Multicenter development and validation of a serum microRNA-based diagnostic model for breast cancer and its association with tumor burden and biological aggressiveness: an observational case-control study.
12 affiliations
- 1Department of Breast Surgery, Kyoto University Graduate School of Medicine, Kyoto, Japan. [email protected].
- 2Department of Breast Surgery, Kyoto University Graduate School of Medicine, Kyoto, Japan.
- 3Research and Development Division, ARKRAY, Inc., Kyoto, Japan.
- 4Department of Breast Surgery, Shiga General Hospital, Shiga, Japan.
- 5Department of Breast Surgery, Kitano Hospital, Osaka, Japan.
- 6Department of Breast Surgery, Tenri Hospital, Nara, Japan.
- 7Department of Breast Surgery, Kansai Medical University Hospital, Osaka, Japan.
- 8Department of Breast Surgery, Kyoto City Hospital, Kyoto, Japan.
- 9Department of Breast Surgery, Osaka Red Cross Hospital, Osaka, Japan.
- 10Department of Breast Surgery, Kyoto Katsura Hospital, Kyoto, Japan.
- 11Department of Breast Surgery, Hyogo Prefectural Amagasaki General Medical Center, Hyogo, Japan.
- 12Department of Breast Surgery, Tokyo Metropolitan Komagome Hospital, Tokyo, Japan.
Abstract
Background
Although mammography remains the cornerstone of breast cancer screening, its diagnostic performance is influenced by factors such as breast density and tumor characteristics. Blood-based biomarkers may therefore provide complementary biological information to imaging-based detection strategies. While circulating microRNAs (miRNAs) have emerged as promising minimally invasive biomarkers, their diagnostic performance and associations with clinicopathological characteristics remain incompletely understood. This multicenter study aimed to develop and validate a serum miRNA-based diagnostic model for breast cancer and to evaluate its associations with clinical stage, histological grade, and imaging-related factors.Methods
In this multicenter observational case-control study, serum samples from 345 patients with breast cancer and 373 cancer-free controls were analyzed using next-generation sequencing-based miRNA profiling. After normalization and batch correction, 114 stably expressed miRNAs were selected for model construction. An ensemble machine learning model integrating five algorithms (Lasso, Ridge, support vector machine, Nu-SVM, and histogram-based gradient boosting) was developed using a training cohort (n = 569) and evaluated in a held-out test cohort (n = 149). Model performance was assessed using receiver operating characteristic curve analysis and the area under the curve (AUC) with 95% confidence intervals calculated by the DeLong method. Model-derived prediction scores were further evaluated in relation to clinicopathological and imaging-related variables.Results
The serum miRNA-based diagnostic model demonstrated promising discrimination between breast cancer cases and cancer-free controls, with an AUC of 0.875 (95% CI, 0.820-0.930) in the held-out test set. Model-derived prediction scores increased with advancing clinical stage and showed an ordered trend across histological grades. In exploratory analyses, prediction scores tended to increase across higher clinical T categories and were higher among patients with distant metastasis, whereas no clear differences were observed according to mammographic category, breast density, or intrinsic tumor subtype.Conclusions
In this multicenter case-control study, a serum miRNA-based ensemble model demonstrated promising diagnostic discrimination for breast cancer. The model-derived prediction score was associated with clinical stage and histological grade, suggesting that circulating miRNA profiles may provide biologically relevant information beyond binary case-control discrimination. These findings support further investigation of serum miRNA profiling as a potential adjunctive diagnostic approach; however, prospective validation in representative screening populations is required before clinical implementation.Trial registration
Not applicable.
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BibTeX
@article{yamaguchi2026multicenter,
title = {{Multicenter development and validation of a serum microRNA-based diagnostic model for breast cancer and its association with tumor burden and biological aggressiveness: an observational case-control study.}},
author = {Ayane Yamaguchi and Kana Kawanishi and Kei Ishii and Yukiko Fukui and Hanako Shimizu and Masahiro Kawashima and Ryo Otomo and Soseki Taniguchi and Mitsuharu Hirai and Wakako Tsuji and Sachiko Takahara and Hiroyasu Yamashiro and Hirotsugu Yanai and Yoshio Moriguchi and Shigeru Tsuyuki and Mariko Nishie and Hirofumi Suwa and Norikazu Masuda and Masakazu Toi},
journal = {Breast cancer research : BCR},
year = {2026},
month = aug,
doi = {10.1186/s13058-026-02361-1},
url = {https://europepmc.org/article/MED/42972452},
note = {CC BY},
}
RIS
TY - JOUR
TI - Multicenter development and validation of a serum microRNA-based diagnostic model for breast cancer and its association with tumor burden and biological aggressiveness: an observational case-control study.
AU - Ayane Yamaguchi
AU - Kana Kawanishi
AU - Kei Ishii
AU - Yukiko Fukui
AU - Hanako Shimizu
AU - Masahiro Kawashima
AU - Ryo Otomo
AU - Soseki Taniguchi
AU - Mitsuharu Hirai
AU - Wakako Tsuji
AU - Sachiko Takahara
AU - Hiroyasu Yamashiro
AU - Hirotsugu Yanai
AU - Yoshio Moriguchi
AU - Shigeru Tsuyuki
AU - Mariko Nishie
AU - Hirofumi Suwa
AU - Norikazu Masuda
AU - Masakazu Toi
PY - 2026
DA - 2026/08/06
JO - Breast cancer research : BCR
DO - 10.1186/s13058-026-02361-1
UR - https://europepmc.org/article/MED/42972452
AB - Background Although mammography remains the cornerstone of breast cancer screening, its diagnostic performance is influenced by factors such as breast density and tumor characteristics. Blood-based biomarkers may therefore provide complementary biological information to imaging-based detection strategies. While circulating microRNAs (miRNAs) have emerged as promising minimally invasive biomarkers, their diagnostic performance and associations with clinicopathological characteristics remain incompletely understood. This multicenter study aimed to develop and validate a serum miRNA-based diagnostic model for breast cancer and to evaluate its associations with clinical stage, histological grade, and imaging-related factors.Methods In this multicenter observational case-control study, serum samples from 345 patients with breast cancer and 373 cancer-free controls were analyzed using next-generation sequencing-based miRNA profiling. After normalization and batch correction, 114 stably expressed miRNAs were selected for model construction. An ensemble machine learning model integrating five algorithms (Lasso, Ridge, support vector machine, Nu-SVM, and histogram-based gradient boosting) was developed using a training cohort (n = 569) and evaluated in a held-out test cohort (n = 149). Model performance was assessed using receiver operating characteristic curve analysis and the area under the curve (AUC) with 95% confidence intervals calculated by the DeLong method. Model-derived prediction scores were further evaluated in relation to clinicopathological and imaging-related variables.Results The serum miRNA-based diagnostic model demonstrated promising discrimination between breast cancer cases and cancer-free controls, with an AUC of 0.875 (95% CI, 0.820-0.930) in the held-out test set. Model-derived prediction scores increased with advancing clinical stage and showed an ordered trend across histological grades. In exploratory analyses, prediction scores tended to increase across higher clinical T categories and were higher among patients with distant metastasis, whereas no clear differences were observed according to mammographic category, breast density, or intrinsic tumor subtype.Conclusions In this multicenter case-control study, a serum miRNA-based ensemble model demonstrated promising diagnostic discrimination for breast cancer. The model-derived prediction score was associated with clinical stage and histological grade, suggesting that circulating miRNA profiles may provide biologically relevant information beyond binary case-control discrimination. These findings support further investigation of serum miRNA profiling as a potential adjunctive diagnostic approach; however, prospective validation in representative screening populations is required before clinical implementation.Trial registration Not applicable.
SN - 1465-5411
SN - 1465-542X
ER -