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Identifying Potential Exosome-Derived mRNA Biomarkers for Diagnosis and Prediction of Breast Cancer Using
Chenhao Li1, Chunyan Wei2, Qijia Tian3
1Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China.
Interdisciplinary Sciences, Computational Life Sciences
|July 15, 2026
Summary
Exosomal messenger RNA (mRNA) profiles show potential for non-invasive breast cancer detection. Machine learning models accurately identified breast cancer using these exosomal mRNA signatures, paving the way for early diagnosis.
Area of Science:
- Biomarkers
- Genomics
- Oncology
Background:
- Breast cancer poses a significant global health challenge, necessitating advanced early detection methods.
- Exosomes are emerging as key players in intercellular communication and show promise for early tumor detection.
- Previous studies explored exosomal gene expression in other cancers, but its use in breast cancer prediction is under-investigated.
Purpose of the Study:
- To evaluate the utility of exosomal mRNA profiles for early breast cancer detection.
- To identify robust exosomal gene expression signatures for breast cancer prediction.
- To assess the performance of machine learning classifiers in distinguishing breast cancer from healthy controls using exosomal mRNA data.
Main Methods:
- Exosomal mRNA profiles were analyzed from a dataset including breast cancer patients and healthy controls.
- A nested cross-validation framework and machine learning classifiers were employed for robust model development and evaluation.
- Differential expression analysis and feature selection were performed to identify key predictive genes, ensuring no information leakage.
Main Results:
- A stable set of robust feature genes was identified through feature selection, consistently selected across iterations.
- The xgbTree machine learning classifier demonstrated high performance, achieving an AUC of 0.992 on an independent test set.
- Exosomal mRNA profiles proved to be a promising non-invasive approach for early breast cancer detection, with high accuracy (0.970) and F1-score (0.979).
Conclusions:
- Exosomal mRNA profiling represents a viable non-invasive strategy for early breast cancer detection.
- Machine learning models, particularly xgbTree, can effectively utilize exosomal mRNA signatures for accurate breast cancer classification.
- This approach holds significant potential for improving early diagnosis and patient outcomes in breast cancer management.
