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. 2024 Nov 26:27:100414.
doi: 10.1016/j.vas.2024.100414. eCollection 2025 Mar.

Development of a sensitive disease-screening model using comprehensive circulating microRNA profiles in dogs: A pilot study

Kohei Omura  1 , Kaori Ide  2 , Masashi Takahashi  3 , Yu Furusawa  3 , Masanori Kobayashi  4 , Yuichi Miyagawa  5 , Aki Fujiwara-Igarashi  6 , Takahiro Teshima  7 , Yoshiaki Kubo  8 , Akiko Yasuda  8 , Karin Yoshida  8 , Noriyuki Hayakawa  8 , Masato Kobayashi  4 , Yasuyuki Momoi  9
Affiliations

Development of a sensitive disease-screening model using comprehensive circulating microRNA profiles in dogs: A pilot study

Kohei Omura et al. Vet Anim Sci. .

Abstract

In the veterinary field, the utility of disease-identification models that use comprehensive circulating microRNA (miRNA) profiles produced through measurements based on next-generation sequencing (NGS) remains unproven. To integrate NGS technology with automated machine learning (autoML) to create a comprehensive circulating miRNA profile and to assess the clinical utility of a disease-screening model derived from this profile. The study involved dogs diagnosed with or being treated for various diseases, including tumors, across multiple veterinary clinics (n = 254), and healthy dogs without apparent diseases (n = 91). miRNA was extracted from EDTA-treated plasma, and a comprehensive analysis was conducted of one million reads per sample using NGS. Then autoML technology was applied to develop a diagnostic model based on miRNA. Among these models, the one with the highest performance was chosen for evaluation. The diagnostic model, based on the comprehensive circulating miRNA profile developed in this study, achieved an AUC score of 0.89, with a sensitivity of 85 % and a specificity of 88 % for the disease samples. The miRNA-based diagnostic model demonstrated high sensitivity for disease groups and has the potential to be an effective screening test. This study indicates that a comprehensive miRNA profile in dog plasma could serve as a highly sensitive blood biomarker.

Keywords: Automated machine learning; Canine; Next-generation sequencing; microRNA.

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Conflict of interest statement

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Kohei Omura reports a relationship with Arkray Marketing Inc that includes: employment. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig 1
Fig. 1
ROC curve analysis of the optimal diagnostic model for disease detection using a comprehensive miRNA profile with 5-fold cross validation. combined ROC: solid line; ROC folds 1-5: dashed lines. AUC score: 0.95; sensitivity: 85%; specificity: 88%.
Fig 2
Fig. 2
Performance evaluation of the optimal miRNA-based diagnostic model in a validation dataset. AUC score: 0.89; sensitivity: 84% (95% CI: 0.74–0.92); specificity: 89% (95% CI: 0.78–1.00).
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