Nature Communications (Feb 2022)
Multiplexed nanomaterial-assisted laser desorption/ionization for pan-cancer diagnosis and classification
- Hua Zhang,
- Lin Zhao,
- Jingjing Jiang,
- Jie Zheng,
- Li Yang,
- Yanyan Li,
- Jian Zhou,
- Tianshu Liu,
- Jianmin Xu,
- Wenhui Lou,
- Weige Yang,
- Lijie Tan,
- Weiren Liu,
- Yiyi Yu,
- Meiling Ji,
- Yaolin Xu,
- Yan Lu,
- Xiaomu Li,
- Zhen Liu,
- Rong Tian,
- Cheng Hu,
- Shumang Zhang,
- Qinsheng Hu,
- Yangdong Deng,
- Hao Ying,
- Sheng Zhong,
- Xingdong Zhang,
- Yunbing Wang,
- Hua Wang,
- Jingwei Bai,
- Xiaoying Li,
- Xiangfeng Duan
Affiliations
- Hua Zhang
- National Engineering Research Center for Biomaterials, Sichuan University
- Lin Zhao
- Department of Endocrinology and Metabolism, Fudan Institute of Metabolic Diseases, Zhongshan Hospital, Fudan University
- Jingjing Jiang
- Department of Endocrinology and Metabolism, Fudan Institute of Metabolic Diseases, Zhongshan Hospital, Fudan University
- Jie Zheng
- State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University
- Li Yang
- National Engineering Research Center for Biomaterials, Sichuan University
- Yanyan Li
- National Engineering Research Center for Biomaterials, Sichuan University
- Jian Zhou
- Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University
- Tianshu Liu
- Department of Oncology, Zhongshan Hospital, Fudan University
- Jianmin Xu
- Department of General Surgery, Zhongshan Hospital, Fudan University
- Wenhui Lou
- Department of General Surgery, Zhongshan Hospital, Fudan University
- Weige Yang
- Department of General Surgery, Zhongshan Hospital, Fudan University
- Lijie Tan
- Department of Thoracic Surgery, Zhongshan Hospital, Fudan University
- Weiren Liu
- Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University
- Yiyi Yu
- Department of Oncology, Zhongshan Hospital, Fudan University
- Meiling Ji
- Department of General Surgery, Zhongshan Hospital, Fudan University
- Yaolin Xu
- Department of General Surgery, Zhongshan Hospital, Fudan University
- Yan Lu
- Department of Endocrinology and Metabolism, Fudan Institute of Metabolic Diseases, Zhongshan Hospital, Fudan University
- Xiaomu Li
- Department of Endocrinology and Metabolism, Fudan Institute of Metabolic Diseases, Zhongshan Hospital, Fudan University
- Zhen Liu
- School of Pharmaceutical Sciences, Tsinghua University
- Rong Tian
- School of Pharmaceutical Sciences, Tsinghua University
- Cheng Hu
- National Engineering Research Center for Biomaterials, Sichuan University
- Shumang Zhang
- National Engineering Research Center for Biomaterials, Sichuan University
- Qinsheng Hu
- Department of Orthopaedic Surgery, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University
- Yangdong Deng
- School of Software, Tsinghua University
- Hao Ying
- CAS Key Laboratory of Nutrition, Metabolism and Food safety, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences
- Sheng Zhong
- State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University
- Xingdong Zhang
- National Engineering Research Center for Biomaterials, Sichuan University
- Yunbing Wang
- National Engineering Research Center for Biomaterials, Sichuan University
- Hua Wang
- Department of Oncology, the First Affiliated Hospital, Institute for Liver Diseases of Anhui Medical University
- Jingwei Bai
- School of Pharmaceutical Sciences, Tsinghua University
- Xiaoying Li
- Department of Endocrinology and Metabolism, Fudan Institute of Metabolic Diseases, Zhongshan Hospital, Fudan University
- Xiangfeng Duan
- Department of Chemistry and Biochemistry, University of California
- DOI
- https://doi.org/10.1038/s41467-021-26642-9
- Journal volume & issue
-
Vol. 13,
no. 1
pp. 1 – 11
Abstract
As cancer is increasingly considered a metabolic disorder, it is postulated that serum metabolite profiling can be a viable approach for detecting the presence of cancer. Here, the authors report a machine learning model using mass spectrometry-based liquid biopsy data for pan-cancer screening and classification.
