenomic next-generation sequencing (mNGS) enable simultaneous examination of microbial and host genetic material. We developed a multimodal machine learning-based diagnostic approach to differentiate lung cancer and pulmonary infections by analyzing 402 bronchoalveolar lavage fluid (BALF) mNGS datasets, including lung cancer (n = 123), bacterial infections (n = 114), fungal infections (n = 79), and pulmonary tuberculosis (n = 86). The training cohort revealed differences in microbial profiles, bacteriophage abundance, host gene and transposable element expression, immune cell composition, and tumor fraction derived from copy number variation (CNV). The integrated model (Model VI) achieved an AUC of 0.937 (95% CI, 0.910–0.964) in the training cohort and 0.847 (95% CI, 0.776–0.918) in the test cohort. A rule-in/rule-out strategy further improved accuracy in differentiating lung cancer from tuberculosis (accuracy = 0.896), fungal (accuracy = 0.915), and bacterial (accuracy = 0.907) infections. These findings highlight the potential of mNGS-based multimodal analysis as a cost-effective tool for early and accurate differential diagnosis.