Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

A sustainable approach to universal metabolic cancer diagnosis

Nature Sustainability (2024)Cite this article

Subjects

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%β€”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.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

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.

Similar content being viewed by others

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.

References

  1. Global Atlas of Medical Devices 2022 (WHO, 2022); https://www.who.int/publications/i/item/9789240062207

  2. Cancer Stat Facts: Colorectal Cancer (National Cancer Institute, 2020); https://seer.cancer.gov/statfacts/html/colorect.html

  3. Bar, N. et al. A reference map of potential determinants for the human serum metabolome. Nature 588, 135–140 (2020).

    ArticleΒ  Google ScholarΒ 

  4. Ricotti, V. et al. Wearable full-body motion tracking of activities of daily living predicts disease trajectory in Duchenne muscular dystrophy. Nat. Med. 29, 95–103 (2023).

    ArticleΒ  CASΒ  Google ScholarΒ 

  5. Chen, W. et al. Early detection of visual impairment in young children using a smartphone-based deep learning system. Nat. Med. 29, 493–503 (2023).

    ArticleΒ  CASΒ  Google ScholarΒ 

  6. CarreΓ±o, J. M. et al. Activity of convalescent and vaccine serum against SARS-CoV-2 Omicron. Nature 602, 682–688 (2022).

    ArticleΒ  Google ScholarΒ 

  7. Adhikari, A. N. et al. The role of exome sequencing in newborn screening for inborn errors of metabolism. Nat. Med. 26, 1392–1397 (2020).

    ArticleΒ  CASΒ  Google ScholarΒ 

  8. Cohen, J. D. et al. Detection and localization of surgically resectable cancers with a multi-analyte blood test. Science 359, 926–930 (2018).

    ArticleΒ  CASΒ  Google ScholarΒ 

  9. Meng, F. et al. Ratiometric electrochemical OR gate assay for NSCLC-derived exosomes. J. Nanobiotechnol. 21, 104 (2023).

    ArticleΒ  CASΒ  Google ScholarΒ 

  10. Newborn Screening Laboratory Bulletin (Centers for Disease Control and Prevention, 2014); https://archive.cdc.gov/www_cdc_gov/nbslabbulletin/bulletin_next_generation.html

  11. Organization, W. H. Every Newborn Progress Report 2019 (WHO, 2020); https://www.who.int/publications/m/item/every-newborn-progress-report-2019

  12. Huang, L. et al. A multifunctional platinum nanoreactor for point-of-care metabolic analysis. Matter 1, 1669–1680 (2019).

    ArticleΒ  Google ScholarΒ 

  13. Tsesses, S. et al. Tunable photon-induced spatial modulation of free electrons. Nat. Mater. 22, 345–352 (2023).

    ArticleΒ  CASΒ  Google ScholarΒ 

  14. Nachtigall, F. M., Pereira, A., Trofymchuk, O. S. & Santos, L. S. Detection of SARS-CoV-2 in nasal swabs using MALDI-MS. Nat. Biotechnol. 38, 1168–1173 (2020).

    ArticleΒ  CASΒ  Google ScholarΒ 

  15. Yin, Q. et al. A TLR7-nanoparticle adjuvant promotes a broad immune response against heterologous strains of influenza and SARS-CoV-2. Nat. Mater. 22, 380–390 (2023).

    CASΒ  Google ScholarΒ 

  16. Chen, H., Huang, C., Wu, Y., Sun, N. & Deng, C. Exosome metabolic patterns on aptamer-coupled polymorphic carbon for precise detection of early gastric cancer. ACS Nano 16, 12952–12963 (2022).

    ArticleΒ  CASΒ  Google ScholarΒ 

  17. Unger, M. S., Blank, M., Enzlein, T. & Hopf, C. Label-free cell assays to determine compound uptake or drug action using MALDI-TOF mass spectrometry. Nat. Protoc. 16, 5533–5558 (2021).

    ArticleΒ  CASΒ  Google ScholarΒ 

  18. Montefusco, L. et al. Acute and long-term disruption of glycometabolic control after SARS-CoV-2 infection. Nat. Metab. 3, 774–785 (2021).

    ArticleΒ  CASΒ  Google ScholarΒ 

  19. Chen, Y. et al. Plasma metabolic fingerprints for large-scale screening and personalized risk stratification of metabolic syndrome. Cell Rep. Med. 4, 101109 (2023).

    ArticleΒ  CASΒ  Google ScholarΒ 

  20. Li, X. et al. Circulating metabolite homeostasis achieved through mass action. Nat. Metab. 4, 141–152 (2022).

    ArticleΒ  CASΒ  Google ScholarΒ 

  21. Wang, J. et al. New insights into the structure–performance relationships of mesoporous materials in analytical science. Chem. Soc. Rev. 47, 8766–8803 (2018).

    ArticleΒ  CASΒ  Google ScholarΒ 

  22. Xu, Z. et al. Efficient plasma metabolic fingerprinting as a novel tool for diagnosis and prognosis of gastric cancer: a large-scale, multicentre study. Gut 72, 2051–2067 (2023).

    ArticleΒ  CASΒ  Google ScholarΒ 

  23. Beckonert, O. et al. Metabolic profiling, metabolomic and metabonomic procedures for NMR spectroscopy of urine, plasma, serum and tissue extracts. Nat. Protoc. 2, 2692–2703 (2007).

    ArticleΒ  CASΒ  Google ScholarΒ 

  24. Morin, S., Bazarova, N., Jacon, P. & Vella, S. The manufacturers’ perspective on World Health Organization prequalification of in vitro diagnostics. Clin. Infect. Dis. 66, 301–305 (2018).

    ArticleΒ  Google ScholarΒ 

  25. Meng, L. et al. Development of an automatic ultrasonic matrix sprayer for matrix-assisted laser desorption/ionization mass spectrometry imaging. Anal. Chem. 94, 6457 (2022).

    ArticleΒ  CASΒ  Google ScholarΒ 

  26. Harvey, D. J. Analysis of carbohydrates and glycoconjugates by matrix‐assisted laser desorption/ionization mass spectrometry: an update for 2019–2020. Mass Spectrom. Rev. 42, 1984 (2023).

    ArticleΒ  CASΒ  Google ScholarΒ 

  27. Zhang, M. et al. Ultra‐fast label‐free serum metabolic diagnosis of coronary heart disease via a deep stabilizer. Adv. Sci. 8, 2101333 (2021).

    ArticleΒ  CASΒ  Google ScholarΒ 

  28. Dominique, N. L. et al. Giving gold wings: ultrabright and fragmentation free mass spectrometry reporters for barcoding, bioconjugation monitoring, and data storage. Angew. Chem. Int. Ed. Engl. 62, e202219182 (2023).

    ArticleΒ  CASΒ  Google ScholarΒ 

  29. Guan, M. et al. Silver nanoparticles as matrix for MALDI FTICR MS profiling and imaging of diverse lipids in brain. Talanta 179, 624–631 (2018).

    ArticleΒ  Google ScholarΒ 

  30. BastΓΊs, N. G., MerkoΓ§i, F., Piella, J. & Puntes, V. Synthesis of highly monodisperse citrate-stabilized silver nanoparticles of up to 200 nm: kinetic control and catalytic properties. Chem. Mater. 26, 2836–2846 (2014).

    ArticleΒ  Google ScholarΒ 

  31. Yagnik, G. B. et al. Large scale nanoparticle screening for small molecule analysis in laser desorption ionization mass spectrometry. Anal. Chem. 88, 8926–8930 (2016).

    ArticleΒ  CASΒ  Google ScholarΒ 

  32. Chen, W. et al. Comprehensive metabolic fingerprints characterize neuromyelitis optica spectrum disorder by nanoparticle-enhanced laser desorption/ionization mass spectrometry. ACS Nano 17, 19779–19792 (2023).

    ArticleΒ  CASΒ  Google ScholarΒ 

  33. MΓΌller, W. H., Verdin, A., De Pauw, E., Malherbe, C. & Eppe, G. Surface-assisted laser desorption/ionization mass spectrometry imaging: a review. Mass Spectrom. Rev. 41, 373–420 (2022).

    ArticleΒ  Google ScholarΒ 

  34. Prysiazhnyi, V. et al. Fate of gold nanoparticles in laser desorption/ionization mass spectrometry: toward the imaging of individual nanoparticles. J. Am. Soc. Mass Spectrom. 34, 570–578 (2023).

    ArticleΒ  Google ScholarΒ 

  35. Poulos, R. C. et al. Strategies to enable large-scale proteomics for reproducible research. Nat. Commun. 11, 3793 (2020).

    ArticleΒ  CASΒ  Google ScholarΒ 

  36. Cajka, T. & Fiehn, O. Comprehensive analysis of lipids in biological systems by liquid chromatography-mass spectrometry. Trends Analyt. Chem. 61, 192–206 (2014).

    ArticleΒ  Google ScholarΒ 

  37. Saini, R. K., Prasad, P., Shang, X. & Keum, Y.-S. Advances in lipid extraction methodsβ€”a review. Int. J. Mol. Sci. 22, 13643 (2021).

    ArticleΒ  CASΒ  Google ScholarΒ 

  38. Yang, Y. et al. Solubility of benzoin in three binary solvent mixtures and investigation of intermolecular interactions by molecular dynamic simulation. J. Mol. Liq. 243, 472–483 (2017).

    ArticleΒ  Google ScholarΒ 

  39. Adeboyejo, K. et al. Simultaneous determination of HCV genotype and NS5B resistance associated substitutions using dried serum spots from SΓ£o Paulo state, Brazil. Access Microbiol. 4, 326 (2022).

    ArticleΒ  CASΒ  Google ScholarΒ 

  40. Okai, C. A. et al. Profiling of intact blood proteins by matrix‐assisted laser desorption/ionization mass spectrometry without the need for freezing – dried serum spots as future clinical tools for patient screening. Rapid Commun. Mass Spectrom. 35, e9121 (2021).

    ArticleΒ  CASΒ  Google ScholarΒ 

  41. Verstraete, J. & Stove, C. Patient-centric assessment of thiamine status in dried blood volumetric absorptive microsamples using LC–MS/MS analysis. Anal. Chem. 93, 2660–2668 (2021).

    ArticleΒ  CASΒ  Google ScholarΒ 

  42. Platt, F. M. Emptying the stores: lysosomal diseases and therapeutic strategies. Nat. Rev. Drug Discov. 17, 133–150 (2018).

    ArticleΒ  CASΒ  Google ScholarΒ 

  43. Shan, D. et al. N-protein presents early in blood, dried blood and saliva during asymptomatic and symptomatic SARS-CoV-2 infection. Nat. Commun. 12, 1931 (2021).

    ArticleΒ  Google ScholarΒ 

  44. Wang, G. X. et al. Metabolic detection and systems analyses of pancreatic ductal adenocarcinoma through machine learning, lipidomics, and multi-omics. Sci. Adv. 7, eabh2724 (2021).

    ArticleΒ  Google ScholarΒ 

  45. Su, H. et al. Plasmonic alloys reveal a distinct metabolic phenotype of early gastric cancer. Adv. Mater. 33, 2007978 (2021).

    ArticleΒ  CASΒ  Google ScholarΒ 

  46. Pang, Z. et al. Using MetaboAnalyst 5.0 for LC HRMS spectra processing, multi-omics integration and covariate adjustment of global metabolomics data. Nat. Protoc. 17, 1735–1761 (2022).

    ArticleΒ  CASΒ  Google ScholarΒ 

  47. Zhang, Z. et al. DHHC9-mediated GLUT1 S-palmitoylation promotes glioblastoma glycolysis and tumorigenesis. Nat. Commun. 12, 5872 (2021).

    ArticleΒ  CASΒ  Google ScholarΒ 

  48. Galstyan, A. et al. Blood–brain barrier permeable nano immunoconjugates induce local immune responses for glioma therapy. Nat. Commun. 10, 3850 (2019).

    ArticleΒ  CASΒ  Google ScholarΒ 

  49. Taibl, K. R. et al. Newborn metabolomic signatures of maternal per- and polyfluoroalkyl substance exposure and reduced length of gestation. Nat. Commun. 14, 3120 (2023).

    ArticleΒ  CASΒ  Google ScholarΒ 

  50. Jiang, X. et al. Development of a bile acid-based newborn screen for Niemann–Pick disease type C. Sci. Transl. Med. 8, 337ra63 (2016).

    ArticleΒ  Google ScholarΒ 

  51. Pickhardt, P. J., Hassan, C., Halligan, S. & Marmo, R. Colorectal cancer: CT colonography and colonoscopy for detectionβ€”systematic review and meta-analysis. Radiology 259, 393–405 (2011).

    ArticleΒ  Google ScholarΒ 

  52. Duarte, R. B. et al. Computed tomography colonography versus colonoscopy for the diagnosis of colorectal cancer: a systematic review and meta-analysis. Ther. Clin. Risk Manage. 14, 349–360 (2018).

    ArticleΒ  Google ScholarΒ 

  53. Chen, H. et al. Participation and yield of a population-based colorectal cancer screening programme in China. Gut 68, 1450–1457 (2019).

    ArticleΒ  Google ScholarΒ 

  54. Zheng, R. et al. Cancer incidence and mortality in China, 2016. J. Natl Cancer Cent. 2, 1–9 (2022).

    ArticleΒ  Google ScholarΒ 

  55. Bokhorst, L. P. et al. Compliance rates with the prostate cancer research international active surveillance (PRIAS) protocol and disease reclassification in noncompliers. Eur. Urol. 68, 814–821 (2015).

    ArticleΒ  Google ScholarΒ 

  56. Alferink, L. J. M. et al. Microbiomics, metabolomics, predicted metagenomics, and hepatic steatosis in a population-based study of 1,355 adults. Hepatology 73, 968–982 (2021).

    ArticleΒ  CASΒ  Google ScholarΒ 

  57. May, T. et al. One-carbon metabolism in children with marasmus and kwashiorkor. EBioMedicine 75, 103791 (2022).

    ArticleΒ  CASΒ  Google ScholarΒ 

  58. Yin, C. et al. Ultrabroadband photodetectors up to 10.6 Β΅m based on 2D Fe3O4 nanosheets. Adv. Mater. 32, 2002237 (2020).

    ArticleΒ  CASΒ  Google ScholarΒ 

  59. Park, H. et al. Magnetite nanoparticles as efficient materials for removal of glyphosate from water. Nat. Sustain. 3, 129–135 (2019).

    ArticleΒ  Google ScholarΒ 

  60. Huang, L. et al. Machine learning of serum metabolic patterns encodes early-stage lung adenocarcinoma. Nat. Commun. 11, 3556 (2020).

    ArticleΒ  CASΒ  Google ScholarΒ 

  61. RΓ­os-Ocampo, J. P. et al. Thermal districts in Colombia: developing a methodology to estimate the cooling potential demand. Renew. Sustain. Energy Rev. 165, 112612 (2022).

    ArticleΒ  Google ScholarΒ 

  62. Letang, E. et al. Minimally invasive tissue sampling: a tool to guide efforts to reduce AIDS-related mortality in resource-limited settings. Clin. Infect. Dis. 73, 343–350 (2021).

    ArticleΒ  Google ScholarΒ 

  63. Buscail, L., Bournet, B. & Cordelier, P. Role of oncogenic KRAS in the diagnosis, prognosis and treatment of pancreatic cancer. Nat. Rev. Gastroenterol. Hepatol. 17, 153–168 (2020).

    ArticleΒ  CASΒ  Google ScholarΒ 

  64. Sun, Y. et al. Noninvasive urinary protein signatures associated with colorectal cancer diagnosis and metastasis. Nat. Commun. 13, 2757 (2022).

    ArticleΒ  CASΒ  Google ScholarΒ 

  65. Guo, X. et al. Circulating exosomal gastric cancer-associated long noncoding RNA1 as a biomarker for early detection and monitoring progression of gastric cancer: a multiphase study. JAMA Surg. 155, 572–579 (2020).

    ArticleΒ  Google ScholarΒ 

  66. Verstraete, J., Boffel, L. & Stove, C. Dried blood microsample-assisted determination of vitamins: recent developments and challenges. Trends Analyt. Chem. 132, 116057 (2020).

    ArticleΒ  CASΒ  Google ScholarΒ 

  67. Ng, A. H. C. et al. A digital microfluidic system for serological immunoassays in remote settings. Sci. Transl. Med. 10, eaar6076 (2018).

    ArticleΒ  Google ScholarΒ 

  68. Kvaskoff, D. et al. Minimizing matrix effects for the accurate quantification of 25-hydroxyvitamin D metabolites in dried blood spots by LC–MS/MS. Clin. Chem. 62, 639–646 (2016).

    ArticleΒ  CASΒ  Google ScholarΒ 

  69. Sammut, S.-J. et al. Multi-omic machine learning predictor of breast cancer therapy response. Nature 601, 623–629 (2022).

    ArticleΒ  CASΒ  Google ScholarΒ 

  70. Myszczynska, M. A. et al. Applications of machine learning to diagnosis and treatment of neurodegenerative diseases. Nat. Rev. Neurol. 16, 440–456 (2020).

    ArticleΒ  Google ScholarΒ 

  71. Dufresne, M., Patterson, N. H., Norris, J. L. & Caprioli, R. M. Combining salt doping and matrix sublimation for high spatial resolution MALDI imaging mass spectrometry of neutral lipids. Anal. Chem. 91, 12928–12934 (2019).

    ArticleΒ  CASΒ  Google ScholarΒ 

  72. Szunerits, S. et al. The impact of chemical engineering and technological advances on managing diabetes: present and future concepts. Chem. Soc. Rev. 50, 2102–2146 (2021).

    ArticleΒ  CASΒ  Google ScholarΒ 

  73. Sang, L. et al. Mitochondrial long non-coding RNA GAS5 tunes TCA metabolism in response to nutrient stress. Nat. Metab. 3, 90–106 (2021).

    ArticleΒ  CASΒ  Google ScholarΒ 

  74. Li, W.-Q. et al. Beneficial effects of endoscopic screening on gastric cancer and optimal screening interval: a population-based study. Endoscopy 54, 848–858 (2022).

    ArticleΒ  Google ScholarΒ 

  75. Cai, Q. et al. Development and validation of a prediction rule for estimating gastric cancer risk in the Chinese high-risk population: a nationwide multicentre study. Gut 68, 1576–1587 (2019).

    ArticleΒ  CASΒ  Google ScholarΒ 

  76. Chen, R. et al. Effectiveness of one-time endoscopic screening programme in prevention of upper gastrointestinal cancer in China: a multicentre population-based cohort study. Gut 70, 251–260 (2021).

    Google ScholarΒ 

  77. Zhang, T. et al. Changing trends of disease burden of gastric cancer in China from 1990 to 2019 and its predictions: findings from global burden of disease study. Chin. J. Cancer Res. 33, 11–26 (2021).

    ArticleΒ  CASΒ  Google ScholarΒ 

  78. Jin, G. et al. Genetic risk, incident gastric cancer, and healthy lifestyle: a meta-analysis of genome-wide association studies and prospective cohort study. Lancet Oncol. 21, 1378–1386 (2020).

    ArticleΒ  CASΒ  Google ScholarΒ 

  79. Xia, R. et al. Estimated cost-effectiveness of endoscopic screening for upper gastrointestinal tract cancer in high-risk areas in China. JAMA Netw. Open 4, e2121403 (2021).

    ArticleΒ  Google ScholarΒ 

  80. Canto, M. I. et al. Frequent detection of pancreatic lesions in asymptomatic high-risk individuals. Gastroenterology 142, 796–804 (2012).

    ArticleΒ  Google ScholarΒ 

  81. Pereira, S. P. et al. Early detection of pancreatic cancer. Lancet Gastroenterol. Hepatol. 5, 698–710 (2020).

    ArticleΒ  Google ScholarΒ 

  82. US Preventive Services Task Force et al. Screening for pancreatic cancer: US preventive services task force reaffirmation recommendation statement. JAMA 322, 438–444 (2019).

    ArticleΒ  Google ScholarΒ 

  83. Godec, P. et al. Democratized image analytics by visual programming through integration of deep models and small-scale machine learning. Nat. Commun. 10, 4551 (2019).

    ArticleΒ  Google ScholarΒ 

Download references

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.

Author information

Author notes

  1. These authors contributed equally: Ruimin Wang, Shouzhi Yang.

Authors and Affiliations

  1. Department of Clinical Laboratory Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, P. R. China

    Ruimin Wang,Β Shouzhi Yang,Β Mengfei Wang,Β Yan Zhou,Β Xvelian Li,Β Jiayi WangΒ &Β Lin Huang

  2. Shanghai Institute of Thoracic Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, P. R. China

    Ruimin Wang,Β Shouzhi Yang,Β Mengfei Wang,Β Yan Zhou,Β Xvelian Li,Β Jiayi WangΒ &Β Lin Huang

  3. State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Institute of Medical Robotics and Shanghai Academy of Experimental Medicine, Shanghai Jiao Tong University, Shanghai, P. R. China

    Ruimin Wang,Β Shouzhi Yang,Β Wei Chen,Β Wanshan Liu,Β Yida Huang,Β Jiao Wu,Β Jing CaoΒ &Β Kun Qian

  4. Instrumental Analysis Center, Shanghai Jiao Tong University, Shanghai, P. R. China

    Lei Feng

  5. School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, P. R. China

    Jingjing Wan

Authors
  1. Ruimin Wang
    View author publications

    You can also search for this author in PubMedΒ Google Scholar

  2. Shouzhi Yang
    View author publications

    You can also search for this author in PubMedΒ Google Scholar

  3. Mengfei Wang
    View author publications

    You can also search for this author in PubMedΒ Google Scholar

  4. Yan Zhou
    View author publications

    You can also search for this author in PubMedΒ Google Scholar

  5. Xvelian Li
    View author publications

    You can also search for this author in PubMedΒ Google Scholar

  6. Wei Chen
    View author publications

    You can also search for this author in PubMedΒ Google Scholar

  7. Wanshan Liu
    View author publications

    You can also search for this author in PubMedΒ Google Scholar

  8. Yida Huang
    View author publications

    You can also search for this author in PubMedΒ Google Scholar

  9. Jiao Wu
    View author publications

    You can also search for this author in PubMedΒ Google Scholar

  10. Jing Cao
    View author publications

    You can also search for this author in PubMedΒ Google Scholar

  11. Lei Feng
    View author publications

    You can also search for this author in PubMedΒ Google Scholar

  12. Jingjing Wan
    View author publications

    You can also search for this author in PubMedΒ Google Scholar

  13. Jiayi Wang
    View author publications

    You can also search for this author in PubMedΒ Google Scholar

  14. Lin Huang
    View author publications

    You can also search for this author in PubMedΒ Google Scholar

  15. Kun Qian
    View author publications

    You can also search for this author in PubMedΒ Google Scholar

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.

Ethics declarations

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.

Peer review

Peer review information

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

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Figs. 1–18 and Tables 1–10.

Reporting Summary

Supplementary Data 1

Source data for supplementary figures.

Source data

Source Data Fig. 1

Statistical source data.

Source Data Fig. 2

Statistical source data.

Source Data Fig. 3

Statistical source data.

Source Data Fig. 4

Statistical source data.

Source Data Fig. 5

Statistical source data.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41893-024-01323-9

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter β€” what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing