Yongbin Lu1,2, Gang Yang3, Yi Xiao4, Tao Zhang5, Fei Su5, Ruixia Chang6, Xiaoling Ling7, Yana Bai8,9. 1. Scientific Development and Planning Department, The First Hospital of Lanzhou University, Lanzhou, China. 2. College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, China. 3. Neurosurgery, The First Hospital of Lanzhou University, Lanzhou, China. 4. Breast Surgery, The First Hospital of Lanzhou University, Lanzhou, China. 5. Department of Oncology, The First Hospital of Lanzhou University, Lanzhou, 730000, People's Republic of China. 6. School of Public Health, Huazhong University of Science and Technology, Wuhan, China. 7. Department of Oncology, The First Hospital of Lanzhou University, Lanzhou, 730000, People's Republic of China. ldyy_lingxl@lzu.edu.cn. 8. College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, China. Yanabai@lzu.edu.cn. 9. Epidemiology and Health Statistics, School of Public Health, Lanzhou University, Lanzhou, 730000, People's Republic of China. Yanabai@lzu.edu.cn.
Abstract
BACKGROUND: Triple-negative breast cancer (TNBC) is one of the leading causes of death among females around the world. However, the molecular mechanism of the disease among TNBC patients remains to be further studied. METHODS: In our study, four microarray data and two high throughput sequencing data were acquired from the GEO database, and the differentially expressed genes (DEGs) between TNBC and normal tissues had been analyzed. Analysis of functional enrichment and pathway enrichment of DEGs was conducted by the Funrich software, and protein-protein interaction (PPI) network gained from the STRING, and hub genes were confirmed by the Cytoscape. Kaplan-Meier plotter (KM plotter) online dataset had been used to analyze DEGs of overall survival (OS), and progression-free survival (PFS). RESULTS: In total, 1638 DEGs were gained in our study covering 984 upregulated and 654 downregulated genes. Moreover, a PPI network was constructed, and cyclin-dependent kinase 1 (CDK1), cyclin B1 (CCNB1), and cyclin A2 (CCNA2) were found as top genes with higher node degrees. CDK1, CCNA2, and CCNB1were obviously enriched in the cell cycle. The top upregulated genes including CDK1, CCNB1, CCNA2, and PLK1 were overexpressed in TNBC, and correlated with worse OS in breast cancer. High expression of CCNB1 was correlated with worse PFS in TNBC (HR = 1.42, 95% CI: 1.04-1.94, P = 0.028). Besides, there was a correlation between CCNB1 and CDK1 in TNBC, as well as between CCNA2 and CDK1 (r = 0.804, P < 0.001; r = 0.577, P < 0.001, respectively). CONCLUSION: Our results suggest that cyclin CDK1, CCNB1, and CCNA2 are overexpressed in TNBC and they could act as novel biomarkers for the diagnosis and treatment of TNBC.
BACKGROUND: Triple-negative breast cancer (TNBC) is one of the leading causes of death among females around the world. However, the molecular mechanism of the disease among TNBC patients remains to be further studied. METHODS: In our study, four microarray data and two high throughput sequencing data were acquired from the GEO database, and the differentially expressed genes (DEGs) between TNBC and normal tissues had been analyzed. Analysis of functional enrichment and pathway enrichment of DEGs was conducted by the Funrich software, and protein-protein interaction (PPI) network gained from the STRING, and hub genes were confirmed by the Cytoscape. Kaplan-Meier plotter (KM plotter) online dataset had been used to analyze DEGs of overall survival (OS), and progression-free survival (PFS). RESULTS: In total, 1638 DEGs were gained in our study covering 984 upregulated and 654 downregulated genes. Moreover, a PPI network was constructed, and cyclin-dependent kinase 1 (CDK1), cyclin B1 (CCNB1), and cyclin A2 (CCNA2) were found as top genes with higher node degrees. CDK1, CCNA2, and CCNB1were obviously enriched in the cell cycle. The top upregulated genes including CDK1, CCNB1, CCNA2, and PLK1 were overexpressed in TNBC, and correlated with worse OS in breast cancer. High expression of CCNB1 was correlated with worse PFS in TNBC (HR = 1.42, 95% CI: 1.04-1.94, P = 0.028). Besides, there was a correlation between CCNB1 and CDK1 in TNBC, as well as between CCNA2 and CDK1 (r = 0.804, P < 0.001; r = 0.577, P < 0.001, respectively). CONCLUSION: Our results suggest that cyclin CDK1, CCNB1, and CCNA2 are overexpressed in TNBC and they could act as novel biomarkers for the diagnosis and treatment of TNBC.
Entities:
Keywords:
Biomarkers; CCNA2; CCNB1; CDK1; Triple-negative breast cancer