Metabolomic investigation of urinary extracellular vesicles for early detection and screening of lung cancer

Journal of Nanobiotechnology

Research

Liu Huang¹  Da Sun²  Xinxi Zhu³  Rui Yang⁴  Jiaxin Luo⁴  Hengrui Li⁴  Qingfu Zhu⁴  Hao Xu⁴  Fei Liu⁵  Bo Peng⁶  Qinsi Yang⁶ 

[1] Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 430030, Wuhan, Hubei, China;Institute of Life Sciences & Engineering Laboratory of Zhejiang Province for Pharmaceutical Development of Growth Factors, Wenzhou University, 325035, Wenzhou, China;Key Laboratory of Heart and Lung, The First Affiliated Hospital of Wenzhou Medical University, 325000, Wenzhou, Zhejiang, China;National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, 325027, Wenzhou, China;The First Affiliated Hospital of Wenzhou Medical University, 325035, Wenzhou, Zhejiang, China;Wenzhou Institute, University of Chinese Academy of Sciences, 325000, Wenzhou, Zhejiang, China;

关键词: Lung cancer;    Metabolomics;    Extracellular vesicles;    Early diagnosis;   

DOI  :  10.1186/s12951-023-01908-0

 received in 2023-01-06, accepted in 2023-04-24,  发布年份 2023

来源: Springer

【 摘 要 】

Lung cancer is a prevalent cancer type worldwide that often remains asymptomatic in its early stages and is frequently diagnosed at an advanced stage with a poor prognosis due to the lack of effective diagnostic techniques and molecular biomarkers. However, emerging evidence suggests that extracellular vesicles (EVs) may promote lung cancer cell proliferation and metastasis, and modulate the anti-tumor immune response in lung cancer carcinogenesis, making them potential biomarkers for early cancer detection. To investigate the potential of urinary EVs for non-invasive detection and screening of patients at early stages, we studied metabolomic signatures of lung cancer. Specifically, we conducted metabolomic analysis of 102 EV samples and identified metabolome profiles of urinary EVs, including organic acids and derivatives, lipids and lipid-like molecules, organheterocyclic compounds, and benzenoids. Using machine learning with a random forest model, we screened for potential markers of lung cancer and identified a marker panel consisting of Kanzonol Z, Xanthosine, Nervonyl carnitine, and 3,4-Dihydroxybenzaldehyde, which exhibited a diagnostic potency of 96% for the testing cohort (AUC value). Importantly, this marker panel also demonstrated effective prediction for the validation set, with an AUC value of 84%, indicating the reliability of the marker screening process. Our findings suggest that the metabolomic analysis of urinary EVs provides a promising source of non-invasive markers for lung cancer diagnostics. We believe that the EV metabolic signatures could be used to develop clinical applications for the early detection and screening of lung cancer, potentially improving patient outcomes.

【 授权许可】

CC BY   
© The Author(s) 2023

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