Issue 7, 2021

Breast cancer plasma biopsy by in situ determination of exosomal microRNA-1246 with a molecular beacon

Abstract

Liquid biopsy is becoming an innovative tool in precision oncology owing to its noninvasive identification of biomarkers circulating in the body fluid at various time points for continuous and real-time analysis of disease progression. MicroRNAs in blood exosomes are identified as a new promising class of potential biomarkers for cancer diagnostics and prognostics. Conventional detection of blood exosomal microRNAs need multiple-step, complicated, costly, and time-consuming sample preparation of exosomes isolation and RNA extract, which affect the accuracy and reproducibility of analytical results. In this work, we set up an in situ quantitative analysis of human plasma exosomal miR-1246 by a probe of 2′-O-methyl and phosphorothioate modified molecular beacon. The probe has outstanding nuclease resistance in highly active RNase A/T1/I, which makes it stable for direct application in blood samples. With rapid rupture of exosomes membrane by Triton X-100, the probe can enter exosomes to specifically target miR-1246 exhibiting quantitative fluorescent signals. Using the output signals as a diagnostic marker, we differentiated 33 breast cancer patients from 37 healthy controls with 97.30% sensitivity and 93.94% specificity at the best cutoff. The blood biopsy is simple without extracting plasma exosomes and their nucleic acids content, time-saving in about 2 h of total analysis process, and microvolumes needed for plasma sample, suggesting its good potential to clinical application.

Graphical abstract: Breast cancer plasma biopsy by in situ determination of exosomal microRNA-1246 with a molecular beacon

Supplementary files

Article information

Article type
Paper
Submitted
15 Nov 2020
Accepted
11 Feb 2021
First published
12 Feb 2021

Analyst, 2021,146, 2264-2276

Breast cancer plasma biopsy by in situ determination of exosomal microRNA-1246 with a molecular beacon

Y. Chen, L. Zhai, L. Zhang, X. Ma, Z. Liu, M. Li, J. Chen and W. Duan, Analyst, 2021, 146, 2264 DOI: 10.1039/D0AN02224A

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