Interdisciplinary sciences, computational life sciences · 2026
Identifying Potential Exosome-Derived mRNA Biomarkers for Diagnosis and Prediction of Breast Cancer Using Machine-Learning Approaches.
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Li Chenhao, Wei Chunyan, Tian Qijia, Bu Dechao, Ge Yang, Zhang Mingdi, Hu Xiaolin
Аннотация
INTRODUCTION: Breast cancer remains a major global health burden, underscoring the urgent need for reliable early detection strategies. Exosomes, as mediators of intercellular communication, have shown promise in early tumor screening through Raman spectroscopy and gene expression profiling in pancreatic and colorectal cancers. However, the application of exosomal gene expression profiles for breast cancer prediction remains largely unexplored.
METHODS: Exosomal mRNA profiles were obtained from exoRBase 3.0 (242 breast cancer, 244 healthy controls). Sample sex was inferred using XIST and UTY expression, yielding 337 female samples for analysis. A nested cross-validation framework (20 repetitions, fivefold) was implemented, with differential expression analysis and feature selection performed exclusively within each training fold to prevent information leakage. Ten machine learning classifiers were evaluated on an independent held-out test set. Model performance was assessed using accuracy, precision, recall, and F1-score.
RESULTS: Feature selection demonstrated high stability (average Jaccard score 0.5912), with 9 genes consistently selected across all 100 iterations and a set of robust feature genes was identified. Among classifiers, xgbTree achieved the best performance (AUC 0.992, accuracy 0.970, F1 0.979) on the independent test set, supporting exosomal mRNA profiles as a promising non-invasive approach for the early breast cancer detection.
METHODS: Exosomal mRNA profiles were obtained from exoRBase 3.0 (242 breast cancer, 244 healthy controls). Sample sex was inferred using XIST and UTY expression, yielding 337 female samples for analysis. A nested cross-validation framework (20 repetitions, fivefold) was implemented, with differential expression analysis and feature selection performed exclusively within each training fold to prevent information leakage. Ten machine learning classifiers were evaluated on an independent held-out test set. Model performance was assessed using accuracy, precision, recall, and F1-score.
RESULTS: Feature selection demonstrated high stability (average Jaccard score 0.5912), with 9 genes consistently selected across all 100 iterations and a set of robust feature genes was identified. Among classifiers, xgbTree achieved the best performance (AUC 0.992, accuracy 0.970, F1 0.979) on the independent test set, supporting exosomal mRNA profiles as a promising non-invasive approach for the early breast cancer detection.