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A sustainable approach to universal metabolic cancer diagnosis

Author

Listed:
  • Ruimin Wang

    (Shanghai Jiao Tong University
    Shanghai Jiao Tong University
    Shanghai Jiao Tong University)

  • Shouzhi Yang

    (Shanghai Jiao Tong University
    Shanghai Jiao Tong University
    Shanghai Jiao Tong University)

  • Mengfei Wang

    (Shanghai Jiao Tong University
    Shanghai Jiao Tong University)

  • Yan Zhou

    (Shanghai Jiao Tong University
    Shanghai Jiao Tong University)

  • Xvelian Li

    (Shanghai Jiao Tong University
    Shanghai Jiao Tong University)

  • Wei Chen

    (Shanghai Jiao Tong University)

  • Wanshan Liu

    (Shanghai Jiao Tong University)

  • Yida Huang

    (Shanghai Jiao Tong University)

  • Jiao Wu

    (Shanghai Jiao Tong University)

  • Jing Cao

    (Shanghai Jiao Tong University)

  • Lei Feng

    (Shanghai Jiao Tong University)

  • Jingjing Wan

    (East China Normal University)

  • Jiayi Wang

    (Shanghai Jiao Tong University
    Shanghai Jiao Tong University)

  • Lin Huang

    (Shanghai Jiao Tong University
    Shanghai Jiao Tong University)

  • Kun Qian

    (Shanghai Jiao Tong University)

Abstract

Over a billion people across the world experience a high rate of missed disease diagnosis, an issue that highlights the need for diagnostic tools showing increased accuracy and affordability. In addition, such tools could be used in ecologically fragile and energy-limited regions, pointing to the need for developing solutions that can maximize health gains under limited resources for enhanced sustainability. Metabolic diagnosis holds promise but faces challenges due to the applicability of biospecimens and limited robustness of analytical tools. Here we present a diagnostic method coupling dried serum spots (DSS) and nanoparticle-enhanced laser desorption/ionization mass spectrometry (NPELDI MS). Our approach allows diagnosis of multiple cancers within minutes at affordable cost, environmental friendliness, serum-equivalent precision and user-friendly protocol. Our assessment shows that the implementation of this tool in less-developed regions could reduce the estimated proportion of undiagnosed cases of colorectal cancer from 84.30% to 29.20%, gastric cancer from 77.57% to 57.22% and pancreatic cancer from 34.56% to 9.30%—an overall reduction in the range of 20.35–55.10%. This work provides insights into delivering more sustainable metabolic diagnosis with maximum health gains.

Suggested Citation

  • Ruimin Wang & Shouzhi Yang & Mengfei Wang & Yan Zhou & Xvelian Li & Wei Chen & Wanshan Liu & Yida Huang & Jiao Wu & Jing Cao & Lei Feng & Jingjing Wan & Jiayi Wang & Lin Huang & Kun Qian, 2024. "A sustainable approach to universal metabolic cancer diagnosis," Nature Sustainability, Nature, vol. 7(5), pages 602-615, May.
  • Handle: RePEc:nat:natsus:v:7:y:2024:i:5:d:10.1038_s41893-024-01323-9
    DOI: 10.1038/s41893-024-01323-9
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