细胞外小泡
生物标志物
癌症
胰腺癌
癌症生物标志物
蛋白质组学
胞外囊泡
癌症研究
病理
计算生物学
医学
微泡
生物
小RNA
内科学
生物化学
细胞生物学
基因
作者
Komila Rasuleva,Santhalingam Elamurugan,Aaron Bauer,Mdrakibhasan Khan,Qian Wen,Zhaofan Li,Preston D. Steen,Ang Guo,Wenjie Xia,S. Mathew,Rick J. Jansen,Dali Sun
出处
期刊:ACS Sensors
[American Chemical Society]
日期:2021-11-30
卷期号:6 (12): 4489-4498
被引量:26
标识
DOI:10.1021/acssensors.1c02022
摘要
Tumor-derived extracellular vesicles (EVs) are under intensive study for their potential as noninvasive diagnosis biomarkers. Most EV-based cancer diagnostic assays trace supernumerary of a single cancer-associated marker or marker signatures. These types of biomarker assays are either subtype-specific or vulnerable to be masked by high background signals. In this study, we introduce using the β-sheet richness (BR) of the tumor-derived EVs as an effective way to discriminate EVs originating from malignant and nonmalignant cells, where EV contents are evaluated as a collective attribute rather than single factors. Circular dichroism, Fourier transform infrared spectroscopy, fluorescence staining assays, and a de novo workflow combining proteomics, bioinformatics, and protein folding simulations were employed to validate the collective attribute at both cellular and EV levels. Based on the BR of the tumorous EVs, we integrated immunoprecipitation and fluorescence labeling targeting the circulating tumor-derived EVs in serum and developed the process into a clinical assay, named EvIPThT. The assay can distinguish patients with and without malignant disease in a pilot cohort, with weak correlations to prognosis biomarkers, suggesting the potential for a cancer screening panel with existing prognostic biomarkers to improve overall performance.
科研通智能强力驱动
Strongly Powered by AbleSci AI