Association of Hla Alleles and Mismatches With Post-Transplant Lymphoproliferative Disorder in Solid Organ Transplant Recipients: A Systematic Review and Meta-Analysis Publisher Pubmed



Assadiasl S ; Yazdani M ; Mojtahedi H ; Safdel S ; Mami S ; Sadr M ; Sadeghi A ; Soleimanifar N ; Nicknam MH
Source: Transplant Immunology Published:2026

Abstract

Background: Post-transplant lymphoproliferative disorder (PTLD) is a rare but significant complication after solid organ transplantation. Many factors, including viral infections and immunosuppressive drugs, have been shown to predispose transplant recipients to disease; however, the implication of human leukocyte antigens (HLA) has not yet been understood. In this systematic review, the association of HLA alleles and HLA mismatch number with the development of PTLD was evaluated in solid organ transplant recipients. Methods: A literature search was performed until October 2025 using PubMed/Medline, Scopus, Web of Science, and Cochrane library databases concerning the association of HLA alleles and mismatches with PTLD in kidney, liver, heart, lung, and pancreas transplant recipients. After literature screening, 28 full text articles were included in the systematic review. The statistical analyses were conducted using R software (version 4.3.2) with the meta package for effect size synthesis, heterogeneity assessment, and visualization. Results: The study did not show any particular HLA allele that could increase or decrease disease risk in all populations. However, meta-analysis of HLA-B and HLA-DR mismatches in PTLD risk revealed that while both mismatches showed a trend toward increased risk, HLA-DR mismatches demonstrated a more consistent and statistically significant association with HR: 1.65 (95% CI: 1.28 to 2.12) compared to HLA-B with HR:1.78 (95% CI 0.91 - 3.48). Conclusion: HLA disparity especially, HLA-DR and HLA-B mismatch between donor and recipient might increase the risk of post-transplant lymphoproliferative disease in solid organ transplant recipients. © 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.