Evaluation of the Role of hsa-mir-124 in Predicting Clinical Outcome in Breast Invasive Carcinoma Based on Bioinformatics Analysis

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Abstract

Purpose. Breast invasive carcinoma (BRCA) is the most common malignant tumor. MiR-124 plays a tumor-suppressive role in human cancer. However, the clinical significance of miR-124 in BRCA remains unclear. The aim of this study was to evaluate the association of hsa-mir-124 expression and the clinicopathological characteristics in BRCA using database analysis. Methods. The clinical data and expression profiles of hsa-mir-124 were obtained from the cancer genome atlas for BRCA (TCGA-BRCA). Then, the prognostic value of hsa-mir-124 in BRCA was investigated using the Cox Regression test, and the association of hsa-mir-124 and pathology TNM stages and pathologic stages were measured by the Kruskal-Wallis test and Wilcox. test. In addition, the association of hsa-mir-124 and tumor molecular phenotypes was performed using the Chi-Square test. Results. We found that the overall survival of patients with high expression of hsa-mir-124-1 and hsa-mir-124-2 was better than that of patients with low expression of hsa-mir-124-1 and hsa-mir-124-2. And the expression of hsa-mir-124-1, hsa-mir-124-2, and hsa-mir-124-3 was mainly enriched in T1/T2 stages, NO/N1 stages, and M0 stages. Then, the expression of hsa-mir-124-1, hsa-mir-124-2, and hsa-mir-124-3 was negatively associated with tumor lymph node metastasis. Moreover, the expression of hsa-mir-124 was associated with tumor molecular phenotype in breast invasive carcinoma. Conclusion. Our findings indicated that hsa-mir-124 expressions were associated with overall survival, TNM stages, pathologic characteristics, and tumor molecular phenotype in BRCA via TCGA-BRCA database, providing a new biomarker and a potential therapeutic target for BRCA patients.

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Feng, T., Zhang, P., Sun, Y., Han, X., Tong, J., & Hua, Z. (2020). Evaluation of the Role of hsa-mir-124 in Predicting Clinical Outcome in Breast Invasive Carcinoma Based on Bioinformatics Analysis. BioMed Research International, 2020. https://doi.org/10.1155/2020/1839205

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