CDK1、CCNB1、CDC20、BUB1、MAD2L1、MCM3、BUB1B、MCM2 和 RFC4 可能是使用综合生物信息学分析治疗肝细胞癌的潜在治疗靶点。

CDK1, CCNB1, CDC20, BUB1, MAD2L1, MCM3, BUB1B, MCM2, and RFC4 May Be Potential Therapeutic Targets for Hepatocellular Carcinoma Using Integrated Bioinformatic Analysis.

机构信息

Laboratory Medicine Center, Lanzhou University Second Hospital, Lanzhou, Gansu 730030, China.

出版信息

Biomed Res Int. 2019 Oct 13;2019:1245072. doi: 10.1155/2019/1245072. eCollection 2019.

DOI:10.1155/2019/1245072

PMID:31737652

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6815605/

Abstract

Hepatocellular carcinoma (HCC) is a malignant tumor with high mortality. The abnormal expression of genes is significantly related to the occurrence of HCC. The aim of this study was to explore the differentially expressed genes (DEGs) of HCC and to provide bioinformatics basis for the occurrence, prevention and treatment of HCC. The DEGs of HCC and normal tissues in GSE102079, GSE121248, GSE84402 and GSE60502 were obtained using R language. The GO function analysis and KEGG pathway enrichment analysis of DEGs were carried out using the DAVID database. Then, the protein-protein interaction (PPI) network was constructed using the STRING database. Hub genes were screened using Cytoscape software and verified using the GEPIA, UALCAN, and Oncomine database. We used HPA database to exhibit the differences in protein level of hub genes and used LinkedOmics to reveal the relationship between candidate genes and tumor clinical features. Finally, we obtained transcription factor (TF) of hub genes using NetworkAnalyst online tool. A total of 591 overlapping up-regulated genes were identified. These genes were related to cell cycle, DNA replication, pyrimidine metabolism, and p53 signaling pathway. Additionally, the GEPIA database showed that the CDK1, CCNB1, CDC20, BUB1, MAD2L1, MCM3, BUB1B, MCM2, and RFC4 were associated with the poor survival of HCC patients. UALCAN, Oncomine, and HPA databases and qRT-PCR confirmed that these genes were highly expressed in HCC tissues. LinkedOmics database indicated these genes were correlated with overall survival, pathologic stage, pathology T stage, race, and the age of onset. TF analysis showed that MYBL2, KDM5B, MYC, SOX2, and E2F4 were regulators to these nine hub genes. Overexpression of CDK1, CCNB1, CDC20, BUB1, MAD2L1, MCM3, BUB1B, MCM2, and RFC4 in tumor tissues predicted poor survival in HCC. They may be potential therapeutic targets for HCC.

摘要

肝细胞癌（HCC）是一种死亡率很高的恶性肿瘤。基因的异常表达与 HCC 的发生有显著关系。本研究旨在探讨 HCC 的差异表达基因（DEGs），为 HCC 的发生、预防和治疗提供生物信息学基础。使用 R 语言从 GSE102079、GSE121248、GSE84402 和 GSE60502 数据库中获取 HCC 和正常组织的 DEGs。使用 DAVID 数据库对 DEGs 进行 GO 功能分析和 KEGG 通路富集分析。然后，使用 STRING 数据库构建蛋白质-蛋白质相互作用（PPI）网络。使用 Cytoscape 软件筛选枢纽基因，并使用 GEPIA、UALCAN 和 Oncomine 数据库进行验证。使用 HPA 数据库展示枢纽基因蛋白水平的差异，使用 LinkedOmics 揭示候选基因与肿瘤临床特征的关系。最后，使用 NetworkAnalyst 在线工具获取枢纽基因的转录因子（TF）。共鉴定出 591 个上调的重叠基因，这些基因与细胞周期、DNA 复制、嘧啶代谢和 p53 信号通路有关。此外，GEPIA 数据库显示 CDK1、CCNB1、CDC20、BUB1、MAD2L1、MCM3、BUB1B、MCM2 和 RFC4 与 HCC 患者的不良生存相关。UALCAN、Oncomine 和 HPA 数据库和 qRT-PCR 证实这些基因在 HCC 组织中高表达。LinkedOmics 数据库表明这些基因与总生存期、病理分期、病理 T 分期、种族和发病年龄相关。TF 分析表明，MYBL2、KDM5B、MYC、SOX2 和 E2F4 是这 9 个枢纽基因的调控因子。肿瘤组织中 CDK1、CCNB1、CDC20、BUB1、MAD2L1、MCM3、BUB1B、MCM2 和 RFC4 的过表达预示着 HCC 患者的不良生存。它们可能是 HCC 的潜在治疗靶点。