Early cancer detection by serum biomolecular fingerprinting spectroscopy with machine learning

Shilian Dong; Dong He; Qian Zhang; Chaoning Huang; Zhiheng Hu; Chenyang Zhang; Lei Nie; Kun Wang; Wei Luo; Jing Yu; Bin Tian; Wei Wu; Xu Chen; Fubing Wang; Jing Hu; Xiangheng Xiao

doi:10.1186/s43593-023-00051-5

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Early cancer detection by serum biomolecular fingerprinting spectroscopy with machine learning

光：快讯（英文） 2023年3卷第1期

LETTER | Updated：2024-09-25

- Early cancer detection by serum biomolecular fingerprinting spectroscopy with machine learning
- Early cancer detection by serum biomolecular fingerprinting spectroscopy with machine learning
- Affiliations：
  
  Department of Physics, National Demonstration Center for Experimental Physics Education, Wuhan University, Wuhan 430072, China
  Department of Laboratory Medicine, Zhongnan Hospital of Wuhan University, Wuhan 430071, China
  School of Computer Science, Wuhan University, Wuhan 430072, China
  Department of Hepatobiliary Pancreatic Surgery, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430079, China
  Department of Clinical Laboratory, Tianjin Medical University General Hospital, Tianjin 300052, China
  Department of Blood Transfusion, Wuhan Hospital of Traditional Chinese and Western Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
  Laboratory of Printable Functional Materials and Printed Electronics, Research Center for Graphic Communication, Printing and Packaging, Wuhan University, Wuhan 430072, China
  Sichuan Provincial Key Laboratory for Human Disease Gene Study and the Center for Medical Genetics, Department of Laboratory Medicine, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, University of Electronic Science and Technology, Chengdu 611731, China
  School of Medicine, University of Electronic Science and Technology of China, Chengdu 611731, China
  Wuhan Research Center for Infectious Diseases and Cancer, Chinese Academy of Medical Sciences, Wuhan 430071, China
- Author bio：
  
  *Fubing Wangwfb20042002@sina.com
  Jing Hujinghu_somed@uestc.edu.cn
  Xiangheng Xiaoxxh@whu.edu.cn
- Funds：
  
  the National Natural Science Foundation of China(12025503;12102086);Science Fund for Creative Research Groups of the Natural Science Foundation of Hubei Province(2022CFA005);Experimental Technology project of Wuhan University(WHU-2021-SYJS-06);Sichuan Science and Technology Program(2021YJ0182);the Fundamental Research Funds for the Central Universities(2042021kf0227;2042022kf1181);medical Sci-Tech innovation platform of Zhongnan Hospital(PTXM2021001)
- DOI：10.1186/s43593-023-00051-5
  中图分类号：
- 纸质出版日期：2023，
  
  网络出版日期：2023-07-24，
  
  收稿日期：2023-03-15，
  
  修回日期：2023-05-28，
  
  录用日期：2023-06-06
- Accepted：
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Early cancer detection by serum biomolecular fingerprinting spectroscopy with machine learning[J]. 光：快讯（英文）, 2023,3(1).

Shilian Dong, Dong He, Qian Zhang, et al. Early cancer detection by serum biomolecular fingerprinting spectroscopy with machine learning[J]. eLight, 2023,3(1).
Early cancer detection by serum biomolecular fingerprinting spectroscopy with machine learning[J]. 光：快讯（英文）, 2023,3(1). DOI： 10.1186/s43593-023-00051-5.

Shilian Dong, Dong He, Qian Zhang, et al. Early cancer detection by serum biomolecular fingerprinting spectroscopy with machine learning[J]. eLight, 2023,3(1). DOI： 10.1186/s43593-023-00051-5.

摘要

Abstract

Label-free surface-enhanced Raman scattering (SERS) technique with ultra-sensitivity becomes more and more desirable in biomedical analysis

which is yet hindered by inefficient follow-up data analysis. Here we report an integrative method based on SERS and Artificial Intelligence for Cancer Screening (SERS-AICS) for liquid biopsy such as serum via silver nanowires

combining molecular vibrational signals processing with large-scale data mining algorithm. According to 382 healthy controls and 1582 patients from two independent cohorts

SERS-AICS not only distinguishes pan-cancer patients from health controls with 95.81% overall accuracy and 95.87% sensitivity at 95.40% specificity

but also screens out those samples at early cancer stage. The supereminent efficiency potentiates SERS-AICS a promising tool for detecting cancer with broader types at earlier stage

accompanying with the establishment of a data platform for further deep analysis.

关键词

Keywords

references

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