Abstract P74: In silico evaluation to categorize pathogenicity between Epstein-Barr Virus Infection, Plasmablastic Lymphoma, and Diffuse Large B Cell Lymphoma

Epstein-Barr virus (EBV) is closely linked to various lymphoproliferative disorders, including plasmablastic lymphoma (PBL) and diffuse large B cell lymphoma (DLBCL). However, the molecular mechanisms connecting EBV infection to these malignancies are not fully understood. This study investigates the pathogenic link between EBV, PBL, and DLBCL using advanced bioinformatics approaches. Datasets GSE49628 (EBV-infected B cells and Resting B cells), GSE102203 (EBV+ and EBV− PBL), and GSE103265 (EBV+ and EBV− DLBCL) were analyzed using GEO2R (R v 4.2.2), which integrate the limma (v 3.54.0) package and R/Bioconductor to identify differentially expressed genes (DEGs). From the analysis with |log2FC|≥ 1 and p-value <0.05, 7421, 5016, and 1359 DEGs were identified in the respective datasets, with 256 common DEGs found across all datasets. Protein-protein interaction (PPI) analysis revealed 254 nodes and 266 edges, indicating complex interactions among these DEGs. Further functional enrichment analysis using ShinyGO v0.80 uncovered that these DEGs are involved in critical biological processes, such as T cell activation, intracellular signal transduction, and regulation of the adaptive immune response. Pathway enrichment analysis identified significant associations with the HIF-1 signaling pathway, P53 signaling pathway, Th17 cell differentiation, T cell receptor signaling pathway, and PI3K-Akt signaling pathway, highlighting their role in EBV-driven oncogenesis. By using MCC (Maximal Clique Centrality), EPC (Edge Percolated Component), Degree, and closeness algorithms of cytoHubba plugin v0.1 in cytoscape v3.10.2, We identified five common key genes i.e., IL2RB, CD80, CD274, SELL, and IRF4 as common to the pathogenic link between EBV, PBL, and DLBCL. This novel integrative analysis may provide new insights into the molecular mechanisms of EBV-mediated transformation in DLBCL and PBL, identifying potential biomarkers and therapeutic targets. The study underscores the utility of bioinformatics in understanding complex pathogen-host interactions, offering pathways for personalized treatment strategies in EBV-associated lymphomas. Citation Format: Prankur Awasthi and Saba Hasan. In silico evaluation to categorize pathogenicity between Epstein-Barr Virus Infection, Plasmablastic Lymphoma, and Diffuse Large B Cell Lymphoma [abstract]. In: Proceedings of Frontiers in Cancer Science 2024; 2024 Nov 13-15; Singapore. Philadelphia (PA): AACR; Cancer Res 2025;85(15_Suppl):Abstract nr P74.