基于生物信息数据探索CCNB1、CCNB2和CDK1在肺腺癌中的作用  

Exploring the role of CCNB1,CCNB2 and CDK1 in lung adenocarcinoma based on bioinformatics data

作　　者：陆思芬 魏小珍 杨声英 杨浩[4] 陈勃江[1,5] 李为民[1,5] LU Sifen;WEI Xiaozhen;YANG Shengying;YANG Hao;CHEN Bojiang;LI Weimin(Precision Medicine Key Laboratory of Sichuan Province and Precision Medicine Center,West China Hospital,Sichuan University,Chengdu,Sichuan 610041,P.R.China;Department of Anesthesiology,West China Hospital,Sichuan University,Chengdu,Sichuan 610041,P.R.China;Department of Computer&Software,Jincheng College of Chengdu,Chengdu,Sichuan 611700,P.R.China;Transplantation Immunology Laboratory,West China Hospital,Sichuan University,Chengdu,Sichuan 610041,P.R.China;Department of Pulmonary and Critical Care Medicine,West China Hospital,Sichuan University,Chengdu,Sichuan 610041,P.R.China)

机构地区：[1]四川大学华西医院精准医学中心,精准医学四川省重点实验室,成都610041 [2]四川大学华西医院麻醉科,成都610041 [3]成都锦城学院计算机与软件学院大数据系,成都611700 [4]四川大学华西医院移植免疫研究室,成都610041 [5]四川大学华西医院呼吸与危重症医学科,成都610041

出　　处：《华西医学》2023年第1期18-27,共10页West China Medical Journal

基　　金：国家自然科学基金(82173727);成都市科技项目(2021-YF05-01480-SN)。

摘　　要：目的利用生物信息数据探索细胞周期蛋白B1(cyclin B1,CCNB1)、细胞周期蛋白B2(cyclin B2,CCNB2)和细胞周期蛋白依赖性激酶1(cyclin dependent kinase 1,CDK1)在肺腺癌中的作用。方法首先从基因表达综合数据库(Gene Expression Omnibus,GEO)下载2个数据集的RNA高通量测序表达量数据,采用DESeq2软件鉴定差异表达基因,基于差异表达基因的结果进行后续分析:构建蛋白互作网络,选出其中最有意义的模块,这些模块中包括的基因即为核心基因;用来自其他数据库的RNA高通量测序数据验证核心基因的表达情况,验证其是否为差异表达基因;对经验证确定为差异表达基因的核心基因行生存曲线分析,筛选对肺腺癌生存期有显著影响的核心基因;分析这些核心基因在肺腺癌不同时期的表达情况。然后用KOBAS数据库对筛选出的核心基因行京都基因和基因组数据库通路富集分析,用muTarget工具将筛选出的核心基因表达量与肺腺癌基因突变状态进行关联,基于GDSC数据库的药物信息,探索这些核心基因在肺腺癌治疗中的潜在价值。最后通过人类蛋白质图谱数据库再次验证这些核心基因在肺腺癌中的表达情况。结果基于GEO中数据集筛选出594个上调、651个下调的差异表达基因(P<0.05),其中30个核心基因经筛选用于后续分析;其他数据库的RNA高通量测序数据验证出18个基因是差异表达的;其中8个基因的无病生存率在肺腺癌中有统计学意义(P<0.05)。这8个基因在肺腺癌的不同时期都是差异表达的,在肺腺癌中后期表达量更高。8个基因中,CCNB1、CCNB2和CDK1可在细胞周期通路中显著富集。在肺腺癌中,CCNB1、CCNB2和CDK1的表达量与TP53突变状态相关。另外,CDK1与4种药物有关,揭示了CDK1在肺腺癌治疗中的潜在价值。人类蛋白质图谱数据库中的免疫组织化学数据从蛋白质水平再次验证了CCNB1、CCNB2和CDK1在肺腺癌中高表达。结�Objective To explore the role of cyclin B1(CCNB1),cyclin B2(CCNB2)and cyclin dependent kinase1(CDK1)in lung adenocarcinoma(LUAD)using bioinformatic data.Methods First,RNA expression data were downloaded from two datasets in Gene Expression Omnibus(GEO),and DESeq2 software was used to identify deferentially expressed genes(DEGs).Subsequent analyses were conducted based on the results of these DEGs:proteinprotein interaction(PPI)network was constructed with STRING database;the modules in PPI network were analyzed by Molecular Complex Detection software,and the most significant modules were selected,the genes included in these modules were the hub genes;high-throughput RNA sequencing data from other databases were used to verify the expression of these hub genes to confirm whether they were DEGs;survival curve analyses of the confirmed DEGs were conducted to select genes that had significant influence on the survival of LUAD;the expression of these hub genes in different stages of LUAD were also analyzed.Then,Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis was performed for these selected hub genes using KOBAS database.MuTarget tool was used to analyze the correlations between the expression of these selected hub genes and gene mutation status in LUAD.The potential value of these hub genes in the treatment of LUAD was explored based on the drug information in GDSC database.Finally,immunohistochemical data from Human Protein Atlas(HPA)database were used to verify the expression of these hub genes in LUAD again.Results According to the expression data in GEO,594 up-regulated genes and 651 downregulated genes were identified(P<0.05),among which 30 hub genes were selected for subsequent analyses.The RNA high-throughput sequencing data of other databases verified that 18 genes were DEGs,among which 8 hub genes had significant impact on disease-free survival in LUAD(P<0.05).Moreover,the 8 genes were differentially expressed in different stages of LUAD,which were higher in the middle and late stage of L

关 键 词：肺腺癌 差异表达基因 核心基因 预后标志物 治疗靶标 

分 类 号：R734.2[医药卫生—肿瘤] Q811.4[医药卫生—临床医学]