Digital Decoding of Single Extracellular Vesicle Phenotype Differentiates Early Malignant and Benign Lung Lesions.

ABSTRACT: Accurate identification of malignant lung lesions is a prerequisite for rational clinical management to reduce morbidity and mortality of lung cancer. However, classification of lung nodules into malignant and benign cases is difficult as they show similar features in computer tomography and sometimes positron emission tomography imaging, making invasive tissue biopsies necessary. To address the challenges in evaluating indeterminate nodules, the authors investigate the molecular profiles of small extracellular vesicles (sEVs) in differentiating malignant and benign lung nodules via a liquid biopsy-based approach. Aiming to characterize phenotypes between malignant and benign groups, they develop a single-molecule-resolution-digital-sEV-counting-detection (DECODE) chip that interrogates th

SUBMITTER: Li J 

PROVIDER: S-EPMC9811438 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature