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Background <p>Emerging evidence links multi-anatomical site microbiota of the respiratory tract to lung cancer development; however, its potential for predicting the malignancy risk of ground-glass nodules (GGN) has not been systematically explored.</p> Methods <p>A total of 748 patients with GGN from three medical centers were prospectively enrolled. Nasopharyngeal swabs and bronchoalveolar lavage fluid (BALF) samples were collected from these patients. During the 2-year follow-up, the patients were divided into a benign group (B_GGN, <i>n</i> = 251) and a malignant (M_GGN, <i>n</i> = 136) group. We used 16S rRNA gene sequencing to analyze the structure of the respiratory microbiota. Additionally, seven machine learning algorithms were integrated to construct and screen the best model for predicting the malignancy risk of GGN. The Shapley Additive Explanations method was utilized to identify key microbial markers, and their specificity was verified using an external independent dataset. Furthermore, co-occurrence network analysis and PICRUSt2 functional prediction were conducted to explore the functional changes in microbial communities during the malignant progression of GGN.</p> Results <p>The respiratory microbiota of patients with GGN displayed distinct site-specific distribution characteristics, with the nasopharyngeal microbiota demonstrating significant advantages in predicting the malignancy risk of GGN. The LightGBM prediction model based on the nasopharyngeal microbiota exhibited the best diagnostic performance (AUC = 0.808, 95% CI: 0.769–0.850). <i>Fusobacterium</i>, <i>Gemella</i>, <i>TM7x</i>, <i>Lachnoanaerobaculum</i>, <i>Rothia</i>, and <i>Veillonella</i> were identified as key biomarkers. The specificity of these markers has been validated in multiple external cohorts and can enhance the overall predictive performance of traditional clinical models, such as the Mayo Clinic Model (AUC = 0.835, 95% CI: 0.801–0.873). Functional prediction analysis suggested that the malignant progression of GGN may be associated with dysregulation of amino acid metabolism and immune-related pathways.</p> Conclusions <p>The nasopharyngeal microbiota might serve as a non-invasive and reliable biomarker for early prediction of the malignant risk of GGN, exhibiting potential application value in the clinical management of pulmonary nodules.</p> Trial registration <p>ChiCTR2200062140; Date of registration: 25/07/2022.</p>

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Association of multi-site microbial features with malignancy risk in pulmonary ground-glass nodules and identification of predictive biomarkers: a prospective multicenter cohort study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月28日

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  • Chunxia Huang,
  • Jiawei He,
  • Xi Fu,
  • Yang Zhong,
  • Yuling Jiang,
  • Aoling Yang,
  • Hengzhou Lai,
  • Qian Wang,
  • Shiyan Tan,
  • Xueke Li,
  • Yifang Jiang,
  • Yuli Qu,
  • Xiang Zhuang,
  • Ping Xiao,
  • Yifeng Ren,
  • Chuan Zheng,
  • Fengming You,
  • Qiong Ma

摘要

Background

Emerging evidence links multi-anatomical site microbiota of the respiratory tract to lung cancer development; however, its potential for predicting the malignancy risk of ground-glass nodules (GGN) has not been systematically explored.

Methods

A total of 748 patients with GGN from three medical centers were prospectively enrolled. Nasopharyngeal swabs and bronchoalveolar lavage fluid (BALF) samples were collected from these patients. During the 2-year follow-up, the patients were divided into a benign group (B_GGN, n = 251) and a malignant (M_GGN, n = 136) group. We used 16S rRNA gene sequencing to analyze the structure of the respiratory microbiota. Additionally, seven machine learning algorithms were integrated to construct and screen the best model for predicting the malignancy risk of GGN. The Shapley Additive Explanations method was utilized to identify key microbial markers, and their specificity was verified using an external independent dataset. Furthermore, co-occurrence network analysis and PICRUSt2 functional prediction were conducted to explore the functional changes in microbial communities during the malignant progression of GGN.

Results

The respiratory microbiota of patients with GGN displayed distinct site-specific distribution characteristics, with the nasopharyngeal microbiota demonstrating significant advantages in predicting the malignancy risk of GGN. The LightGBM prediction model based on the nasopharyngeal microbiota exhibited the best diagnostic performance (AUC = 0.808, 95% CI: 0.769–0.850). Fusobacterium, Gemella, TM7x, Lachnoanaerobaculum, Rothia, and Veillonella were identified as key biomarkers. The specificity of these markers has been validated in multiple external cohorts and can enhance the overall predictive performance of traditional clinical models, such as the Mayo Clinic Model (AUC = 0.835, 95% CI: 0.801–0.873). Functional prediction analysis suggested that the malignant progression of GGN may be associated with dysregulation of amino acid metabolism and immune-related pathways.

Conclusions

The nasopharyngeal microbiota might serve as a non-invasive and reliable biomarker for early prediction of the malignant risk of GGN, exhibiting potential application value in the clinical management of pulmonary nodules.

Trial registration

ChiCTR2200062140; Date of registration: 25/07/2022.