Interpretation of EBV infection in pan-cancer genome considering viral life cycle: LiEB (Life cycle of Epstein-Barr virus).
Hyojin SongYoojoo LimHogune ImJeong Mo BaeGyeong Hoon KangJunhak AhnDaehyun BaekTae-You KimSung-Soo YoonYoungil KohPublished in: Scientific reports (2019)
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.
Keyphrases
- epstein barr virus
- life cycle
- diffuse large b cell lymphoma
- gene expression
- genome wide
- dna methylation
- data analysis
- squamous cell carcinoma
- endothelial cells
- single cell
- emergency department
- mental health
- poor prognosis
- copy number
- dendritic cells
- papillary thyroid
- transcription factor
- rna seq
- mass spectrometry
- machine learning
- artificial intelligence
- high resolution
- induced pluripotent stem cells
- liquid chromatography
- adverse drug