Citation: Qiongqiong Wan, Zhourui Zhang, Mengmeng Zhao, Xianqin Ruan, Yanhong Hao, Jiajun Deng, Yunlang She, Minglei Yang, Yongxiang Song, Feng Jin, Ailin Wei, Sheng Zhong, Jie Zheng, Dong Xie, Suming Chen. Efficient diagnosis of benign and malignant pulmonary nodules based on Nano-zero-valent iron enhanced serum metabolic fingerprinting[J]. Chinese Chemical Letters, ;2025, 36(10): 110794. doi: 10.1016/j.cclet.2024.110794 shu

Efficient diagnosis of benign and malignant pulmonary nodules based on Nano-zero-valent iron enhanced serum metabolic fingerprinting

Qiongqiong Wan ^{a, 1} ,
Zhourui Zhang ^{a, 1} ,
Mengmeng Zhao ^{b, 1} ,
Xianqin Ruan ^a ,
Yanhong Hao ^a ,
Jiajun Deng ^b ,
Yunlang She ^b ,
Minglei Yang ^c ,
Yongxiang Song ^d ,
Feng Jin ^e ,
Ailin Wei ^f ,
Sheng Zhong ^g ,
Jie Zheng ^g ,
Dong Xie ^{b, *,} ,
Suming Chen ^{a, *,}

a.
The Institute for Advanced Studies, Wuhan University, Wuhan 430072, China
b.
Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai 200433, China
c.
Department of Thoracic Surgery, Hwa Mei Hospital, Chinese Academy of Sciences, Ningbo 315010, China
d.
Department of Thoracic Surgery, Affiliated Hospital of Zunyi Medical College, Zunyi Medical College, Guizhou 563099, China
e.
Shandong Key Laboratory of Infectious Respiratory Diseases, Shandong Public Health Clinical Center Affiliated to Shandong University, Ji'nan 250013, China
f.
Department of Scientific Management, Guang'an People's Hospital, Guang'an 638099, China
g.
TiLigh Biosciences, Shanghai 200050, China

kongduxd@163.com

sm.chen@whu.edu.cn

Received Date: 11 June 2024
Revised Date: 8 November 2024
Accepted Date: 21 December 2024
Available Online: 22 December 2024

Figures(6)

Accurate classification of pulmonary nodules is critical for early diagnosis of lung cancer. However, non-invasive and accurate diagnosis of benign and malignant pulmonary nodules faces great challenges. In this study, we develop a nano zero-valent iron (nZVI)-assisted laser desorption/ionization mass spectrometry (LDI MS) platform, which enables ultra-high-throughput acquisition of abundant metabolic fingerprint information of serum in negative ion mode. We further recruit a large-scale multicenter prospective cohort and collect 1099 serum samples from participants with benign and malignant nodules. The accurate machine learning models are built and validated based on nZVI-assisted LDI MS metabolomics to achieve efficient classification of benign and malignant nodules. Using our established stacking ensemble learning model, the AUC of the ROC curve for benign and malignant lung nodule classification can be as high as 0.9, and the sensitivity can reach 85.5%, which is significantly better than existing clinical models. This work provides an integrated workflow from detection technology to diagnostic models for biomarker-based pulmonary nodule diagnosis, which would be widely used in rapid and large-scale screening of pulmonary nodules.
- Pulmonary nodule,
- Nano-zero-valent iron,
- Laser desorption/ionization mass spectrometry,
- Metabolomics,
- Diagnosis
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沈阳化工大学材料科学与工程学院沈阳 110142