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Proteomics-driven noninvasive screening of circulating serum protein panels for the early diagnosis of hepatocellular carcinoma

Author

Listed:
  • Xiaohua Xing

    (Mengchao Hepatobiliary Hospital of Fujian Medical University)

  • Linsheng Cai

    (Mengchao Hepatobiliary Hospital of Fujian Medical University
    Clinical Oncology School of Fujian Medical University)

  • Jiahe Ouyang

    (Mengchao Hepatobiliary Hospital of Fujian Medical University)

  • Fei Wang

    (Mengchao Hepatobiliary Hospital of Fujian Medical University)

  • Zongman Li

    (Mengchao Hepatobiliary Hospital of Fujian Medical University)

  • Mingxin Liu

    (Mengchao Hepatobiliary Hospital of Fujian Medical University)

  • Yingchao Wang

    (Mengchao Hepatobiliary Hospital of Fujian Medical University)

  • Yang Zhou

    (Mengchao Hepatobiliary Hospital of Fujian Medical University)

  • En Hu

    (Mengchao Hepatobiliary Hospital of Fujian Medical University)

  • Changli Huang

    (Clinical Oncology School of Fujian Medical University)

  • Liming Wu

    (Mengchao Hepatobiliary Hospital of Fujian Medical University
    Zhejiang University)

  • Jingfeng Liu

    (Clinical Oncology School of Fujian Medical University)

  • Xiaolong Liu

    (Mengchao Hepatobiliary Hospital of Fujian Medical University)

Abstract

Early diagnosis of hepatocellular carcinoma (HCC) lacks highly sensitive and specific protein biomarkers. Here, we describe a staged mass spectrometry (MS)-based discovery-verification-validation proteomics workflow to explore serum proteomic biomarkers for HCC early diagnosis in 1002 individuals. Machine learning model determined as P4 panel (HABP2, CD163, AFP and PIVKA-II) clearly distinguish HCC from liver cirrhosis (LC, AUC 0.979, sensitivity 0.925, specificity 0.915) and healthy individuals (HC, AUC 0.992, sensitivity 0.975, specificity 1.000) in an independent validation cohort, outperforming existing clinical prediction strategies. Furthermore, the P4 panel can accurately predict LC to HCC conversion (AUC 0.890, sensitivity 0.909, specificity 0.877) with predicting HCC at a median of 11.4 months prior to imaging in prospective external validation cohorts (No.: Keshen 2018_005_02 and NCT03588442). These results suggest that proteomics-driven serum biomarker discovery provides a valuable reference for the liquid biopsy, and has great potential to improve early diagnosis of HCC.

Suggested Citation

  • Xiaohua Xing & Linsheng Cai & Jiahe Ouyang & Fei Wang & Zongman Li & Mingxin Liu & Yingchao Wang & Yang Zhou & En Hu & Changli Huang & Liming Wu & Jingfeng Liu & Xiaolong Liu, 2023. "Proteomics-driven noninvasive screening of circulating serum protein panels for the early diagnosis of hepatocellular carcinoma," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-44255-2
    DOI: 10.1038/s41467-023-44255-2
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    References listed on IDEAS

    as
    1. Yulin Sun & Zhengguang Guo & Xiaoyan Liu & Lijun Yang & Zongpan Jing & Meng Cai & Zhaoxu Zheng & Chen Shao & Yefan Zhang & Haidan Sun & Li Wang & Minjie Wang & Jun Li & Lusong Tian & Yue Han & Shuangm, 2022. "Noninvasive urinary protein signatures associated with colorectal cancer diagnosis and metastasis," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    2. Lin Huang & Lin Wang & Xiaomeng Hu & Sen Chen & Yunwen Tao & Haiyang Su & Jing Yang & Wei Xu & Vadanasundari Vedarethinam & Shu Wu & Bin Liu & Xinze Wan & Jiatao Lou & Qian Wang & Kun Qian, 2020. "Machine learning of serum metabolic patterns encodes early-stage lung adenocarcinoma," Nature Communications, Nature, vol. 11(1), pages 1-11, December.
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