Early Cancer Detection via Multi-microRNA Profiling of Urinary Exosomes Captured by Nanowires

Area of Science:

• Biochemistry
• Nanotechnology
• Oncology

Background:

• MicroRNAs within extracellular vesicles (EVs), including exosomes, show differences between healthy individuals and cancer patients, serving as potential cancer biomarkers.
• Kidney filtration can transfer blood EVs, which are not fully transferred between cells, into urine.
• Previous work demonstrated zinc oxide nanowires' ability to capture urinary EVs via surface charge and hydrogen bonding, yielding cancer-related microRNAs.

Purpose of the Study:

• To assess the scalability of zinc oxide nanowires for comprehensive EV capture from urine.
• To develop an in situ method for extracting microRNAs from captured EVs.
• To identify differential microRNAs in non-cancer and lung cancer subjects using machine learning.

Main Methods:

• Scalable capture of urinary EVs, including exosomes, using zinc oxide nanowires.
• In situ extraction of microRNAs from captured EVs.
• Machine learning analysis of extracted microRNAs to differentiate between non-cancer and lung cancer subjects.

Main Results:

• Zinc oxide nanowires effectively captured a vast number of EVs from urine, confirming the presence of approximately 2500 human microRNA species.
• Machine learning models identified specific microRNAs that differ between non-cancer and lung cancer individuals.
• The developed method achieved high accuracy (AUC of 0.99) in classifying cancer and non-cancer subjects, including early-stage cancer.

Conclusions:

• Urinary EVs and their microRNA cargo are a viable source for non-invasive cancer detection.
• Zinc oxide nanowire technology offers a scalable and efficient platform for urinary exosome and microRNA analysis.
• Machine learning applied to urinary microRNA profiles enables highly accurate early-stage lung cancer diagnosis.