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Sci Rep. 2019;9(1):3465.

Interpretation of EBV infection in pan-cancer genome considering viral life cycle: LiEB (Life cycle of Epstein-Barr virus).

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제1저자 송효진 · 교신저자 윤성수, 고영일

PMID 30837539 PMC6401378 DOI 10.1038/s41598-019-39706-0 IF 4.9 혈액종양내과
소속 (11)
  1. Cancer Research Institute, Seoul National University College of Medicine, Seoul, Republic of Korea.
  2. Center for Medical Innovation, Seoul National University Hospital, Seoul, Republic of Korea.
  3. Department of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea.
  4. Genome Opinion, Ansan, Gyeonggi-do, Republic of Korea.
  5. Department of Pathology, Seoul National University Hospital, Seoul, Republic of Korea.
  6. Center for RNA Research, Institute for Basic Science, Seoul, Republic of Korea.
  7. School of Biological Sciences, Seoul National University, Seoul, Republic of Korea.
  8. Cancer Research Institute, Seoul National University College of Medicine, Seoul, Republic of Korea. ssysmc@snu.ac.kr.
  9. Department of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea. ssysmc@snu.ac.kr.
  10. Center for Medical Innovation, Seoul National University Hospital, Seoul, Republic of Korea. go01@snu.ac.kr.
  11. Department of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea. go01@snu.ac.kr.

Abstract

We report a novel transcriptomic analysis workflow called LiEB (Life cycle of Epstein-Barr virus) to characterize distributions of oncogenic virus, Epstein-Barr virus (EBV) infection in human tumors. We analyzed 851 The Cancer Genome Atlas whole-transcriptome sequencing (WTS) data to investigate EBV infection by life cycle information using three-step LiEB workflow: 1) characterize virus infection generally; 2) align transcriptome sequences against a hybrid human-EBV genome, and 3) quantify EBV gene expression. Our results agreed with EBV infection status of public cell line data. Analysis in stomach adenocarcinoma identified EBV-positive cases involving PIK3CA mutations and/or CDKN2A silencing with biologically more determination, compared to previous reports. In this study, we found that a small number of colorectal adenocarcinoma cases involved with EBV lytic gene expression. Expression of EBV lytic genes was also observed in 3% of external colon cancer cohort upon WTS analysis. Gene set enrichment analysis showed elevated expression of genes related to E2F targeting and interferon-gamma responses in EBV-associated tumors. Finally, we suggest that interpretation of EBV life cycle is essential when analyzing its infection in tumors, and LiEB provides high capability of detecting EBV-positive tumors. Observation of EBV lytic gene expression in a subset of colon cancers warrants further research.

Figures

Figure 1
Figure 1 Workflow of the three-step EBV in silico detection algorithm. We examined 851 WTS samples covering 23 cancer types from TCGA database. Our workflow (LiEB) involves three steps to detect EBV-positive samples: (1) detection of viral infection; (2) alignment against a hybrid genome; (3) quantification of EBV gene expression.
Figure 2
Figure 2 (a) Proportion of EBV-positive samples among 23 cancer types examined. Each bar represents the percentage of samples in which one or more mapped read was detected for each of the four EBV strains (HHV4_wt, HHV4_GD1, HHV4_AG876, and EBV_artifactual_join) examined. Note that we did not include READ in the further analysis set due to an insufficient number of sample sets (N = 7), although this cancer type shows a high proportion of EBV infection in this figure. (b,c) Proportion of samples expressing EBV latent and lytic genes among 23 cancer types examined. Each stacked bar indicates the percentage of samples of each cancer type expressing EBV latent (b) and lytic (c) genes. Colors represent each TCGA cancer project.
Figure 3
Figure 3 (a) EBV lytic gene expression and its partial genomic structure. (Left) Thirteen EBV lytic genes were classified into three categories according to the sequential processes of the lytic stage: early transcription factors, initiation complex genes, and lytic antigens (c.f., the list of lytic genes refers to Draborg, A. H. et al. Clinical and Developmental Immunology [2012]). (Right) Heatmap illustrating the expression of EBV lytic genes in three types of gastrointestinal cancer types: colon adenocarcinoma (COAD, red), rectal adenocarcinoma (READ, blue), and stomach adenocarcinoma (STAD, green). The gradation of blue color indicates the row-scaled expression value of each EBV gene. Three annotation bars on STAD samples indicate those that were EBV positive as detected by our LiEB workflow and in published preliminary reports: TCGA Network group20 and Li Ding group18. (b) Comparison of EBV lytic gene expression in TCGA COAD, READ, and SNUH_COAD. Heatmap illustrating the expression of EBV lytic genes in three gastrointestinal cancer cohorts: SNUH colon cancer (COAD-SNUH, red), TCGA colon adenocarcinoma (COAD-TCGA, blue), and rectal adenocarcinoma (READ-TCGA, green). The gradation of blue color indicates the column-scaled expression value of each EBV gene.
Figure 4
Figure 4 Dot plot of enriched pathways determined from GSEA results. Each dot plot demonstrates enriched pathways in TCGA (5a) and LiEB (5b) comparison of GSEA results. The size of the dot represents gene count, and the color represents the adjusted p-value.

그림 출처: PubMed Central 공개 논문 (PMC6401378)

Conflict of interest statement

The authors declare no competing interests.

그 밖의 정보
출판 형태
Journal Article, Research Support, Non-U.S. Gov't
저널
Scientific reports · eISSN 2045-2322
전자 출판
2019-03-05
MeSH
Cell Transformation, Viral · Epstein-Barr Virus Infections / complications / diagnosis / virology · Gene Expression Profiling · Genome, Human* · Herpesvirus 4, Human / physiology · High-Throughput Nucleotide Sequencing · Humans · Life Cycle Stages · Molecular Diagnostic Techniques · Mutation · Neoplasms / diagnosis / etiology · Reproducibility of Results · Transcriptome
참고문헌
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