RESUMEN
BACKGROUND/AIM: Breast cancer is a common type of cancer in women, and metastasis frequently leads to therapy failure. Using next-generation sequencing (NGS), we aspired to identify the optimal differentially expressed genes (DEGs) for use as prognostic biomarkers for breast cancer. MATERIALS AND METHODS: NGS was used to determine transcriptome profiles in breast cancer tissues and their corresponding adjacent normal tissues from three patients with breast cancer. RESULTS: Herein, 15 DEGs (fold change >4 and <0.25) involved in extracellular matrix (ECM)-receptor interaction signaling were identified through NGS. Among them, our data indicated that high HMMR expression levels were correlated with a poor pathological stage (p<0.001) and large tumor size (p<0.001), whereas high COL6A6 and Reelin (RELN) expression levels were significantly correlated with an early pathological stage (COL6A6: p=0.003 and RELN: p<0.001). Multivariate analysis revealed that high HMMR and SDC1 expression levels were significantly correlated with poor overall survival (OS; HMMR: adjusted hazard ratio [aHR] 1.93, 95% confidence interval [CI]=1.10-3.41, p=0.023; SDC1: [aHR] 2.47, 95%CI=1.28-4.77, p=0.007) for breast cancer. Combined, the effects of HMMR and SDC1 showed a significant correlation with poor OS for patients with breast cancer (high expression for both HMMR and SDC1: [aHR] 3.29, 95%CI=1.52-7.12, p=0.003). CONCLUSION: These findings suggest that HMMR and SDC1 involved in the ECM-receptor interaction signaling pathway could act as effective independent prognostic biomarkers for breast ductal carcinoma.