1. Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Guangdong Province 518172, China
2. School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, Guangdong Province 518172, China
3. Institute of Molecular Medicine and Bioengineering, National Chiao Tung University, Hsinchu City 30068, Taiwan, ROC
4. Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu City 30068, Taiwan, ROC
5. Department of Biological Science and Technology, National Chiao Tung University, Hsinchu City 30068, Taiwan, ROC
6. Come True Biomedical Inc., Taichung 408, Taiwan, ROC
7. Department of Obstetrics and Gynecology, Hsinchu MacKay Memorial Hospital, Hsinchu City 300, Taiwan, ROC
8. Department of Medicine, MacKay Medical College, New Taipei City 252, Taiwan, ROC
9. MacKay Junior College of Medicine, Nursing and Management College, Taipei City 112, Taiwan, ROC
10. Department of Medical Research, Hsinchu Mackay Memorial Hospital, Hsinchu City 30071, Taiwan, ROC
11. Center for Intelligent Drug Systems and Smart Bio-Devices, National Chiao Tung University, Hsinchu City 30068, Taiwan, ROC
12. Graduate Institute of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung 80708, Taiwan, ROC
*These authors contributed equally to this work.
Rationale: Triple-negative breast cancer (TNBC), which has the highest recurrence rate and shortest survival time of all breast cancers, is in urgent need of a risk assessment method to determine an accurate treatment course. Recently, miRNA expression patterns have been identified as potential biomarkers for diagnosis, prognosis, and personalized therapy. Here, we investigate a combination of candidate miRNAs as a clinically applicable signature that can precisely predict relapse in TNBC patients after surgery.
Methods: Four total cohorts of training (TCGA_TNBC and GEOD-40525) and validation (GSE40049 and GSE19783) datasets were analyzed with logistic regression and Gaussian mixture analyses. We established a miRNA signature risk model and identified an 8-miRNA signature for the prediction of TNBC relapse.
Results: The miRNA signature risk model identified ten candidate miRNAs in the training set. By combining 8 of the 10 miRNAs (miR-139-5p, miR-10b-5p, miR-486-5p, miR-455-3p, miR-107, miR-146b-5p, miR-324-5p and miR-20a-5p), an accurate predictive model of relapse in TNBC patients was established and was highly correlated with prognosis (AUC of 0.80). Subsequently, this 8-miRNA signature prognosticated relapse in the two validation sets with AUCs of 0.89 and 0.90.
Conclusion: The 8-miRNA signature predictive model may help clinicians provide a prognosis for TNBC patients with a high risk of recurrence after surgery and provide further personalized treatment to decrease the chance of relapse.
Keywords: triple-negative breast cancer, miRNA signature, relapse, prediction, prognosis