人肝细胞癌预后不良相关基因的生物信息学分析及其临床意义

doi:10.12047/j.cjap.5764.2019.021

中国应用生理学杂志 ›› 2019, Vol. 35 ›› Issue (1): 90-96.doi: 10.12047/j.cjap.5764.2019.021

• 研究论文 • 上一篇

人肝细胞癌预后不良相关基因的生物信息学分析及其临床意义

席义博^1,2+,张皓旻^1,2+,杨波²,陈熙勐^1,2,贺培凤^1△,卢学春^2,1△

1. 山西医科大学管理学院, 太原 030001;
2. 解放军总医院南楼血液科, 国家老年疾病临床医学研究中心北京 100853

收稿日期:2018-10-09 出版日期:2019-01-28 发布日期:2019-06-27
通讯作者: ^△ Tel: 13241892863, 13934569928; E-mail: luxuechun@126.com, hepeifeng2006@126.com. +: 共同第一作者
基金资助:
2017年度国家老年疾病临床医学研究中心招标课题( NCRCG-PLAGH-2017011);解放军总医院转化医学项目(2017TM-020); 山西省重点研发计划项目(201803D31067)

Bioinformatics analysis of genes related to poor prognosis of human hepatocellular carcinoma and its clinical significance

XI Yi-bo^1,2+, ZHANG Hao-min^1,2+, YANG Bo², CHEN Xi-meng^1,2, HE Pei-feng^1△, LU Xue-chun^2,1△

1. School of Management, Shanxi Medical University, Taiyuan 030001;
2. Department of Hematology, South Building, General Hospital of the People's Liberation Army, National Center for Clinical Research of Geriatric Diseases, Beijing 100853, China

Received:2018-10-09 Online:2019-01-28 Published:2019-06-27

摘要/Abstract

摘要： 目的:筛选肝细胞癌(HCC)预后不良相关基因,并探讨其临床意义。方法:在基因表达综合数据库(GEO)中获取符合分析条件的肝细胞癌全基因组表达谱数据并分析得到差异表达基因(DEGs),再运用生物学信息注释及可视化数据库 (DAVID) 和蛋白相互作用数据库 (String) 分别进行功能富集分析和蛋白质互作用网络的构建。利用癌症基因组图谱数据库(TCGA)和Cox比例风险回归模型对相关差异基因进行预后分析。结果:找到一个符合条件的人类HCC数据库 (GSE84402),共筛选出1141个差异表达基因(DEGs),其中上调基因720个,下调基因421个。基因功能富集分析和蛋白质互作用分析结果显示CDK1、CDC6、CCNA2、CHEK1、CENPE 、PIK3R1、RACGAP1、BIRC5、KIF11和CYP2B6为HCC预后的关键基因。TCGA数据库和Cox回归模型分析显示CDC6、PIK3R1、RACGAP1和KIF11的表达升高,CENPE的表达降低与HCC预后不良密切相关。结论:CDC6、CENPE、PIK3R1、RACGAP1和KIF11可能和HCC的预后不良相关,可作为未来HCC预后研究的参考标志物。

关键词: 肝细胞癌, 预后不良基因, 生物信息学, Cox比例风险回归模型

Abstract: Objective:To screen genes associated with poor prognosis of hepatocellular carcinoma (HCC) and to explore the clinical significance of these genes. Methods: The proper expression profile data of HCC was obtained from the Gene Expression Omnibus (GEO) database, and the differentially expressed genes (DEGs) were identified by differential expression analysis. The DAVID and String database were used for function enrichment analysis and to construct the protein-protein interaction (PPI) network respectively. The Cancer Genome Atlas (TCGA) database and the Cox Proportional Hazard Model were used for prognosis analysis of the DEGs. Results: A eligible human HCC data set (GSE84402) met the requirements. A total of 1141 differentially expressed genes were identified, including 720 up-regulated and 421 down-regulated genes. The results of function enrichment analysis and PPI network performed that CDK1、CDC6、CCNA2、CHEK1、CENPE 、PIK3R1、RACGAP1、BIRC5、KIF11 and CYP2B6 were prognosis key genes. And the prognosis analysis showed that the expressions of CDC6、PIK3R1、KIF11 and RACGAP1 were increased, and the expression of CENPE was decreased, which was closely related to prognosis of HCC. Conclusion: CDC6、CENPE、PIK3R1、KIF11 and RACGAP1 may be closely related to poor prognosis of HCC, and can be used as molecular biomarkers for future research of HCC prognosis.

Key words: hepatocellular carcinoma, poor prognosis genes, bioinformatics, Cox Proportional Hazard Model

席义博, 张皓旻, 杨波, 陈熙勐, 贺培凤, 卢学春. 人肝细胞癌预后不良相关基因的生物信息学分析及其临床意义[J]. 中国应用生理学杂志, 2019, 35(1): 90-96.

XI Yi-bo, ZHANG Hao-min, YANG Bo, CHEN Xi-meng, HE Pei-feng, LU Xue-chun. Bioinformatics analysis of genes related to poor prognosis of human hepatocellular carcinoma and its clinical significance[J]. CJAP, 2019, 35(1): 90-96.

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人肝细胞癌预后不良相关基因的生物信息学分析及其临床意义

Bioinformatics analysis of genes related to poor prognosis of human hepatocellular carcinoma and its clinical significance

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