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Viral Genomic Footprints in Breast Cancer: A Systematic Review and Meta-Analysis of Tissue-Based Detection of Epstein-Barr Virus and Bovine Leukemia Virus

Margioula-Siarkou G, Margioula-Siarkou C, Vavoulidis E, Flindris S, Petousis S, Haitoglou C, Mavromatidis G, Dinas K

International journal of molecular sciences, 2026

Abstract

Viral carcinogenesis as a causative mechanism of breast cancer has been intensively researched during the last decades. The role of Epstein-Barr virus (EBV) and Bovine Leukemia virus (BLV) in breast oncogenesis has been investigated in a plethora of studies, but with conflicting results. The aim of this systematic review and meta-analysis was to compare the frequency of molecular detection of the EBV genome and BLV genome between women with breast cancer and women without malignant breast tumors. This systematic review and meta-analysis adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. MEDLINE, SCOPUS, Cochrane CENTRAL and ClinicalTrials.gov were searched up to 20 May 2024. Included studies were those comparing the frequency of molecular detection of the EBV and/or BLV genome in breast tissue specimens with polymerase chain reaction (PCR) methods between patients with breast cancer and women without breast malignancies. The primary outcomes of the study were the frequency of molecular detection of the EBV genome and BLV genome. Methodological quality of included studies was assessed using the Newcastle-Ottawa Scale. A total of 29 studies met the selection criteria and were included in this meta-analysis; 19 studies reported results for molecular detection of the EBV genome, 9 studies for detection of the BLV genome and 1 study for detection of genomic material of both viruses. The frequency of molecular detection of viral…

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Evidence Hub reads this as a meta-analysis, done in animal, not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health.

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