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A sustainable approach to universal metabolic cancer diagnosis

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.

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Fig. 1: NPELDI MS platform.
Fig. 2: Feasibility analysis.
Fig. 3: Applicability to real-world cases.
Fig. 4: Validation via cancers diagnosis.
Fig. 5: Economic assessment.

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Data availability

The verification of the metabolites in this study was achieved by comparing the m/z features with the human metabolome database (HMDB, http://www.hmdb.ca/). The data that support the findings of this study are available within the Article and its Supplementary Information or from the corresponding author upon reasonable request. Source data are provided with this paper.

Code availability

The computer codes utilized during the current study are available from the corresponding author upon reasonable request, due to competing financial interests.

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Acknowledgements

We acknowledge financial support from the National Key R&D Program of China (Nos. 2021YFF0703500 (K.Q.), 2022YFE0103500 (K.Q.), 2022YFC2502800 (J. Wan)), the Medical-Engineering Joint Funds of Shanghai Jiao Tong University (Nos. YG2024ZD07 and YG2023ZD08 (K.Q.)), the National Natural Science Funds (Grant Nos. 82372148 (L.H.), 81971771 (K.Q.), 22074044 (J. Wan), 22122404 (J. Wan)) and the Shanghai Institutions of Higher Learning (No. 2021-01-07-00-02-E00083 (K.Q.)). This work was also sponsored by the Innovative Research Team of High-Level Local Universities in Shanghai (SHSMU-ZDCX20210700 (K.Q.)), the Innovation Group Project of Shanghai Municipal Health Commission (2019CXJQ03 (K.Q.)), the Innovation Research Plan by the Shanghai Municipal Education Commission (ZXWF082101 (K.Q.)), the National Research Center for Translational Medicine Shanghai (Nos. TMSK-2021-124 and NRCTM(SH)-2021-06 (K.Q.)), the Science and Technology Commission of Shanghai Municipality (20DZ2220400 (K.Q.)) and Fundamental Research Funds for the Central Universities.

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Authors

Contributions

J. Wan, J. Wang, L.H. and K.Q. conceived the study. R.W. and S.Y. carried out the study and wrote the initial article. M.W. and S.Y. constructed the disease model and calculated the missed diagnosis rate. R.W., Y.Z., X.L., W.C. and W.L. analysed the MS data with help from Y.H., J. Wu, J.C. and L.F. All authors provided comments and contributed to the final version of the paper.

Corresponding authors

Correspondence to Jingjing Wan, Jiayi Wang, Lin Huang or Kun Qian.

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Competing interests

R.W., S.Y., L.H. and K.Q. have filed patents (CN116519778A, China, 2023) for both the technology and the use of the technology to detect bio-samples. These patents are owned and managed by Shanghai Jiao Tong University. The other authors declare no competing interests.

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Nature Sustainability thanks Carsten Hopf and Jian Liu for their contribution to the peer review of this work.

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Wang, R., Yang, S., Wang, M. et al. A sustainable approach to universal metabolic cancer diagnosis. Nat Sustain 7, 602–615 (2024). https://doi.org/10.1038/s41893-024-01323-9

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