摘要
目的:基于肿瘤基因组图谱(The Cancer Genome Atlas,TCGA)数据库筛选潜在泛癌生物标志物,为多种肿瘤的诊断和预后评估提供帮助。方法:利用“GDC Data Transfer Tool”和“GDCRNATools”软件包获取TCGA数据库,完成数据整理,将13种肿瘤纳入研究。以错误发现率(false discovery rate,FDR)<0.05且差异倍数(fold change,FC)>1.5作为差异表达标准,筛选在13种肿瘤中均上调或均下调的基因和微小RNA(microRNAs,miRNAs)。利用受试者工作特征曲线(receiver operating characteristic curve,ROC曲线)的曲线下面积(area under the curve,AUC)、最佳截断值及对应的灵敏度和特异度反映诊断价值。利用Kaplan-Meier法计算生存概率后进行对数秩(log-rank)检验并计算风险比(hazard ratio,HR)反映预后评估价值。利用DAVID工具对差异表达基因进行GO(Gene Ontology)、KEGG(Kyoto Encyclopedia of Genes and Genomes)富集分析。利用STRING和TargetScan工具对差异表达基因和miRNAs进行调控网络分析。结果:共发现48个基因和2个miRNAs在13种肿瘤中均差异表达,其中25个基因均表达上调,23个基因和2个miRNAs均表达下调。多数差异表达基因和miRNAs区分病例和对照的能力较好,AUC、灵敏度和特异度可达0.8~0.9。生存分析结果显示,差异表达基因和miRNAs与多种肿瘤患者的生存显著相关,且多数上调基因是患者生存的危险因素(HR>1),而多数下调基因是患者生存的保护因素(0<HR<1)。GO和KEGG富集分析显示,差异表达基因多富集于与细胞增殖有关的生物学事件。在调控网络分析中,共13个基因和2个miRNAs存在调控和相互作用关系。结论:在13种肿瘤中均差异表达的48个基因和2个miRNAs可能作为潜在泛癌生物标志物,为多种肿瘤的诊断和预后评估提供帮助,并为发展肿瘤治疗的广谱靶点提供线索。
Objective:To screen potential pan-cancer biomarkers based on The Cancer Genome Atlas(TCGA)database,and to provide help for the diagnosis and prognosis assessment of a variety of cancers.Methods:“GDC Data Transfer Tool”and“GDCRNATools”packages were used to obtain TCGA database.After data sorting,a total of 13 cancers were selected for further analysis.False disco-very rate(FDR)<0.05 and fold change(FC)>1.5 were used as the differential expression criteria to screen genes and miRNAs that were up-or down-regulated in all the 13 cancers.In the receiver operating characteristic curve(ROC curve),the area under the curve(AUC),the best cut-off value and the corresponding sensitivity and specificity were used to reflect diagnostic significance.The Kaplan-Meier method was used to calculate the survival probability and then the log-rank test was performed.Hazard ratio(HR)was calculated to reflect prognostic evaluation significance.DAVID tool were used to perform GO(Gene Ontology)and KEGG(Kyoto Encyclopedia of Genes and Genomes)enrichment analysis for differentially expressed genes.STRING and TargetScan tools were used to analyze the regulatory network of differentially expressed genes and miRNAs.Results:A total of 48 genes and 2 miRNAs were differentially expressed in all the 13 cancers.Among them,25 genes were up-regulated,23 genes and 2 miRNAs were down-regulated.Most differentially expressed genes and miRNAs had good ability to distinguish between the cases and controls,with AUC,sensitivity and specificity up to 0.8-0.9.Survival analysis results show that differentially expressed genes and miRNAs were significantly associated with patient survival in a variety of cancers.Most up-regulated genes were risk factors for patient survival(HR>1),while most down-regulated genes were protective factors for patient survival(0<HR<1).The enrichment analysis of GO and KEGG showed that the differentially expressed genes were mostly enriched in biological events related to cell proliferation.In the regulatory network analysis,a
作者
周川
马雪
邢云昆
李璐迪
陈洁
姚碧云
傅娟玲
赵鹏
ZHOU Chuan;MA Xue;XING Yun-kun;LI Lu-di;CHEN Jie;YAO Bi-yun;FU Juan-ling;ZHAO Peng(Department of Toxicology, Beijing Key Laboratory of Toxicological Research and Risk Assessment for Food Safety, Peking University School of Public Health, Beijing 100191, China)
出处
《北京大学学报（医学版）》
CAS
CSCD
北大核心
2021年第3期602-607,共6页
Journal of Peking University:Health Sciences
基金
国家自然科学基金(81370079、81001253)
北京市自然科学基金(7132122)。
关键词
泛癌
生物标记
肿瘤
基因表达调控
基因组
人
Pan-cancer
Biomarkers,tumor
Gene expression regulation
Genome,human
