Identification of biomarkers associated with diagnosis and prognosis of colorectal cancer patients based on integrated bioinformatics analysis
Identification of biomarkers associated with diagnosis and prognosis of colorectal cancer patients based on integrated bioinformatics analysis
复制标题
DOI：
10.1016/j.gene.2019.01.001
复制
发表时间：
2019-04-15
期刊：
影响因子：
3.5
通讯作者：
Xu, Feng
中科院分区：
文献类型：
--
作者：
Chen, Linbo;Lu, Dewen;Xu, Feng
背景资料：本研究旨在基于GEO和TCGA数据集，筛选大肠癌（CRC）诊断和预后的潜在基因标志物。用R软件筛选重叠差异表达基因（DEG）后，利用大卫数据库对DEG进行功能富集分析。然后，使用STRING数据库和Cytoscape构建蛋白质蛋白质相互作用（PPI）网络，并确定枢纽基因。应用受试者工作特征曲线（ROC）评价枢纽基因的诊断价值。应用考克斯比例风险回归分析筛选潜在的预后相关基因。结果：综合分析GEO和TCGA数据库，共发现207个大肠癌常见DEG基因。构建了由70个节点和170条边组成的PPI网络，并确定了前10个枢纽基因。对于CCL19、CXCL1、CXCL5、CXCL11、CXCL12、GNG 4、INSL 5、NMU、PYY和SST，中枢基因的ROC曲线的曲线下面积（AUC）分别为0.900、0.927、0.869、0.863、0.980、0.682、0.903、0.790、0.995和0.989。由SLC4A4、NFE2L3、GLDN、PCOLCE2、TIMP1、CCL28、SCGB2A1、AXIN2和MMP 1等9个基因组成的预后基因标签在预测结直肠癌患者的总生存率方面具有良好的性能。5年生存率的ROC曲线AUC为0.741。结论：本研究结果对进一步探索大肠癌诊断和预后预测的潜在生物标志物具有一定的指导意义。
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.