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北京大学学报（医学版） ›› 2021, Vol. 53 ›› Issue (3): 602-607. doi: 10.19723/j.issn.1671-167X.2021.03.028

• 技术方法 • 上一篇    下一篇

基于肿瘤基因组图谱数据库探索性筛选潜在泛癌生物标志物

周川,马雪,邢云昆,李璐迪,陈洁,姚碧云,傅娟玲,赵鹏Δ()   

  1. 北京大学公共卫生学院毒理学系,食品安全毒理学研究与评价北京市重点实验室,北京 100191
  • 收稿日期:2020-11-02 出版日期:2021-06-18 发布日期:2021-06-16
  • 通讯作者: 赵鹏 E-mail:zhaopeng@bjmu.edu.cn
  • 基金资助:
    国家自然科学基金(81370079);国家自然科学基金(81001253);北京市自然科学基金(7132122)

Exploratory screening of potential pan-cancer biomarkers based on The Cancer Genome Atlas database

ZHOU Chuan,MA Xue,XING Yun-kun,LI Lu-di,CHEN Jie,YAO Bi-yun,FU Juan-ling,ZHAO PengΔ()   

  1. Department of Toxicology, Beijing Key Laboratory of Toxicological Research and Risk Assessment for Food Safety, Peking University School of Public Health, Beijing 100191, China
  • Received:2020-11-02 Online:2021-06-18 Published:2021-06-16
  • Contact: Peng ZHAO E-mail:zhaopeng@bjmu.edu.cn
  • Supported by:
    National Natural Science Foundation of China(81370079);National Natural Science Foundation of China(81001253);Beijing Natural Science Foundation(7132122)

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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。

关键词: 泛癌, 生物标记,肿瘤, 基因表达调控, 基因组,人

Abstract:

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 total of 13 differentially expressed genes and 2 differentially expressed miRNAs had regulatory and interaction relationships. Conclusion: The 48 genes and 2 miRNAs that were differentially expressed in 13 cancers may serve as potential pan-cancer biomarkers, providing help for the diagnosis and prognosis evaluation of a variety of cancers, and providing clues for the development of broad-spectrum tumor therapeutic targets.

Key words: Pan-cancer, Biomarkers,tumor, Gene expression regulation, Genome,human

中图分类号: 

  • R730.43

表1

纳入研究的TCGA项目组信息"

Project Disease Gene miRNAs
Case Control Case Control
TCGA-BLCA Bladder urothelial carcinoma 408 19 409 19
TCGA-BRCA Breast invasive carcinoma 1 091 113 1 078 104
TCGA-HNSC Head and neck squamous cell carcinoma 500 44 523 44
TCGA-KICH Kidney chromophobe 65 24 66 25
TCGA-KIRC Kidney renal clear cell carcinoma 530 72 516 71
TCGA-KIRP Kidney renal papillary cell carcinoma 288 32 291 34
TCGA-LIHC Liver hepatocellular carcinoma 371 50 372 50
TCGA-LUAD Lung adenocarcinoma 513 59 513 46
TCGA-LUSC Lung squamous cell carcinoma 501 49 478 45
TCGA-PRAD Prostate adenocarcinoma 495 52 494 52
TCGA-STAD Stomach adenocarcinoma 375 32 436 41
TCGA-THCA Thyroid carcinoma 502 58 506 59
TCGA-UCEC Uterine corpus endometrial carcinoma 543 35 538 33

图1

差异表达基因和microRNAs的差异倍数"

图2

差异表达基因和microRNAs的诊断价值"

图3

差异表达基因和microRNAs与患者生存的关系"

图4

上调差异表达基因GO和KEGG通路富集分析(▲基因数目)"

图5

下调差异表达基因GO分析(▲基因数目)"

图6

差异表达基因与microRNAs调控网络"

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