Objectives

Plasmablastic lymphoma (PBL) is a subtype of diffuse large B-cell lymphoma (DLBCL) often associated with Epstein–Barr virus (EBV) infection. Despite recent advances in treatment, PBL still has a poor prognosis. EBV is listed as one of the human tumor viruses that may cause cancer, and is closely related to the occurrence of some nasopharyngeal carcinoma (NPC), lymphoma and 10% of gastric cancer (GC). It is very important to explore the differentially expressed genes (DEGs) between EBV-positive and EBV-negative PBL. Through bioinformatics analysis of DEGs between EBV-positive PBL and EBV-negative PBL, we gain a deeper understanding of the pathogenesis of EBV-positive PBL.

Methods

We selected the GSE102203 data set, and screened the DEGs between EBV-positive PBL and EBV-negative PBL. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were applied. The protein–protein interaction (PPI) network was constructed, and screened for the hub genes. Finally, Gene Set Enrichment Analysis (GSEA) was performed.

Results

In EBV-positive PBL, the immune-related pathway is upregulated and Cluster of differentiation 27 (CD27) and programmed cell death-ligand 1 (PD-L1) are hub genes.

Conclusions

In EBV-positive PBL, EBV may affect tumorigenesis through activation of immune-related pathways and upregulation of CD27, PD-L1. Immune checkpoint blockers of CD70/CD27 and programmed cell death 1 (PD-1)/PD-L1 pathways may be one of the effective strategies for the treatment of EBV-positive PBL.