Magnetic 3D macroporous MOF oriented urinary exosome metabolomics for early diagnosis of bladder cancer

被引:5
作者
Cao, Yiqing [1 ,2 ]
Feng, Jianan [3 ]
Zhang, Qiao [4 ]
Deng, Chunhui [5 ]
Yang, Chen [6 ]
Li, Yan [1 ,2 ,7 ]
机构
[1] Fudan Univ, Shanghai Pudong Hosp, Ctr Med Res & Innovat, Shanghai 201203, Peoples R China
[2] Fudan Univ, Sch Pharm, Dept Pharmaceut Anal, Shanghai 201203, Peoples R China
[3] Shanghai Univ Med & Hlth Sci, Sch Pharm, Shanghai 201318, Peoples R China
[4] Fudan Univ, Ctr Instrument Anal, Sch Pharm, Shanghai 201203, Peoples R China
[5] Fudan Univ, Inst Biomed Sci, Dept Chem, Shanghai 200032, Peoples R China
[6] Fudan Univ, Huashan Hosp, Dept Urol, Shanghai 200040, Peoples R China
[7] Fudan Univ, Innovat Ctr New Drug Dev Immune Inflammatory Dis, Shanghai 201203, Peoples R China
基金
中国国家自然科学基金;
关键词
Exosome; Metabolomics; Mass spectrometry; MOF; Bladder cancer; METAL-ORGANIC FRAMEWORKS;
D O I
10.1186/s12951-024-02952-0
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 ; 0836 ; 090102 ; 100705 ;
摘要
Bladder cancer (BCa) exhibits the escalating incidence and mortality due to the untimely and inaccurate early diagnosis. Urinary exosome metabolites, carrying critical tumor cell information and directly related to bladder, emerge as promising non-invasive diagnostic biomarkers of BCa. Herein, the magnetic 3D ordered macroporous zeolitic imidazolate framework-8 (magMZIF-8) is synthesized and used for efficient urinary exosome isolation. Notably, beyond retaining the single crystals and micropores of conventional ZIF-8, MZIF-8 is further enhanced with highly oriented and ordered macropores (150 nm) and the large specific surface area (973 m2<middle dot>g-1), which could enable the high purity and yield separation of exosomes via leveraging the combination of size exclusion, affinity, and electrostatic interactions between magMZIF-8 and the surfaces of exosome. Furthermore, the magnetic and hydrophilic properties of magMZIF-8 will further simplify the process and enhance the efficiency of separation. After conditional optimization, a 50 mL of urine is sufficient for exosome metabolomics analysis, and the time for isolating exosomes from 42 urine samples was 2 hours only. Incorporating machine learning algorithms with LC-MS/MS analysis of the metabolic patterns obtained from isolated exosomes, early-stage BCa patients were differentiated from healthy controls, with area under the curve (AUC) value of 0.844-0.9970 in the training set and 0.875-1.00 in the test set, signifying its potential as a reliable diagnostic tool. This study offers a promising approach for the non-invasive and efficient diagnosis of BCa on a large scale via exosome metabolomics.
引用
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页数:13
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