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Journal of Peking University (Health Sciences) ›› 2021, Vol. 53 ›› Issue (3): 602-607. doi: 10.19723/j.issn.1671-167X.2021.03.028

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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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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

CLC Number: 

  • R730.43

Table 1

Information of the TCGA projects included in the study"

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

Figure 1

Fold change of differentially expressed genes and microRNAs BLCA, bladder urothelial carcinoma; BRCA, breast invasive carcinoma; HNSC, head and neck squamous cell carcinoma; KICH, kidney chromophobe; KIRC, kidney renal clear cell carcinoma; KIRP, kidney renal papillary cell carcinoma; LIHC, liver hepatocellular carcinoma; LUAD, lung adenocarcinoma; LUSC, lung squamous cell carcinoma; PRAD, prostate adenocarcinoma; STAD, stomach adenocarcinoma; THCA, thyroid carcinoma; UCEC, uterine corpus endometrial carcinoma; FC, fold change; miR, microRNA."

Figure 2

Diagnostic significance of differentially expressed genes and microRNAs AUC, area under the curve; Other abbreviations as in Figure 1."

Figure 3

Relationship with patient survival of differentially expressed genes and microRNAs HR, hazard ratio; Other abbreviations as in Figure 1."

Figure 4

GO and KEGG pathway enrichment analysis of up-regulated differentially expressed genes (▲ number of genes) GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; BP, biological process; CC, cellular component; MF, molecular function."

Figure 5

GO analysis of down-regulated differentially expressed genes (▲ number of genes) Abbreviations as in Figure 4."

Figure 6

Regulatory network of differentially expressed genes and microRNAs"

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