Development of a Prognostic Model Based on Identification of EMT-related LncRNAs in Triple Negative Breast Cancer
Abstract Background: Triple negative breast cancer (TNBC) remains the most incurable subtype of breast cancer owing to high heterogeneity, aggressive nature, and lack of treatment options. It is generally acknowledged that epithelial-mesenchymal transition (EMT) is the key step in tumor metastasis. Methods: With the application of TCGA and GEO database, we identified EMT-related lncRNAs by Cox univariate regression analysis. Optimum risk scores were calculated and used to divide TNBC patients into high/low-risk subgroups by the median value using lasso regression analysis. Kaplan-Meier and ROC curve analyses were applied for model validation. Then we assessed the risk model from multi-omic aspects including immune infiltration, drug sensitivity, mutability spectrum, signaling pathways, and clinical indicators.Results: The risk model was composed of 22 EMT-related long noncoding RNAs (lncRNAs), which seemed to be valuable in prognostic prediction of TNBC patients. The model could act as an independent prognostic factor of TNBC, and showed a robust prognostic ability in the stratification analysis. Conclusions: Together, our study successfully established a risk model with great accuracy and efficacy in prognosis prediction of TNBC patients.