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Cancer biomarkers discovered using pan-cancer plasma proteomic profiling

Nature Biomedical Engineering volume 10, pages 16–38 (2026)Cite this article

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An Author Correction to this article was published on 22 September 2025

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Abstract

Mass-spectrometry-based proteomic data of tumour patient plasma samples present opportunities for improving cancer detection. Here we generate plasma proteomic profiles from 2,251 pan-cancer patient samples and investigate potential diagnostic biomarkers. Proteomic subtyping with different dominant tumour types links proteomic features and clinical indicators such as tumour stage. The highly immune-activated subtype, consisting of renal and bladder cancers, shows elevated glucose–insulin metabolism and reduced lipid metabolism. Comparison of the plasma proteome before and after surgery indicates that proteome patterns could be used to monitor post-surgery therapeutic effects. We also develop a binary classified model that distinguishes between tumour types and healthy controls, as well as a multicancer model for pan-cancer classification of proteins that could be useful biomarkers, and validate their performance in an independent cohort. In addition, we find that the plasma proteome, along with clinical indicators, whole blood cells and so on, can distinguish the pathological subtypes of specific tumour types. This study portrays a pan-cancer plasma proteomic landscape, providing information on plasma biomarkers that could help in discovering diagnostic opportunities.

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Fig. 1: Plasma proteomic analyses of pan-cancer specimens.
Fig. 2: Proteomic classification reveals heterogeneity of pan-cancer.
Fig. 3: Differential plasma proteomes of different physiological system groups.
Fig. 4: Identification of specific tumour-derived plasma proteins.
Fig. 5: Integration of tissue and plasma proteomes.
Fig. 6: Immune infiltration in pan-cancer tumours.
Fig. 7: Short-term changes in the plasma proteome after surgery.
Fig. 8: Proteomic classifier to predict tumour patients.

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Data availability

Proteome raw data have been deposited in the ProteomeXchange Consortium (dataset identifier: PXD051137) via the iProX partner repository (https://www.iprox.cn/) under Project ID: IPX0003227000 (ref. 57). The main data supporting the results in this study are available within the paper and its Supplementary Information. Source data are provided with this paper.

Code availability

All code for computational analyses were derived from publicly available websites and previous publications, and are cited in the corresponding Methods sections. The code used for this study is deposited in GitHub at https://doi.org/10.5281/zenodo.15179267 (ref. 58).

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Acknowledgements

This work was supported by the National Key Research and Development Program of China (2022YFA1303200 [C.D.], 2022YFA1303201 [C.D.] and 2023YFC2505900 [J.F.]), the National Natural Science Foundation of China (32330062 [C.D.], 31972933 [C.D.] and 82272166 [W.Y.]), sponsored by the Program of Shanghai Academic/Technology Research Leader (22XD1420100 [C.D.]), the Major Project of Special Development Funds of Zhangjiang National Independent Innovation Demonstration Zone (ZJ2019-ZD-004 [C.D.]), the Shanghai Municipal Science and Technology Major Project (2023SHZDZX02 [C.D.]), the Fudan Original Research Personalized Support Project [C.D.], Cultivation Project of Precision Medicine Joint Fund of Hebei Natural Science Foundation (H2022201044 [Y.J.]), Government-funded clinical talents Training Project of Hebei Province, Hebei Province ‘Three three Three Talent Project’ supported project (C20221002 [Y.J.]), the National Natural Science Foundation of China (82273792 [Y.J.]), Innovative team for precise care and rehabilitation of patients with cancer (IT2023C07 [Y.J.]), the Young Scientists Fund of the National Natural Science Foundation of China (32201215 [J.F.]), the National Ten Thousand Plan Young Top Talents [Y.Q.], the Natural Science Foundation of China (82172817 [Y.Q]), and the Shanghai ‘Science and Technology Innovation Action Plan’ medical innovation research project (22Y11905100 [Y.Q]). This work is supported by the Shanghai Municipal Science and Technology Major Project, the Human Phenome Data Center of Fudan university, and the Shanghai Phenomic precision measurement professional technical service platform (23DZ2290800).

Author information

Author notes

  1. These authors contributed equally: Lin Bai, Jiacheng Lyu, Jinwen Feng, Xiaoqiang Qiao, Yuanyuan Qu, Guojian Yang, Yuanxue Zhu, Lingxiao Liao.

Authors and Affiliations

  1. Clinical Research Center for Cell-based Immunotherapy of Shanghai Pudong Hospital, Fudan University Pudong Medical Center, State Key Laboratory of Genetics and Development of Complex Phenotypes, School of Life Sciences, Human Phenome Institute, Department of Urology, Fudan University Shanghai Cancer Center, Center for Cell and Gene Therapy, Fudan University, Shanghai, China

    Lin Bai, Jiacheng Lyu, Jinwen Feng, Yuanyuan Qu, Guojian Yang, Zeya Xu, Tao Ji, Peng Ran, Hailiang Zhang, Lingli Zhu, Sha Tian, Subei Tan, Yan Pu, Lingling Li, Dingwei Ye & Chen Ding

  2. Department of Medical Oncology, Affiliated Hospital of Hebei University, Hebei Key Laboratory of Cancer Radiotherapy and Chemotherapy, Baoding, Hebei, China

    Xiaoqiang Qiao, Yuanxue Zhu, Aimin Zang, Xiaofang Wang, Yumiao Li, Guofa Zhao, Zizheng Song, Jin Song & Youchao Jia

  3. College of Pharmaceutical Sciences, Key Laboratory of Medicinal Chemistry and Molecular Diagnosis, Ministry of Education, Hebei University, Baoding, China

    Xiaoqiang Qiao

  4. Department of Oncology, Shanghai Medical College, Shanghai Genitourinary Cancer Institute, Shanghai, China

    Yuanyuan Qu, Hailiang Zhang & Dingwei Ye

  5. Department of Neurosurgery, The Affiliated Nanhua Hospital, Hengyang Medical School, University of South China, Hengyang, China

    Lingxiao Liao, Wencong Ding & Yongshi Liao

  6. Hebei Normal University of Science and Technology, Qinhuangdao, China

    Hui Gao

  7. Clinical Lab, Affiliated Hospital of Hebei University, Baoding, Hebei, China

    Yan Wang

  8. Department of Hematology, Beijing Tongren Hospital, Capital Medical University, Beijing, China

    Liang Wang

  9. Department of Hepatological Surgery, Affiliated Hospital of Hebei University, Baoding, Hebei, China

    Jinghua Li

  10. CT/MRI room, Affiliated Hospital of Hebei University, Baoding, Hebei, China

    Xiaoping Yin

  11. College of Clinical Medicine, Hebei University, Baoding, China

    Dan Liu & Xiangpeng Gao

  12. Department of Medical Oncology, Affiliated Hospital of Hebei University, Baoding, China

    Dan Liu & Xiangpeng Gao

  13. Departments of Cancer Research Institute, Affiliated Cancer Hospital of Xinjiang Medical University, Xinjiang Key Laboratory of Translational Biomedical Engineering, Urumqi, China

    Wenjia Guo & Chen Ding

  14. Department of Pediatric Orthopedics, Spine Center, Xin Hua Hospital Affiliated to, Shanghai Jiao Tong University School of Medicine, Shanghai, China

    Wenjun Yang

Authors
  1. Lin Bai
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Contributions

C.D., Y.J., W.Y., Y.L., D.Y. and L.B. conceived and planned the project. H.G., A.Z., W.D., H.Z., L.Z., Y.W., L.W., X.W., Y.L., J.L., X.Y., G.Z., D.L., X.G., Z.S., J.S. and W.G. were responsible for samples and clinical information collection. L.B., Z.X., T.J., P.R., S. Tian, S. Tan, Y.P. and L. Li. contributed to sample preparation. L.B., J. Lyu, J.F. and G.Y. analysed the data and contributed to the interpretation of the results. C.D. took the lead in writing the paper. All authors provided critical feedback and helped shape the research, analysis and paper.

Corresponding authors

Correspondence to Yongshi Liao, Dingwei Ye, Wenjun Yang, Youchao Jia or Chen Ding.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–11.

Reporting Summary

Supplementary Data 1

Clinical characteristics of pan-cancer patients, and proteome data matrix used in the paper.

Supplementary Data 2

Plasma proteomic subtyping revealing heterogeneity of pan-cancer.

Supplementary Data 3

Differential plasma proteomes of different physiological system groups.

Supplementary Data 4

Specific plasma protein patterns distinguish different tumour types and subtypes.

Supplementary Data 5

Integration of tissue and plasma proteomes to nominate core tumour biomarkers.

Supplementary Data 6

Immune infiltration in pan-cancer tumours.

Supplementary Data 7

Short-term changes of the plasma proteome after surgery.

Supplementary Data 8

Machine learning-based model construction for binary and multitumour classification.

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Bai, L., Lyu, J., Feng, J. et al. Cancer biomarkers discovered using pan-cancer plasma proteomic profiling. Nat. Biomed. Eng 10, 16–38 (2026). https://doi.org/10.1038/s41551-025-01448-y

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