Delayed diagnosis of cancer is one of the leading reasons for its fatality rate. With early diagnosis being vital to patient care, novel diagnostic tools that can detect early-stage cancer have garnered a lot of attention in recent years.
While miRNAs are viable biomarkers for early cancer detection, the identification of cancer-related miRNAs in blood and other bodily fluids has proven challenging. To remedy this, a team of researchers has focused their efforts on the development of a nanowire-based miRNA extraction and machine learning analysis method.
"Circulating miRNAs in the blood are mostly encapsulated in extracellular vesicles (EVs) and carry critical regulatory information," said Takao Yasui from the Institute of Science Tokyo (Science Tokyo). "These miRNAs differ between healthy individuals and those with cancer. By utilizing zinc oxide (ZnO) nanowires to capture and extract miRNAs in urine, our research group has attempted to develop a non-invasive cancer detection tool."
In their work, the team utilized ZnO nanowires to efficiently capture miRNA-containing EVs, including exosomes-unique subtypes of EVs ranging from 40nm to 200nm. During miRNA profiling analysis of 200 urine samples, the team identified the presence of over 2,400 miRNA species.
By using a logistic regression classifier they constructed using machine learning, the team identified one urinary miRNA ensemble comprised of 53 miRNA species that could differentiate cancer and noncancer subjects with excellent specificity and sensitivity.
"We also identified another urinary miRNA ensemble that could accurately detect stage-I lung cancer. Since urinary miRNA ensembles can predict early-stage lung cancer, we believe that urinary miRNA ensembles have sufficient potential to be developed as liquid biopsies for early-stage cancer prediction," Yasui added.