Proteomic characteristics reveal the signatures and the risks of T1 colorectal cancer metastasis to lymph nodes

  1. Aojia Zhuang
  2. Aobo Zhuang
  3. Yijiao Chen
  4. Zhaoyu Qin
  5. Dexiang Zhu
  6. Li Ren
  7. Ye Wei
  8. Pengyang Zhou
  9. Xuetong Yue
  10. Fuchu He  Is a corresponding author
  11. Jianming Xu  Is a corresponding author
  12. Chen Ding  Is a corresponding author
  1. Fudan University, China
  2. National Center for Protein Sciences, China

Abstract

The presence of lymph node metastasis (LNM) affects treatment strategy decisions in T1NxM0 colorectal cancer (CRC), but the currently used clinicopathological-based risk stratification cannot predict LNM accurately. In this study, we detected proteins in formalin-fixed paraffin-embedded (FFPE) tumor samples from 143 LNM-negative and 78 LNM-positive patients with T1 CRC and revealed changes in molecular and biological pathways by label-free liquid chromatography tandem mass spectrometry (LC-MS/MS) and established classifiers for predicting LNM in T1 CRC. An effective 55-proteins prediction model was built by machine learning and validated in a training cohort (N=132) and two validation cohorts (VC1, N=42; VC2, N=47), achieved an impressive AUC of 1.00 in the training cohort, 0.96 in VC1 and 0.93 in VC2, respectively. We further built a simplified classifier with 9 proteins, and achieved an AUC of 0.824. The simplified classifier was performed excellent in two external validation cohorts. The expression patterns of 13 proteins were confirmed by immunohistochemistry, and the IHC score of 5 proteins were used to build a IHC predict model with an AUC of 0.825. RHOT2 silence significantly enhanced migration and invasion of colon cancer cells. Our study explored the mechanism of metastasis in T1 CRC and can be used to facilitate the individualized prediction of LNM in patients with T1 CRC, which may provide a guidance for clinical practice in T1 CRC.

Data availability

All data generated or analyzed during this study are included in the manuscript and supporting file; Source Data files have been provided for all figures. The proteome raw data that support the findings of this study have been deposited to the ProteomeXchange Consortium (dataset identifier: PXD041476, https://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD041476) via the iProX partner repository (https://www.iprox.cn/) under Project ID IPX0003019000 at https://www.iprox.cn/page/project.html?id=IPX0003019000.

The following data sets were generated

Article and author information

Author details

  1. Aojia Zhuang

    School of Life Sciences, Fudan University, Shanghai, China
    Competing interests
    The authors declare that no competing interests exist.
  2. Aobo Zhuang

    School of Life Sciences, Fudan University, Shanghai, China
    Competing interests
    The authors declare that no competing interests exist.
  3. Yijiao Chen

    School of Life Sciences, Fudan University, Shanghai, China
    Competing interests
    The authors declare that no competing interests exist.
  4. Zhaoyu Qin

    School of Life Sciences, Fudan University, Shanghai, China
    Competing interests
    The authors declare that no competing interests exist.
  5. Dexiang Zhu

    School of Life Sciences, Fudan University, Shanghai, China
    Competing interests
    The authors declare that no competing interests exist.
  6. Li Ren

    School of Life Sciences, Fudan University, Shanghai, China
    Competing interests
    The authors declare that no competing interests exist.
  7. Ye Wei

    School of Life Sciences, Fudan University, Shanghai, China
    Competing interests
    The authors declare that no competing interests exist.
  8. Pengyang Zhou

    School of Life Sciences, Fudan University, Shanghai, China
    Competing interests
    The authors declare that no competing interests exist.
  9. Xuetong Yue

    School of Life Sciences, Fudan University, Shanghai, China
    Competing interests
    The authors declare that no competing interests exist.
  10. Fuchu He

    China State Key Laboratory of Proteomics, National Center for Protein Sciences, Beijing, China
    For correspondence
    hefc@nic.bmi.ac.cn
    Competing interests
    The authors declare that no competing interests exist.
  11. Jianming Xu

    School of Life Sciences, Fudan University, Shanghai, China
    For correspondence
    xujmin@aliyun.com
    Competing interests
    The authors declare that no competing interests exist.
  12. Chen Ding

    School of Life Sciences, Fudan University, Shanghai, China
    For correspondence
    chend@fudan.edu.cn
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8673-3464

Funding

National Key Research and Development Program of China

  • Chen Ding

Clinical Research Plan of SHDC

  • Jianming Xu

Program of Shanghai Academic Research Leader

  • Chen Ding

Shuguang Program og Shanghai Education Development Foundation and Shanghai Municipal Education Commission

  • Chen Ding

National Natural Science Foundation of China

  • Chen Ding

the Major Project of Special Development Funds of Zhangjiang National Independent Innovation Demonstration Zone

  • Chen Ding

Shanghai Municipal Science and Technology Major Project

  • Chen Ding

the Fudan original research personalized support project

  • Chen Ding

CAMS Innovation Fund for Medical Sciences

  • Fuchu He

Shanghai Science and Technology Committee Project

  • Jianming Xu

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Human subjects: The present study was carried out comply with the ethical standards of Helsinki Declaration II and approved by the Institution Review Board of Fudan University Zhongshan Hospital (B2019-166).

Copyright

© 2023, Zhuang et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Aojia Zhuang
  2. Aobo Zhuang
  3. Yijiao Chen
  4. Zhaoyu Qin
  5. Dexiang Zhu
  6. Li Ren
  7. Ye Wei
  8. Pengyang Zhou
  9. Xuetong Yue
  10. Fuchu He
  11. Jianming Xu
  12. Chen Ding
(2023)
Proteomic characteristics reveal the signatures and the risks of T1 colorectal cancer metastasis to lymph nodes
eLife 12:e82959.
https://doi.org/10.7554/eLife.82959

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https://doi.org/10.7554/eLife.82959

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