Journal
ADVANCED MATERIALS
Volume 34, Issue 26, Pages -Publisher
WILEY-V C H VERLAG GMBH
DOI: 10.1002/adma.202201422
Keywords
cancer; diagnostics; mass spectrometry; metabolites; metal-organic frameworks
Categories
Funding
- NSFC [81971771, 22033002]
- MOST [2022YFE0103500, 2021YFF0703500, 2021YFA0910100, 2018YFC1312800, 2017YFE0124400]
- Shanghai Institutions of Higher Learning [2021-01-07-00-02-E00083]
- Major Science and Technology Project of Precious Metal Materials Genome Engineering in Yunnan Province [2019ZE001-1, 202002AB080001-6]
- Shanghai RisingStar Programme [19QA1404800]
- Innovation Group Project of Shanghai Municipal Health Commission [2019CXJQ03]
- Shanghai Municipal Education Commission [ZXWF082101]
- National Research Center for Translational Medicine Shanghai [TMSK2021-124, NRCTM(SH)-2021-06]
- Medical-Engineering Joint Funds of Shanghai Jiao Tong University [YG2019QNA44, YG2021ZD09, YG2022QN107]
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This study developed a precisely engineered metal-organic framework for enhancing the signal of molecule biomarkers, achieving high-accuracy diagnosis of major cancers.
Cancers heavily threaten human life; therefore, a high-accuracy diagnosis is vital to protect human beings from the suffering of cancers. While biopsies and imaging methods are widely used as current technologies for cancer diagnosis, a new detection platform by metabolic analysis is expected due to the significant advantages of fast, simple, and cost-effectiveness with high body tolerance. However, the signal of molecule biomarkers is too weak to acquire high-accuracy diagnosis. Herein, precisely engineered metal-organic frameworks for laser desorption/ionization mass spectrometry, allowing favorable charge transfer within the molecule-substrate interface and mitigated thermal dissipation by adjusting the phonon scattering with metal nodes, are developed. Consequently, a surprising signal enhancement of approximate to 10 000-fold is achieved, resulting in diagnosis of three major cancers (liver/lung/kidney cancer) with area-under-the-curve of 0.908-0.964 and accuracy of 83.2%-90.6%, which promises a universal detection tool for large-scale clinical diagnosis of human cancers.
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