Fan Xuehui, Liu Lili, Shi Yue, Guo Fanghan, Wang Haining, Zhao Xiuli, Zhong Di, Li Guozhong
Department of Neurology, The First Affiliated Hospital of Harbin Medical University, 23 You Zheng Street, Harbin, 150001, Heilongjiang Province, People's Republic of China.
World J Surg Oncol. 2020 Aug 22;18(1):222. doi: 10.1186/s12957-020-01995-5.
Although RNA-binding proteins play an essential role in a variety of different tumours, there are still limited efforts made to systematically analyse the role of RNA-binding proteins (RBPs) in the survival of colorectal cancer (CRC) patients.
Analysis of CRC transcriptome data collected from the TCGA database was conducted, and RBPs were extracted from CRC. R software was applied to analyse the differentially expressed genes (DEGs) of RBPs. To identify related pathways and perform functional annotation of RBP DEGs, Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were carried out using the database for annotation, visualization and integrated discovery. Protein-protein interactions (PPIs) of these DEGs were analysed based on the Search Tool for the Retrieval of Interacting Genes (STRING) database and visualized by Cytoscape software. Based on the Cox regression analysis of the prognostic value of RBPs (from the PPI network) with survival time, the RBPs related to survival were identified, and a prognostic model was constructed. To verify the model, the data stored in the TCGA database were designated as the training set, while the chip data obtained from the GEO database were treated as the test set. Then, both survival analysis and ROC curve verification were conducted. Finally, the risk curves and nomograms of the two groups were generated to predict the survival period.
Among RBP DEGs, 314 genes were upregulated while 155 were downregulated, of which twelve RBPs (NOP14, MRPS23, MAK16, TDRD6, POP1, TDRD5, TDRD7, PPARGC1A, LIN28B, CELF4, LRRFIP2, MSI2) with prognostic value were obtained.
The twelve identified genes may be promising predictors of CRC and play an essential role in the pathogenesis of CRC. However, further investigation of the underlying mechanism is needed.
尽管RNA结合蛋白在多种不同肿瘤中发挥着重要作用,但在系统分析RNA结合蛋白(RBPs)在结直肠癌(CRC)患者生存中的作用方面,所做的工作仍然有限。
对从TCGA数据库收集的CRC转录组数据进行分析,从CRC中提取RBPs。应用R软件分析RBPs的差异表达基因(DEGs)。为了识别相关途径并对RBP DEGs进行功能注释,使用注释、可视化和综合发现数据库进行基因本体(GO)功能和京都基因与基因组百科全书(KEGG)途径富集分析。基于相互作用基因检索工具(STRING)数据库分析这些DEGs的蛋白质-蛋白质相互作用(PPIs),并通过Cytoscape软件进行可视化。基于RBPs(来自PPI网络)与生存时间的Cox回归分析,确定与生存相关的RBPs,并构建预后模型。为了验证该模型,将存储在TCGA数据库中的数据指定为训练集,而将从GEO数据库获得的芯片数据作为测试集。然后,进行生存分析和ROC曲线验证。最后,生成两组的风险曲线和列线图以预测生存期。
在RBP DEGs中,314个基因上调,155个基因下调,其中获得了12个具有预后价值的RBPs(NOP14、MRPS23、MAK16、TDRD6、POP1、TDRD5、TDRD7、PPARGC1A、LIN28B、CELF4、LRRFIP2、MSI2)。
所确定的12个基因可能是CRC的有前景的预测指标,并在CRC的发病机制中发挥重要作用。然而,需要进一步研究其潜在机制。