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Peer-reviewedCC BYJournal of veterinary internal medicine

Novel liquid biopsy based on comprehensive small RNA profiling for screening of cancer in dogs utilizing machine learning.

  • Sora Harako1
  • Masashi Yuki2
  • Shunsuke Noguchi3
  • Taisuke Ohno4
  • Nobuhiro Nozaki5
  • Ruisa Nishida1
  • Hiroaki Hojo1
  • Ririka Horikawa1
  • Daiki Kawata1
  • Yuichiro Noda1
  • Naoki Miura5,6
  • Takuya Mizuno7,8,9
1 more authors
  • Yasuyuki Momoi4
9 affiliations
  1. 1Research and Development Division, ARKRAY, Inc., Kyoto, Japan.
  2. 2Yuki Animal Hospital, Nagoya, Aichi, Japan.
  3. 3Department of Oncology, Japan Animal Referral Medical Center Osaka, Osaka, Japan.
  4. 4Department of Veterinary Clinical Pathobiology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan.
  5. 5Joint Graduate School of Veterinary Medicine, Kagoshima University, Kagoshima, Japan.
  6. 6Veterinary Teaching Hospital, Joint Faculty of Veterinary Medicine, Kagoshima University, Kagoshima, Japan.
  7. 7Laboratory of Molecular Diagnostics and Therapeutics, Joint Faculty of Veterinary Medicine, Yamaguchi University, Yamaguchi, Japan.
  8. 8Division of Translational Research for One Medicine, Research Institute for Cell Design Medical Science, Yamaguchi University, Yamaguchi, Japan.
  9. 9Japan Small Animal Cancer Center, Affiliated with the Japan Small Animal Medical Center Foundation, Saitama, Japan.
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Abstract

Background

Recent investigations have demonstrated the relevance in cancer of a variety of small RNAs, beyond microRNAs (miRNAs). However, the comprehensive application of these RNAs to cancer detection in dogs remains unexplored.Hypothesis/objectives

We investigated whether combining miRNAs and other small RNAs using a custom database improves the accuracy of machine learning cancer screening in dogs compared with models using only miRNAs.Animals

A total of 389 dogs were enrolled: 85 with cancer, 77 with nonmalignant diseases, and 227 healthy controls.Methods

Small RNAs in blood samples were sequenced, and miRNAs (via miRBase) and other small RNAs (via a custom database) were profiled. Based on these profiles, 2 datasets were prepared: one with only miRNAs and another combining miRNAs with other small RNAs. For each dataset, 12 classification models were constructed using identical methods to enable paired comparisons of performance, with 5-fold cross-validation and area under the receiver operating characteristic curve (AUC) as the primary metrics.Results

The model that integrated both miRNAs and other small RNAs yielded a significantly higher AUC compared with the miRNA-only model (median AUC: 0.926 [95% CI, 0.911-0.944] vs 0.881 [0.860-0.897]; Wilcoxon signed-rank test, P < .001), indicating enhanced capability in discriminating malignant tumors in dogs.Conclusions and clinical importance

We investigated the comprehensive integration of diverse small RNA species, beyond miRNAs, using a machine learning approach for cancer screening in dogs. Although further prospective validation in independent cohorts is needed, our results indicate that this strategy improves the performance of cancer classification models in dogs.

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Cite

BibTeXRIS
BibTeX
@article{harako2026novel,
  title = {{Novel liquid biopsy based on comprehensive small RNA profiling for screening of cancer in dogs utilizing machine learning.}},
  author = {Sora Harako and Masashi Yuki and Shunsuke Noguchi and Taisuke Ohno and Nobuhiro Nozaki and Ruisa Nishida and Hiroaki Hojo and Ririka Horikawa and Daiki Kawata and Yuichiro Noda and Naoki Miura and Takuya Mizuno and Yasuyuki Momoi},
  journal = {Journal of veterinary internal medicine},
  year = {2026},
  month = sep,
  doi = {10.1093/jvimsj/aalag270},
  url = {https://europepmc.org/article/MED/42960979},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Novel liquid biopsy based on comprehensive small RNA profiling for screening of cancer in dogs utilizing machine learning.
AU  - Sora Harako
AU  - Masashi Yuki
AU  - Shunsuke Noguchi
AU  - Taisuke Ohno
AU  - Nobuhiro Nozaki
AU  - Ruisa Nishida
AU  - Hiroaki Hojo
AU  - Ririka Horikawa
AU  - Daiki Kawata
AU  - Yuichiro Noda
AU  - Naoki Miura
AU  - Takuya Mizuno
AU  - Yasuyuki Momoi
PY  - 2026
DA  - 2026/09/01
JO  - Journal of veterinary internal medicine
DO  - 10.1093/jvimsj/aalag270
UR  - https://europepmc.org/article/MED/42960979
AB  - Background  Recent investigations have demonstrated the relevance in cancer of a variety of small RNAs, beyond microRNAs (miRNAs). However, the comprehensive application of these RNAs to cancer detection in dogs remains unexplored.Hypothesis/objectives  We investigated whether combining miRNAs and other small RNAs using a custom database improves the accuracy of machine learning cancer screening in dogs compared with models using only miRNAs.Animals  A total of 389 dogs were enrolled: 85 with cancer, 77 with nonmalignant diseases, and 227 healthy controls.Methods  Small RNAs in blood samples were sequenced, and miRNAs (via miRBase) and other small RNAs (via a custom database) were profiled. Based on these profiles, 2 datasets were prepared: one with only miRNAs and another combining miRNAs with other small RNAs. For each dataset, 12 classification models were constructed using identical methods to enable paired comparisons of performance, with 5-fold cross-validation and area under the receiver operating characteristic curve (AUC) as the primary metrics.Results  The model that integrated both miRNAs and other small RNAs yielded a significantly higher AUC compared with the miRNA-only model (median AUC: 0.926 [95% CI, 0.911-0.944] vs 0.881 [0.860-0.897]; Wilcoxon signed-rank test, P < .001), indicating enhanced capability in discriminating malignant tumors in dogs.Conclusions and clinical importance  We investigated the comprehensive integration of diverse small RNA species, beyond miRNAs, using a machine learning approach for cancer screening in dogs. Although further prospective validation in independent cohorts is needed, our results indicate that this strategy improves the performance of cancer classification models in dogs.
SN  - 0891-6640
SN  - 1939-1676
ER  -