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MicroRNAs

MicroRNAs

MicroRNA (miRNA) are short, regulatory RNA transcribed from introns (non-coding regions of a gene) or intergenic regions (stretches of DNA present between genes). Several processing steps are required to form biologically active, mature miRNA. The initial transcript, called primary miRNA (pri-mRNA), base-pairs with itself, forming a stem-loop structure. Within the nucleus, an endonuclease enzyme, called Drosha, shortens the stem-loop structure into hairpin-shaped pre-miRNA. After the pre-miRNA...

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MicroRNA Based Liquid Biopsy: The Experience of the Plasma miRNA Signature Classifier MSC for Lung Cancer Screening

MicroRNA Based Liquid Biopsy: The Experience of the Plasma miRNA Signature Classifier MSC for Lung Cancer Screening

Published on: October 26, 2017

A machine learning model for cancer screening in dogs using comprehensive circulating microRNA profiles.

Ruisa Nishida¹, Masashi Takahashi², Kaori Ide³

¹Research and Development Division, ARKRAY, Inc., Kyoto 602-0008, Japan.

Journal of Veterinary Internal Medicine

|February 26, 2026

Summary

A new diagnostic model using microRNA (miRNA) profiles and machine learning can accurately detect cancer in dogs. This liquid biopsy approach shows promise as a screening tool in veterinary practice.

Keywords:

liquid biopsy next-generation sequencing non-cording RNA oncology

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MicroRNA Amplification and Recognition through Locked-nucleic-acid In situ Hybridization as a Novel Detection and Quantification Method

Published on: October 7, 2025

Area of Science:

Veterinary oncology
Molecular diagnostics
Bioinformatics

Background:

MicroRNAs (miRNAs) are crucial non-coding RNAs in cancer development.
Current diagnostic methods lack specificity in differentiating canine cancer from other conditions.
Liquid biopsy using miRNAs for canine cancer detection remains largely unexplored.

Purpose of the Study:

To develop and validate a diagnostic model for canine cancer detection.
To differentiate dogs with cancer from healthy dogs and those with non-malignant diseases.
To assess the utility of next-generation sequencing (NGS) and machine learning in canine cancer diagnostics.

Main Methods:

Plasma samples from 574 dogs (168 cancer, 138 non-malignant disease, 268 healthy) were analyzed using NGS.
Comprehensive miRNA profiles were generated and analyzed using machine learning (DataRobot).
A model was built using the 50 most highly expressed miRNAs, optimized via 5-fold cross-validation.

Main Results:

The miRNA-based diagnostic model achieved an area under the curve (AUC) of 0.907.
The model demonstrated high accuracy, with both sensitivity and specificity at 0.85.
It successfully distinguished dogs with cancer from healthy controls and dogs with non-malignant conditions.

Conclusions:

A diagnostic model integrating NGS-derived miRNA profiles and machine learning is effective for canine cancer detection.
This approach can differentiate cancer-affected dogs from healthy and non-malignant disease groups.
The findings support the potential use of this model as a non-invasive screening test in veterinary settings.