Linbo Chen1, Dewen Lu1, Keke Sun1, Yuemei Xu1, Pingping Hu1, Xianpeng Li2, Feng Xu3. 1. Department of Gastroenterology, Ningbo Yinzhou People's Hospital, Ningbo 315040, China. 2. Department of Infectious Diseases, Ningbo Yinzhou People's Hospital, Ningbo 315040, China. Electronic address: 849840290@qq.com. 3. Department of Gastroenterology, Ningbo Yinzhou People's Hospital, Ningbo 315040, China. Electronic address: xufeng200468@126.com.
Abstract
BACKGROUND: The current study aimed to identify potential diagnostic and prognostic gene biomarkers for colorectal cancer (CRC) based on the Gene Expression Omnibus (GEO) datasets and The Cancer Genome Atlas (TCGA) dataset. METHODS: Microarray data of gene expression profiles of CRC from GEO and RNA-sequencing dataset of CRC from TCGA were downloaded. After screening overlapping differentially expressed genes (DEGs) by R software, functional enrichment analyses of the DEGs were performed using the DAVID database. Then, the STRING database and Cytoscape were used to construct a protein-protein interaction (PPI) network and identify hub genes. The receiver operating characteristic (ROC) curves were conducted to assess the diagnostic values of the hub genes. Cox proportional hazards regression was performed to screen the potential prognostic genes. Kaplan-Meier curve and the time-dependent ROC curve were used to assess the prognostic values of the potential prognostic genes for CRC patients. RESULTS: Integrated analysis of GEO and TCGA databases revealed 207 common DEGs in CRC. A PPI network consisted of 70 nodes and 170 edges were constructed and top 10 hub genes were identified. The area under curve (AUC) of the ROC curves of the hub genes were 0.900, 0.927, 0.869, 0.863, 0.980, 0.682, 0.903, 0.790, 0.995, and 0.989 for CCL19, CXCL1, CXCL5, CXCL11, CXCL12, GNG4, INSL5, NMU, PYY, and SST, respectively. A prognostic gene signature consisted of 9 genes including SLC4A4, NFE2L3, GLDN, PCOLCE2, TIMP1, CCL28, SCGB2A1, AXIN2, and MMP1 was constructed with a good performance in predicting overall survivals of CRC patients. The AUC of the time-dependent ROC curve was 0.741 for 5-year survival. CONCLUSION: The results in this study might provide some directive significance for further exploring the potential biomarkers for diagnosis and prognosis prediction of CRC patients.
BACKGROUND: The current study aimed to identify potential diagnostic and prognostic gene biomarkers for colorectal cancer (CRC) based on the Gene Expression Omnibus (GEO) datasets and The Cancer Genome Atlas (TCGA) dataset. METHODS: Microarray data of gene expression profiles of CRC from GEO and RNA-sequencing dataset of CRC from TCGA were downloaded. After screening overlapping differentially expressed genes (DEGs) by R software, functional enrichment analyses of the DEGs were performed using the DAVID database. Then, the STRING database and Cytoscape were used to construct a protein-protein interaction (PPI) network and identify hub genes. The receiver operating characteristic (ROC) curves were conducted to assess the diagnostic values of the hub genes. Cox proportional hazards regression was performed to screen the potential prognostic genes. Kaplan-Meier curve and the time-dependent ROC curve were used to assess the prognostic values of the potential prognostic genes for CRC patients. RESULTS: Integrated analysis of GEO and TCGA databases revealed 207 common DEGs in CRC. A PPI network consisted of 70 nodes and 170 edges were constructed and top 10 hub genes were identified. The area under curve (AUC) of the ROC curves of the hub genes were 0.900, 0.927, 0.869, 0.863, 0.980, 0.682, 0.903, 0.790, 0.995, and 0.989 for CCL19, CXCL1, CXCL5, CXCL11, CXCL12, GNG4, INSL5, NMU, PYY, and SST, respectively. A prognostic gene signature consisted of 9 genes including SLC4A4, NFE2L3, GLDN, PCOLCE2, TIMP1, CCL28, SCGB2A1, AXIN2, and MMP1 was constructed with a good performance in predicting overall survivals of CRC patients. The AUC of the time-dependent ROC curve was 0.741 for 5-year survival. CONCLUSION: The results in this study might provide some directive significance for further exploring the potential biomarkers for diagnosis and prognosis prediction of CRC patients.