乳腺癌（BC）是一种复杂的疾病，具有高变异性，并且没有特定的肿瘤标志物可用于诊断。外泌体含有丰富的母性肿瘤信息，是一种具有潜力进行癌症诊断和预后的新型非侵入性生物标志物。然而，由于样本制备时间长且灵敏度不足，分析血液样本中的外泌体蛋白标志物存在挑战。为了解决这个问题，我们开发了一种新型滤电化学微流控芯片（FEMC），可在无需繁重纯化方法的情况下直接检测和分类BC。在我们的系统中，外泌体富集是通过双重过滤系统实现的。目标物质经过弯曲通道导向到四个印刷屏幕电极（SPE），在此处被事先修饰的抗体捕获。同时，由于对磷酸根具有高亲和力，Zr-MOFs包埋了大量的亚甲蓝分子（MB@UiO-66）吸附在外泌体表面。该过程导致电信号的放大。我们的方法证明，利用BC外泌体相关的肿瘤生物标志物（即PMSA，EGFR，CD81和CEA），可以对各种BC小鼠模型样本和临床BC样本进行分类。整个FEMC检测过程在1小时内完成，检测限为1×104颗粒/毫升。因此，FEMC检测可以提供实时的检测信息，为临床BC诊断和分类提供及时和更准确的机会。版权所有 © 2023 Elsevier B.V.发表。

Breast cancer (BC) is a complex disease with high variability and no specific tumor markers available for diagnosis. Exosomes contain rich maternal tumor information and are a novel non-invasive biomarker with the potential for cancer diagnosis and prognosis. However, analysis of exosomal protein markers in blood samples is challenging due to lengthy sample workups and insufficient sensitivity. To address this difficulty, we developed a novel filter-electrochemical microfluidic chip (FEMC) to detect and classify BC directly in whole blood without requiring heavy purification methods. In our system, exosome enrichment was performed using a dual filtration system. The target was directed through a curved channel onto four screen-printed electrodes (SPEs), where it was captured by the previously modified antibodies. Simultaneously, Zr-MOFs encapsulated with a large number of methylene blue molecules (MB@UiO-66) were absorbed on the surface of exosomes due to the high affinity for phosphate groups. This process leads to the amplification of electrical signals. The approach demonstrated that the utilization of BC exosome-associated tumor biomarkers (i.e., PMSA, EGFR, CD81, and CEA), enabled the classification of various BC mouse models samples and clinical BC samples. The entire FEMC assay was completed in 1 h with a limit of detection of 1 × 104 particles/mL. Thus, the FEMC assay can provide real-time detection information, allowing timely and better-informed opportunities for clinical BC diagnosis and typing.Copyright © 2023. Published by Elsevier B.V.