Abstract
Multiple microRNAs encapsulated in extracellular vesicles (EVs) including exosomes, unique subtypes of EVs, differ in healthy and cancer groups of people, and they represent a warning sign for various cancer scenarios. Since all EVs in blood cannot be transferred from donor to recipient cells during a single blood circulation, kidney filtration could pass some untransferred EVs from blood to urine. Previously, we reported on the ability of zinc oxide nanowires to capture EVs based on surface charge and hydrogen bonding; these nanowires extracted massive numbers of microRNAs in urine, seeking cancer-related microRNAs through statistical analysis. Here, we report on the scalability of the nanowire performance capability to comprehensively capture EVs, including exosomes, in urine, extract microRNAs from the captured EVs in situ, and identify multiple microRNAs in the extracted microRNAs differing in noncancer and lung cancer subjects through machine learning-based analysis. The nanowire-based extraction allowed the presence of about 2500 species of urinary microRNAs to be confirmed, meaning that urine includes almost all human microRNA species. The machine learning-based analysis identified multiple microRNAs from the extracted microRNA species. The ensembles could classify cancer and noncancer subjects with the area under the receiver operating characteristic curve of 0.99, even though the former were staged early.