Exosome Metabolic Patterns on Aptamer-Coupled Polymorphic Carbon for Precise Detection of Early Gastric Cancer
Exosome Metabolic Patterns on Aptamer-Coupled Polymorphic Carbon for Precise Detection of Early Gastric Cancer
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DOI：
10.1021/acsnano.2c05355
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发表时间：
2022-08-10
期刊：
影响因子：
17.1
通讯作者：
Deng, Chunhui
中科院分区：
文献类型：
--
作者：
Chen, Haolin;Huang, Chuwen;Deng, Chunhui
胃癌（GC）由于诊断迟缓，在世界范围内呈现高死亡率。目前，基于外泌体的液体活检已应用于包括癌症在内的疾病的诊断和监测，而基于代谢水平外泌体的疾病检测鲜有报道。本文构建了特异性适配体偶联的金修饰多态性碳（CoMPC@Au-Apt），用于捕获早期GC患者和健康对照（hc）的尿外泌体和随后的外泌体代谢模式分析，而无需额外的洗脱过程。结合所有外泌体代谢模式的机器学习算法，可以很好地区分早期GC患者和hc，发现集和盲测准确率均为100%。最后，确定了三个具有明确身份的关键代谢特征作为生物标志物面板，在发现集和验证集中获得了超过90%的早期GC诊断准确率。此外，通过早期和晚期hcc与GC的比较，揭示了关键代谢特征随GC发展的变化规律，体现了它们对GC的监测能力。这项工作说明了外泌体的高特异性和外泌体代谢分析在疾病诊断和监测中的巨大前景，将推动外泌体驱动的精准医学走向临床。
Gastric cancer (GC) presents high mortality worldwide because of delayed diagnosis. Currently, exosomebased liquid biopsy has been applied in diagnosis and monitoring of diseases including cancers, whereas disease detection based on exosomes at the metabolic level is rarely reported. Herein, the specific aptamer-coupled Au-decorated polymorphic carbon (CoMPC@Au-Apt) is constructed for the capture of urinary exosomes from early GC patients and healthy controls (HCs) and the subsequent exosome metabolic pattern profiling without extra elution process. Combining with machine learning algorithm on all exosome metabolic patterns, the early GC patients are excellently discriminated from HCs, with an accuracy of 100% for both the discovery set and blind test. Ulteriorly, three key metabolic features with clear identities are determined as a biomarker panel, obtaining a more than 90% diagnostic accuracy for early GC in the discovery set and validation set. Moreover, the change law of the key metabolic features along with GC development is revealed through making a comparison among HCs and GC at early stage and advanced stage, manifesting their monitoring ability toward GC. This work illustrates the high specificity of exosomes and the great prospective of exosome metabolic analysis in disease diagnosis and monitoring, which will promote exosome-driven precision medicine toward practical clinical application.