Ming-Hsin Yeh1,2,3, Yau-Jin Tzeng4,5, Ting-Ying Fu6, Jun-Jie You4, Hong-Tai Chang1, Luo-Ping Ger4, Kuo-Wang Tsai7,5,8. 1. Department of Surgery, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan, R.O.C. 2. Department of Surgery, Chung Shan Medical University Hospital, Taichung, Taiwan, R.O.C. 3. School of Medicine, Chung Shan Medical University, Taichung, Taiwan, R.O.C. 4. Department of Medical Education and Research, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan, R.O.C. 5. Institute of Biomedical Sciences, National Sun Yat-Sen University, Kaohsiung, Taiwan, R.O.C. 6. Department of Pathology and Laboratory Medicine, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan, R.O.C. 7. Department of Medical Education and Research, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan, R.O.C. kwtsai6733@gmail.com. 8. Department of Chemical Biology, National Pingtung University of Education, Pingtung, Taiwan, R.O.C.
Abstract
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. Copyright
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. Copyright
Authors: Charles R Schutt; Hua Sun; Jaya Sarin Pradhan; Yvonne Saenger; Jessica Ley; Douglas Adkins; Matthew Ingham; Li Ding; Brian A Van Tine Journal: Oncotarget Date: 2021-03-16