Simultaneous Recognition of Over- and Under-Expressed MicroRNAs Using Nanopore Decoding.

This paper describes a strategy for simultaneous recognition of over- and under-expressed microRNAs (miRNAs) using the method of signal classification-based nanopore decoding. MiRNA has attracted attention as a promising biomarker for cancer diagnosis owing to its cancer-type-specific expression patterns. While nanopore technology has emerged as a simple and label-free method to detect miRNAs and their expression patterns, recognizing patterns involving simultaneous over/under-expression is still challenging due to the inherent working principles. Here, inspired by the sequence design for DNA computation with nanopore decoding, we designed diagnostic DNA probes targeting two individual over/under-expressed miRNAs in the serum of oral squamous cell carcinoma. Through nanopore measurements, our designed probes exhibited characteristic current signals depending on the hybridized miRNA species, which were plotted on the scatter plot of duration versus current blocking ratio. The classified signals reflected the relative abundance of target miRNAs, thereby enabling successful pattern recognition of over/under-expressed miRNAs, even when using clinical samples. We believe that our method paves the way for miRNA-targeting simple diagnosis as a liquid biopsy.

Identification of Neck Lymph Node Metastasis-Specific microRNA-Implication for Use in Monitoring or Prediction of Neck Lymph Node Metastasis.

MicroRNAs (miRNAs) have attracted attention as non-invasive cancer biomarkers in various cancers; however, they have not been adequately investigated in oral squamous cell carcinoma (OSCC). This study investigated the diagnostic performance of serum-derived miRNAs at initial diagnosis for primary neck lymph node metastasis and the predictive performance for late neck lymph node metastasis based on long-term (up to approximately 8 years) follow-up of patients with OSCC. The expression of miRNAs in 40 patients with OSCC was quantified using real-time PCR (qPCR), and a comprehensive statistical analysis of the correlation of miRNA expression for primary and late neck lymph node metastases was performed. For the diagnosis of primary neck lymph node metastases, miR-423 and miR-125 were accurate. The miRNA index for primary metastasis diagnosis (miR-PM) calculated by regression analysis showed high diagnostic accuracy. The miR-5100 was useful for predicting late neck lymph node metastases. The miRNA index for late metastasis prediction (miR-LM) calculated using regression analysis showed high prediction accuracy. MiRNAs were useful for diagnosing primary neck lymph node metastases in OSCC and predicting late neck lymph node metastases. It may help to consider individualized treatment, including follow-up, surgical methods, and postoperative management.