Abstract P1-07-31: Integrated transcriptomics analyses identify novel three microRNAs signature to predict poor prognosis and metastasis in breast cancer
Authors: K Takabe, T Kawaguchi, L Yan, Q Qi, X Peng, J Young, S Liu
Journal: Cancer Research (2018)

Abstract Backgrounds: MicroRNA (miRNA) play a crucial role in cancer progression, and altered miRNA expression has been demonstrated to be associated with breast cancer. MiRNA is also known to be stably detectable not only in tissue but peripheral circulation; therefore it could be a feasible and potential biomarker of breast cancer in clinical settings. However, few studies have been reported to identify promising miRNA profiles as predictive biomarker using statistically satisfied large cohorts of breast cancer patients. The aim of this study is to identify miRNA signature that can predict patient survival utilizing integrated and unbiased transcriptomics analyses. Methods: Integrated and unbiased transcriptomics approach was conducted on genomic and clinicopathological information of 2580 breast cancer patients. We utilized The cancer Genome Atlas (TCGA) to identify miRNA signature that could significantly associated with clinical relevance including prognosis and metastatic information, followed by validation analyses with the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) and Gene Expression Omnibus (GEO) Results: A novel risk scoring model including three miRNAs signature (miR-19a, miR-93, and miR-106a) was identified using Cox model. This miRNA score was able to extract the patient population with extremely poor prognosis in TCGA (5-yr overall survival rate, 49.2 %, p=0.0005). This result was validated with another three completely independent cohorts with microarray dataset accompanied with sufficient clinical information and miRNA expression (GSE19536, n=96, p=0.0009; GSE22220, n=210, p=0.0003; METABRIC, n=1223, p=0,0023). Interestingly, the subtype stratification with PAM-50 classification using bioinformatics pipeline demonstrated that this miRNA score could predict poor overall or disease-free survival especially in the population with luminal A, B or normal like subtype (p=0.0300 and p=0.0001, respectively). In addition, competing risk analysis for tumor recurrences demonstrated that the risk scoring using three miRNAs signature could be significantly associated with bone metastasis (p=0.0052). Finally, Gene Set Enrichment Analysis (GSEA) identified that high risk score using three miRNAs associated significantly with several critical gene sets related to metastatic formation such as angiogenesis (p &amp;lt;0.0001), epithelial mesenchymal transition (EMT) (p = 0.0155), focal adhesion (p &amp;lt;0.0001), TGF-beta signaling pathway (p = 0.0025), and ECM receptor interaction (p = 0.0068). Conclusions: We demonstrated a promising miRNAs signature score system for predicting extremely poor prognosis and metastatic potentiality in breast cancer using novel integrated transcriptomics concept. Citation Format: Takabe K, Kawaguchi T, Yan L, Qi Q, Peng X, Young J, Liu S. Integrated transcriptomics analyses identify novel three microRNAs signature to predict poor prognosis and metastasis in breast cancer [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P1-07-31.