Microfluidic Biochip-Based Multiplexed Profiling of Small Extracellular Vesicles Proteins Integrated with Machine Learning for Early Disease Diagnosis.

Journal: Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Published Date: Jul 7, 2025

Abstract

Accurate early diagnosis is essential for preventing diseases and improving cure and survival rates. There are no reliable early-diagnosis biomarkers for most major diseases. Here, esophageal squamous cell carcinoma (ESCC) is used as a disease model to develop a platform for detecting a panel of proteomic biomarkers for accurate early diagnosis by integrating a barcode immunoassay biochip with machine learning. The biochip captures small extracellular vesicles (EVs) from serum, lyses them in situ, and quantifies multiple proteins, including membrane and internal proteins of EVs. It is utilized to test 273 clinical samples across multiple centers. The validation sets are then analyzed using machine learning, resulting in a precise diagnostic model for ESCC. This model, based on nine diagnostic protein biomarkers identified through mass spectrometry analysis of differentially expressed proteins, achieves an accuracy of 91.0% in external validation, with a 90.8% accuracy in detecting early-stage ESCC. These results significantly surpass the accuracy (only 14.4%) of the currently used biomarker for squamous cell carcinoma. Thus, integrating extracellular vesicles protein analysis with machine learning presents can identify ESCC patients. The developed extracellular vesicles analysis platform offers a promising tool for the clinical application of multi-biomarker detection methods, advancing the early diagnosis of ESCC.

Authors

  • Xue Zhang
    School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
  • Yibin Jia
    Department of Radiation Oncology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Wenhua Xi Road, Jinan, Shandong Province, 250012, China.
  • Zhikai Li
    School of Engineering, Westlake University, Hangzhou, 310014, Zhejiang, China.
  • Yunhong Zhang
    School of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, China.
  • Chao Wang
    College of Agriculture, Shanxi Agricultural University, Taigu, Shanxi, China.
  • Yanbo Liang
    Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai'an, 271000, China.
  • Jiaoyan Qiu
    Institute of Marine Science and Technology, Shandong University, Binhai Road, Qingdao, Shandong Province, 266237, China.
  • Mingyuan Sun
    Institute of Marine Science and Technology, Shandong University, Binhai Road, Qingdao, Shandong Province, 266237, China.
  • Xiaoshuang Chen
    Department of Ultrasound, The First Affiliated Hospital of Xiamen University, Xiamen, Fujian, China.
  • Miao Huang
  • Yu Zhang
    College of Marine Electrical Engineering, Dalian Maritime University, Dalian, China.
  • Jianbo Wang
    Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China.
  • Hong Liu
    Key Laboratory of Grain and Oil Processing and Food Safety of Sichuan Province, College of Food and Bioengineering, Xihua University Chengdu 610039 China xingyage1@163.com.
  • Chuanbin Mao
    Department of Biomedical Engineering, The Chinese University of Hong Kong, Sha Tin, Hong Kong SAR, China.
  • Lin Han
    Department of Radiology Center, The First Affiliated Hospital of Xinxiang Medical University, Xin Xiang, China.

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