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ORIGINAL RESEARCH ARTICLE

MEF2C as a key driver in Epstein–Barr virus-positive diffuse large B-cell lymphoma

Prankur Awasthi1 ,  Nishant Kumar Singh1 ,  Pradyumn Singh2 ,  Balendu Shekher Giri3* ,  Saba Hasan1*
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1 Amity Institute of Biotechnology, Amity University Uttar Pradesh, Lucknow, Uttar Pradesh, India
2 Department of Pathology, Dr. Ram Manohar Lohia Institute of Medical Sciences, Lucknow, Uttar Pradesh, India
3 Sustainability Cluster, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India
EJMO, 026210233 https://doi.org/10.36922/EJMO026210233
Received: 21 May 2026 | Revised: 16 July 2026 | Accepted: 21 July 2026 | Published online: 30 July 2026
© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
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Abstract

Introduction: Epstein–Barr virus (EBV)+ diffuse large B-cell lymphoma (DLBCL) is an aggressive lymphoma with a poor prognosis and complex molecular alterations. The role of ferroptosis in EBV+ DLBCL remains poorly understood. 

Objectives: The objective of this study was to identify and validate key ferroptosis-related genes (FRGs) in EBV+ DLBCL by integrating bioinformatics and experimental approaches.

Methods: Gene expression datasets related to EBV and DLBCL were retrieved from the Gene Expression Omnibus (GEO) database, including GSE49628, GSE56315, and GSE103265. Differentially expressed genes (DEGs) were identified using GEO2R with the criteria |log2 fold change| ≥ 1 and adjusted p < 0.05. The DEG lists from the three datasets were intersected with the human FRGs retrieved from FerrDb using Venny 2.0. A random forest classifier was used to classify FRGs in each dataset through the randomForest and caret R packages. Selected genes were evaluated by quantitative real-time polymerase chain reaction (RT-qPCR) in formalin-fixed, paraffin-embedded DLBCL tissues from 13 patients, and further co-expression analysis of the genes was performed using GeneMANIA.

Results: Overlapping FRGs (n = 11) were identified through Venny 2.1.0. Using a random forest classifier, MEF2C was identified as the most important predictive biomarker, along with TLR4 and SLC40A1. Consistent with the expression-analysis results, RT-qPCR showed significantly higher MEF2C and GPX4 expression in EBV+ DLBCL samples, and significantly lower TLR4 and SLC40A1 expression. Co-expression analysis verified the involvement in pathways associated with iron ion homeostasis, inflammatory signaling, and B-cell proliferation.

Conclusion: This study suggests that molecular changes associated with ferroptosis are linked to EBV+ DLBCL and that MEF2C may be a therapeutic target or biomarker.

Graphical abstract
Keywords
Ferroptosis
Epstein–Barr virus-positive diffuse large B-cell lymphoma
MEF2C
GPX4
EBNA1
Biomarker
Funding
None.
Conflict of interest
The authors declare that they have no competing interests.
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