A sustainable approach to universal metabolic cancer diagnosis

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 () and Kun Qian ()
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Ruimin Wang: Shanghai Jiao Tong University
Shouzhi Yang: Shanghai Jiao Tong University
Mengfei Wang: Shanghai Jiao Tong University
Yan Zhou: Shanghai Jiao Tong University
Xvelian Li: 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
Lin Huang: Shanghai Jiao Tong University
Kun Qian: Shanghai Jiao Tong University

Nature Sustainability, 2024, vol. 7, issue 5, 602-615

Abstract: 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.

Date: 2024
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DOI: 10.1038/s41893-024-01323-9

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