肺腺癌相关基因的生物信息学分析
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摘要
目的:通过生物信息学分析基因表达谱,获取肺腺癌相关基因及信号通路。方法:从GEO数据库下载GSE40791、GSE68571、GSE43458和GSE18842表达数据,将4个微阵列数据集整合获得肺腺癌相关差异表达基因,利用STRING数据库为差异表达基因构建肺腺癌蛋白-蛋白互相作用网络,并挖掘肺腺癌网络中基因模块及关键基因。通过DAVID对各基因模块进行基因富集分析,发掘基因模块在肺腺癌细胞中所执行的调控功能及模块中关键基因与患者的预后关系。结果:初步筛查获得肺腺癌相关37个上调基因、120个下调基因,并成功构建蛋白-蛋白相互作用网络,通过MCODE算法在蛋白-蛋白相互作用网络中构建基因模块以及计算关键基因(KIF14,SEPP1,SPP1,RBP4),最终获得的4个模块分别参与细胞周期、血凝变化、细胞黏附和细胞代谢的调控。经验证4个关键基因在肺腺癌和正常组织中有明显表达差异(P<0.05)。生存分析显示KIF14的表达对肺腺癌的预后有显著影响(P<0.01),SEPP1、SPP1对患者生存率有显著影响(P<0.05),RBP4对患者的生存率影响无统计学意义(P>0.05)。结论:通过生物信息方法分析肺腺癌和癌旁正常组织的差异基因表达,最终筛选出3个差异表达非常显著且对患者预后影响明显的基因,对肺腺癌的诊断和预后治疗提供了新思路,提高肺腺癌机制的研究效率。
Objective: To indentify the candidate genes and signaling pathways in lung adenocarcinoma by analyzing gene profiles with bioinformatics. Methods: The expression profiles of GSE40791, GSE68571, GSE43458, and GSE18842 were down-loaded from the Gene Expression Omnibus(GEO) database. The four microarray datasets were integrated to obtain the differentially expressed genes related to lung adenocarcinoma. STRING database was used to construct the protein-protein interaction(PPI) network of differentially expressed genes, and to further explore the gene modules and the key genes. DAVID was used to perform the gene enrichment analysis of each gene module, and to explore the regulatory function of each gene module in adenocarcinoma cells, as well as the relationship between the key genes in the module and the prognosis of the patients. Results: Thirty-seven up-regulated genes and120 down-regulated genes were obtained from the primary screen, and the protein-protein interaction(PPI) network was successfully constructed. According to MCODE algorithm, we constructed gene modules and calculated the core genes(KIF14, SEPP1, SPP1,RBP4) in the PPI network. Finally, four modules were proved to be involved in regulation of cell cycle, blood coagulation, cell adhesion and cell metabolism, and four key genes were proved to be differentially expressed between lung adenocarcinoma tissues and normal tissues(all P<0.05). Survival analysis showed that expressions of KIF14, SEPP1 and SPP1 had significant effect on the prognosis of lung adenocarcinoma(P<0.01 or P<0.05), while RBP4 exerted insignificant difference in the survival rate of lung adenocarcinoma patients(P>0.05). Conclusion: With bioinformatics, three differentially expressed genes between lung adenocarcinoma tissues and normal adjacent tissues were finally screened out and proved to be closely related to the prognosis of patients, which provided new thoughts in the diagnosis and prognosis prediction of lung adenocarcinoma and improved the study efficiency on the mechanism of lung adenocarcinoma.
Objective: To indentify the candidate genes and signaling pathways in lung adenocarcinoma by analyzing gene profiles with bioinformatics. Methods: The expression profiles of GSE40791, GSE68571, GSE43458, and GSE18842 were down-loaded from the Gene Expression Omnibus(GEO) database. The four microarray datasets were integrated to obtain the differentially expressed genes related to lung adenocarcinoma. STRING database was used to construct the protein-protein interaction(PPI) network of differentially expressed genes, and to further explore the gene modules and the key genes. DAVID was used to perform the gene enrichment analysis of each gene module, and to explore the regulatory function of each gene module in adenocarcinoma cells, as well as the relationship between the key genes in the module and the prognosis of the patients. Results: Thirty-seven up-regulated genes and120 down-regulated genes were obtained from the primary screen, and the protein-protein interaction(PPI) network was successfully constructed. According to MCODE algorithm, we constructed gene modules and calculated the core genes(KIF14, SEPP1, SPP1,RBP4) in the PPI network. Finally, four modules were proved to be involved in regulation of cell cycle, blood coagulation, cell adhesion and cell metabolism, and four key genes were proved to be differentially expressed between lung adenocarcinoma tissues and normal tissues(all P<0.05). Survival analysis showed that expressions of KIF14, SEPP1 and SPP1 had significant effect on the prognosis of lung adenocarcinoma(P<0.01 or P<0.05), while RBP4 exerted insignificant difference in the survival rate of lung adenocarcinoma patients(P>0.05). Conclusion: With bioinformatics, three differentially expressed genes between lung adenocarcinoma tissues and normal adjacent tissues were finally screened out and proved to be closely related to the prognosis of patients, which provided new thoughts in the diagnosis and prognosis prediction of lung adenocarcinoma and improved the study efficiency on the mechanism of lung adenocarcinoma.
引文
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[2]周宝森.女性肺腺癌危险因素分析[J].中国公共卫生,2000,16(6):536-539.DOI:10.11847/zgggws2000-16-06-49.
[3]全斌,喻艳林.肺结核合并肺癌的发生机制研究进展[J].山东医药,2015,55(24):104-106.DOI:10.3969/j.issn.1002-266X.2015.24.047.
[4]GU C,SHEN T.cDNA microarray and bioinformatic analysis for the identification of key genes in Alzheimer's disease[J].Int J Mol Med,2014,33(2):457-461.DOI:10.3892/ijmm.2013.1575.
[5]李瑶.基因芯片数据分析与处理[M].北京:化学工业出版社,2006:7-9.
[6]LUZZI V I,HOLTSCHLAG V,WATSON M A.Gene expression profiling of primary tumor cell populations using laser capture microdissection,rna transcript amplification,and genechip?microarrays[J].Methods Mol Biol,2005,293:187-207.DOI:10.1385/1-59259-853-6:187.
[7]李燕妮,齐士勇,颜艳,等.基于Keap1基因多态性的肾透明细胞癌分子标志物的研究[J].中国肿瘤生物治疗杂志,2017,24(3):278-283.DOI:10.3872/j.issn.1007-385X.2017.03.011.
[8]RADICCHI F,CASTELLANO C,CECCONI F,et al.Defining and identifying communities in networks[J].P NATL ACAD SCI USA,2004,101(9):2658-2663.DOI:10.1073/pnas.0400054101.
[9]张健,王冬,于景翠.胃癌中miRNA功能相关的信号通路[J].中国肿瘤生物治疗杂志,2017,24(11):1331-1335.DOI:10.3872/j.issn.1007-385X.2017.11.016.
[10]MASTERS G A,JOHNSON D H,TEMIN S.Systemic therapy for stageⅣnon-small-cell lung cancer:american society of clinical oncology clinical practice guideline update[J].J Oncol Pract,2017,33(30):832-837.DOI:10.1200/JCO.2015.62.1342.
[11]FU Q,YANG F,ZHAO J,et al.Bioinformatical identification of key pathways and genes in human hepatocellular carcinoma after CSN5 depletion[J].Cell Signal,2018,49:79-86.DOI:10.1016/j.cellsig.2018.06.002.
[12]HANAHAN D,WEINBERG R A.Hallmarks of cancer:the next generation[J].Cell,2011,144(5):646-676.DOI:10.1016/j.cell.2011.02.013.
[13]WANG Q,WANG L,LI D,et al.Kinesin family member 14 is a candidate prognostic marker for outcome of glioma patients[J].Cancer Epidemiol,2013,37(1):79-84.DOI:10.1016/j.canep.2012.08.011.
[14]SHAH S P,ROTH A,GOYA R,et al.The clonal and mutational evolution spectrum of primary triple negative breast cancers[J].Nature,2012,486(7403):395-399.DOI:10.1038/nature10933.
[15]YANG T,ZHANG X B,ZHENG Z M.Suppression of KIF14 expression inhibits hepatocellular carcinoma progression and predicts favorable outcome[J].Cancer Sci,2013,104(5):552-557.DOI:10.1111/cas.12128.
[16]陈宗营,马恒.KIF14在胃癌中的表达及其意义[J].中国现代普通外科进展,2011,14(12):941-944.DOI:10.3969/j.issn.1009-9905.2011.12.005.
[17]TH RIAULT B L,PAJOVIC S,BERNARDINI M Q,et al.Kinesin family member 14:an independent prognostic marker and potential therapeutic target for ovarian cancer[J].INT J Cancer,2012,130(8):1844-1854.DOI:10.1002/ijc.26189.
[18]HUNG P F,HONG T M,HSU Y C,et al.The motor protein kif14inhibits tumor growth and cancer metastasis in lung adenocarcinoma[J].PLoS One,2013,8(4):e61664[2018-12-23].https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0061664.DOI:10.1371/journal.pone.0061664.
[19]EPPLEIN M,BURK R F,CAI Q,et al.A prospective study of plasma selenoprotein p and lung cancer risk among low-income adults[J].Cancer Epidemiol Biomarkers Prev,2014,23(7):1238-1244.DOI:10.1158/1055-9965.EPI-13-1308.
[20]OLDBERG A,FRANZ N A,HEINEG RD D.Cloning and sequence analysis of rat bone sialoprotein(osteopontin)c DNA reveals an ARG-Gly-Asp cell-binding sequence[J].Proc Natl Acad Sci USA,1986,83(23):8819-8823.DOI:10.1073/pnas.83.23.8819.