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Background:
Systematic Review

Worldwide Prevalence of Epstein–Barr Virus in Patients with Burkitt Lymphoma: A Systematic Review and Meta-Analysis

by
Mutaz Jamal Al-Khreisat
1,
Nor Hayati Ismail
1,
Abedelmalek Tabnjh
2,
Faezahtul Arbaeyah Hussain
3,*,
Abdul Aziz Mohamed Yusoff
4,
Muhammad Farid Johan
1,* and
Md Asiful Islam
5
1
Department of Haematology, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian 16150, Kelantan, Malaysia
2
Department of Applied Dental Sciences, Faculty of Applied Medical Sciences, Jordan University of Science and Technology, Irbid 22110, Jordan
3
Department of Pathology, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian 16150, Kelantan, Malaysia
4
Department of Neurosciences, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian 16150, Kelantan, Malaysia
5
WHO Collaborating Centre for Global Women’s Health, Institute of Metabolism and Systems Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham B15 2TT, UK
*
Authors to whom correspondence should be addressed.
Diagnostics 2023, 13(12), 2068; https://doi.org/10.3390/diagnostics13122068
Submission received: 28 February 2023 / Revised: 28 May 2023 / Accepted: 7 June 2023 / Published: 15 June 2023
(This article belongs to the Special Issue Advances in Lymphoma Molecular Diagnostics—2nd Edition)
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Review Reports Versions Notes

Abstract

:
Burkitt lymphoma (BL) is a form of B-cell malignancy that progresses aggressively and is most often seen in children. While Epstein–Barr virus (EBV) is a double-stranded DNA virus that has been linked to a variety of cancers, it can transform B lymphocytes into immortalized cells, as shown in BL. Therefore, the estimated prevalence of EBV in a population may assist in the prediction of whether this population has a high risk of increased BL cases. This systematic review and meta-analysis aimed to estimate the prevalence of Epstein–Barr virus in patients with Burkitt lymphoma. Using the appropriate keywords, four electronic databases were searched. The quality of the included studies was assessed using the Joanna Briggs Institute’s critical appraisal tool. The results were reported as percentages with a 95% confidence interval using a random-effects model (CI). PROSPERO was used to register the protocol (CRD42022372293), and 135 studies were included. The prevalence of Epstein–Barr virus in patients with Burkitt lymphoma was 57.5% (95% CI: 51.5 to 63.4, n = 4837). The sensitivity analyses demonstrated consistent results, and 65.2% of studies were of high quality. Egger’s test revealed that there was a significant publication bias. EBV was found in a significantly high proportion of BL patients (more than 50% of BL patients). This study recommends EBV testing as an alternative for predictions and the assessment of the clinical disease status of BL.

1. Introduction

Epstein–Barr virus (EBV) is a pathogenic double-stranded DNA human herpes virus 4 (HHV4). It was first discovered as a human-associated virus by Michael Anthony Epstein and Yvonne Barr in 1964 [1]. The virus consists of a 170–180 kb liner of double-stranded (ds) enveloped DNA with a toroid-shaped protein core, a nucleocapsid with 162 capsomers, and external virus-encoded glycoprotein spikes on the surface of the viral tegument [2]. The EBV genome encodes more than 85 genes, which are involved in the pathogenesis of infection and initiating EBV-associated human disease. There are two major types of EBV: type 1 EBV, which is found worldwide, and type 2 EBV, which is mainly detected in Africa [3]. EBV is the most frequent cause of infectious mononucleosis, with primary infections commonly occurring asymptomatically in teenagers and young adults, especially college students, while in adults, the symptoms are more severe. After primary infection, EBV establishes latent and lytic programs [4,5]. During the latent form of infection, the virus persists in the host cells, while during the lytic phase of infection, new infectious virions are produced [1]. Individuals infected with EBV control the virus’s infectious behavior through cytotoxic immune cell reactions mediated by natural killer (NK) cells and CD8+ T lymphocytes [6,7]. Only a few infected individuals develop chronic EBV-associated pathologies, often due to immune deficiencies, genetic predisposition, and environmental factors [8]. Chronic EBV infections are mainly in the epithelial and lymphocytic cells, which have been associated with malignant diseases [1,9]. EBV is very common in the general population; however, only a minority of infected people experience EBV-related pathologies, suggesting that additional risk factors, such as immune deficiencies, genetic predisposition, and environmental factors, are also crucial in the development of these pathologies [10,11,12]. EBV-associated malignancies express different EBV latent gene products, which are involved in the anti-apoptotic functions of B cells and interfere with innate and adaptive immunity, allowing infected cells to escape immune surveillance. Burkitt lymphoma (BL) is a highly aggressive B-cell non-Hodgkin’s lymphoma that is characterized by the translocation and dysregulation of the proto-oncogene MYC as well as hypermutated immunoglobulin gene sequences [13]. BL is derived from germinal center B cells [14]. Histologically, BL demonstrates sheets of monomorphic medium-sized B cells with basophilic cytoplasm, numerous mitoses, and frequent apoptotic bodies. Macrophages are scattered among tumor cells, giving BL a distinctive histologic appearance called the starry sky pattern. Tumor cells express membrane immunoglobulin (Ig) M, Ig light chain, B-cellular antigen, B-cell lymphoma (BCL) protein 6, and a cluster of differentiation (CD) 10, 19, 20, and 22, while showing negative findings for CD 5, 23, and BCL 2 [15,16,17]. The EBV status of tumors affects the expression of the Epstein–Barr virus (EBV)/C3d receptor and CD21. In essence, all cases of endemic BL are EBV-positive and express CD21, whereas the majority of non-endemic BL among patients who are non-immunosuppressed are EBV-negative and do not express CD21 [18].
The initial BL case was reported in the early 20th century. Denis Burkitt observed widespread childhood tumors in Uganda, which were characterized by malignant growths in the jaw and within the abdominal cavity [19,20]. The World Health Organization (WHO) classified three clinical variants of BL based on cancer epidemiology: endemic, sporadic, and immunodeficiency-associated. These variants are histologically identical and have similar clinical behavior [21]. Endemic BL (eBL) presents in the jaw in younger children and abdominally in older children in malaria-endemic regions, predominantly in sub-Saharan Africa and Papua New Guinea. eBL has a 2:1 male-to-female ratio and a median age of 6 years [22,23].
eBL is mainly localized to geographical areas where Plasmodium falciparum malaria is holoendemic. Chronic B-cell activation or promotion of EBV’s oncogenic potential in the presence of malarial co-infection has been postulated to increase oncogenesis [24,25,26]. Sporadic BL (sBL) is distributed worldwide, with the majority of cases occurring in the United States and Western Europe. sBL is more frequent in children, accounting for 20% to 30% of lymphomas in this age group. Adults with sporadic BL are uncommon, accounting for less than 1% of NHL cases in the United States [27]. BL presents within the abdominal region, lymph nodes, and can also be extranodal. The third variant is HIV-associated BL (ID-BL), which is diagnosed at the early stage of HIV infection and prior to CD4+ T-cell decreases [28].
EBV varies in detection among the three clinical variants of BL. Most endemic BLs are associated with EBV, which suggests that the virus has a direct role in lymphoma pathogenesis. About 95% of eBL detect EBV [28], whereas only about 10–30% of EBV is detected in sBL [21], and 20–40% of EBV positives are detected in ID-BL [28].
EBV plays a critical role in the onset of multiple sclerosis, according to growing data from several study fields. It has been proposed that multiple sclerosis (MS) depends on the early immune response to EBV infection because the severity of the EBV primary infection is strongly associated with the onset of MS many years later. The inability to control this infection might result in the colonization of resident memory B-cell and T-cell follicles in CNS-accessible regions, such as tertiary lymphoid structures, which are particularly prone to triggering immunological disease in the CNS. The period of infection is probably a factor in the immune system’s elimination of the viruses, autoreactive T cells, and antibodies that are directed against CNS components [29,30].
EBV-related malignancies are linked to a latent form of infection, in which EBV expresses a limited set of proteins called EBV transcription programs (ETPs) in every tumor cell, including six nuclear antigens (EBNAs), three latent membrane proteins (LMPs), and untranslated RNA called EBV-encoded small RNA (EBERs), which can mediate cellular transformation [31]. EBV infects primary B cells and induces them to proliferate, by expressing viral genes that were identified as EBNA1, EBNA2, ENBA3A, EBNA3C, and LMP1, which are involved in the latency phase of EBV infection [1]. Additional genes that are included in the transforming B cells are LMP2, viral miRNAs, the small non-coding RNA EBER, BZLF1, and BRLF1 [32].
The three latency programs that EBV can display are either Latency I, Latency II, or Latency III. A specific, limited set of viral proteins and RNAs are produced by each latency program (Table 1) [33,34].
Studies showed that BL expresses high levels of MYC, and more than 90% show the translocation of the MYC oncogene (8q24) onto the immunoglobulin heavy chain (IgH) (14q34). The chromosomal breakpoints of both MYC and IgH vary between sBL and eBL, giving rise to different aetiologic drivers [35]. A translocation of the MYC gene on chromosome 8, including genetic material from chromosomes 2, 14, or 22, is the classic etiology of BL. The majority of translocations (around 80%) involve the Ig heavy chain on chromosome 14, t(8;14), whereas 15% involve the kappa light chain on chromosome 2, t(2;8), and 5% involve the lambda light chain on chromosome 22 [36,37].
EBV-associated malignancies are diagnosed primarily by a biopsy of the primary tumor, with an EBER in situ hybridization test to confirm the presence of EBV [38]. However, due to the difficulty in obtaining a sample of the tumor or poor patient condition, performikng a biopsy might be challenging [39].
Many studies of EBV-associated lymphoma reveal that EBV-DNA may be found in the plasma of most patients with EBV-related malignancies [40]. DNA from EBV-associated lymphoma is derived as naked DNA fragments from apoptotic or necrotic tumor cells [35,38], whereas it is undetectable in non-EBV-associated tumors or healthy people [24]. Although plasma EBV DNA has recently become more important in the diagnosis and management of EBV-associated cancers [41], particularly Hodgkin’s lymphoma (HL) [41,42] and nasopharyngeal carcinoma [43,44], there are limited data on the diagnostic and prognostic significance of plasma EBV DNA for BL. In order to identify EBV in various types of samples, methods such as the heterophile antibody test, immunofluorescence assays, enzyme immunoassays, Western blot, and polymerase chain reaction (PCR) are used. The use of PCR to determine the EBV viral load is becoming more popular in the diagnosis of EBV-related diseases [45].
Artificial intelligence (AI) is now advancing quickly, and its application in medicine is becoming more relevant. To predict or classify based on input data, AI integrates computer science and databases. Machine learning and deep learning are two types of AI used in the medical field to evaluate medical data and acquire an understanding of the pathogenesis of diseases. Recently, an AI application used for EBV has been developed, such as a deep-learning-based EBV prediction method from H&E-stained whole-slide images (WSI) in gastric cancer [46], and deep-learning-based classifiers to detect microsatellite instability and EBV status directly from hematoxylin-and-eosin-stained histological slides [47]. In BL, artificial neural networks and various types of machine learning were used to analyze the gene expression and protein levels by immunohistochemistry of several hematological neoplasia and pan-cancer series in order to predict patients’ survival and the disease subtype classification with a high accuracy [48]. There is no systematic review and meta-analysis of the prevalence of EBV in patients with BL that we are aware of. As a result, the goal of this systematic review and meta-analysis was to determine the prevalence of EBV in patients with BL, which helps in predicting whether populations are at high risk of increasing the number of BL cases corresponding to EBV infection.

2. Materials and Methods

2.1. Reporting Guidelines and Protocol Registration

This systematic review and meta-analysis were carried out according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) [49] and Meta-analysis of Observational Studies in Epidemiology (MOOSE) [50] guidelines. This study protocol (PROSPERO: CRD42022372293) was submitted to the International Prospective Registry of Systematic Reviews database at the University of York, York, UK.

2.2. Eligibility Criteria

The study looked for published studies on the prevalence of Epstein–Barr virus among Burkitt lymphoma patients. The screening was carried out to find possible studies that looked at the presence of EBV in Burkitt lymphoma patients without any restrictions.

2.3. Literature Search

In total, 3981 studies were retrieved from four electronic databases: PubMed, Scopus, Web of Science, and Google Scholar. The most recent search was in January 2021, for studies on the prevalence of Epstein–Barr viruses among Burkitt lymphoma patients. Burkitt, Burkitt’s, African Lymphoma, Epstein–Barr, EBV, Human Herpesvirus 4, HHV4, HHV-4, and EB virus were used in the search utilizing a combination of Boolean logical operators (‘AND’ & ‘OR’) and the ‘Advanced’ and ‘Expert’ search options (Table S1). To ensure a thorough method, the references of the included papers were also examined. To organize and filter out duplicate studies, EndNote X9 software was used.

2.4. Study Selection

Two authors (M.J.A.-K. and N.H.I.) independently screened the research title and abstract, followed by the entire text, of all studies retrieved from the literature search to determine the matched studies to be included. Excluded studies include review articles, case studies, non-human studies, views, and viewpoints. Data from news accounts and press releases and information acquired from blogs and databases were not considered. With M.F.J., F.A.H, A.A.M.Y., A.T., and M.A.I, disagreements regarding inclusion were discussed and a consensus was reached.

2.5. Data Extraction

The data from the included studies were accessed independently by two authors (M.J.A.-K. and N.H.I.). Before the data extraction procedure, all non-English language studies were translated into English using Google Translate. The data extracted from each of the eligible studies was imported into a predetermined Excel spreadsheet. The following are the extracted data from the selected studies: author name, study type, country, number of BL patients, participants’ age, number of EBV positives in BL, sample type, and EBV detection method. Any discrepancies, or confusing or unfounded data were discussed among the authors in order to reach an agreement. If the problem remains, the corresponding or first author of each study was emailed for clarification.

2.6. Quality Assessment and Publication Bias

The quality of the included studies was assessed using Joanna Briggs Institute’s critical appraisal tools. The studies were defined as poor-quality (high risk of bias), moderate-quality (moderate risk of bias), or high-quality (low risk of bias) if the overall score was ≤49%, 50–69%, or ≥70%, respectively [51,52]. Egger’s test was used to verify the funnel plot’s asymmetry. To evaluate publication bias, a funnel plot was constructed to compare the prevalence estimate against the standard error.

2.7. Data Analyses

To address the inconsistency among the included studies, a tau-squared test was used to assess heterogeneity (I2), where p < 0.05 was regarded as statistically significant. A greater homogeneity was regarded as an I2 value close to zero, where I2 values between 25–50% indicated low heterogeneity, 51–75% indicated moderate heterogeneity, and >75% indicated significant heterogeneity. Based on the critical assessment tools, two authors M.J.A.-K. and N.H.I.) evaluated the quality of each of the included studies by using the critical assessment tools.
Sensitivity analyses and Galbraith plots were also used to assess the quality of the results and identify potential causes of heterogeneity, respectively. The following strategies were used to conduct sensitivity analyses: excluding small studies (n < 100); excluding low-quality studies (high risk of bias); excluding studies that did not disclose the prevalence of EBV in patients with BL; only considering cross-sectional studies; and excluding outlier studies. All analyses and plots were generated by using RevMan software (version 5.3.5), RStudio (version 1.1.463), and the metafor package (version 2.0-0) of R software (version 3.5.1) [53].

2.8. Subgroup and Sensitivity Analyses

For subgroup analysis, the prevalence of EBV in patients with BL was analyzed through four-time interval trends (1969–1982, 1983–1995, 1996–2008, and 2009–2021); methods of EBV detection (nucleic acid hybridization, polymerase chain reaction (PCR), immunofluorescence, in situ hybridization (ISH), ISH+PCR, and southern blot); and geographical locations (Sub-Saharan Africa, Northern Africa, Southern America, Southern Asia, Northern America, Europe, Eastern Asia, and South-eastern Asia). The studies were categorized based on the sociodemographic index (SDI). To measure social and economic development, the SDI, which ranges from zero to one, employs data on the world’s economies, educational systems, and fertility rates. The SDI is divided into five categories: high SDI (lower bound to upper bound: 0.805129 to 1), high–middle SDI (lower bound to upper bound: 0.689504 to 0.805129), middle SDI (lower bound to upper bound: 0.607679 to 0.689504), low–middle SDI (lower bound to upper bound: 0.454743 to 0.607679), and low SDI (lower bound to upper bound: 0 to 0.454743) [54]. To identify the source of heterogeneity and check the robustness of the results, sensitivity analyses were performed using the following strategies: (1) excluding small studies (<100); (2) excluding low-quality studies (high risk of bias); (3) considering only cross-sectional studies; (4) considering only case-control studies; (5) considering only cohort studies; (6) considering only studies where the age was less than 18 years old; and (7) excluding the outlier studies.

3. Results

3.1. Selection and Inclusion of Studies

From the database search, 3981 studies qualified for initial screening, and then 2130 studies were excluded due to being duplicate studies (n = 1778), review articles (n = 259), case reports (n = 86), and non-human studies (n = 7). Therefore, 1851 studies were further assessed for eligibility by a detailed screening of the titles, abstracts, and full text. Finally, after excluding 1716 studies because they did not comply with the objective of this study, 135 studies were eligible to be included in this systematic review and meta-analysis, as illustrated in the PRISMA flow diagram (Figure 1).

3.2. Study Characteristics

Our literature search yielded 135 studies [37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171] published between 1969 and 2021, which examined the prevalence of EBV in patients with BL. Detailed characteristics and references of the included studies are presented in Table 2. Overall, this meta-analysis reports data from 4837 patients with BL lymphoma (34.7% female). The ages of these patients ranged from 2.1 ± 2.5 to 47.7 ± 31.8 years (mean ± SD; range, 0.7–98.0). The studies came from eight different regions, and these region groupings were based on the geographic regions defined under the Standard Country or Area Codes for Statistical Use (known as M49) of the United Nations Statistics Division [55]: region unspecified (n = 414), Sub-Saharan Africa (n = 2104) [56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90], Northern Africa (n = 507) [91,92,93,94,95,96,97,98,99,100,101,102,103,104], Southern America (n = 801) [105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122], Southern Asia (n = 37) [123,124,125], Northern America (n = 201) [126,127,128,129,130,131,132,133,134,135,136,137], Europe (n = 296) [138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155], Eastern Asia (n = 437) [156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171], and South-eastern Asia (n = 40) [172,173,174,175]. Multiple techniques were used to investigate the presence of EBV in patients with BL, including the use of single and combined methods of nucleic acid hybridization [61,63,73,79,80,81,133,134,160], polymerase chain reaction (PCR) [57,69,85,87,92,98,101,102,109,115,125,126,127,131,139,150,176], immunofluorescence [62,65,66,67,70,71,75,76,86,95,96,130,135,153,177,178,179,180], immunoassay [58,64,74,77,138,148,170], in situ hybridization (ISH) [60,68,72,78,82,83,88,89,90,91,93,97,99,104,105,106,108,112,116,117,118,119,120,121,124,128,129,132,140,141,142,143,144,146,147,149,151,152,154,155,156,157,158,159,161,162,163,164,166,168,169,171,173,174,181,182,183,184], Southern blot [111,136,145,165,167,185,186], and ISH+PCR [103,107,113,114,123,137,172,187,188]. The included studies were conducted between 1969 and 2021, and these studies were divided into four time groups with a fixed interval of 13 years for each: the groups of studies were from 1969 to 1982 [61,62,65,66,67,70,71,73,76,79,80,81,84,86,96,130,133,134,153,177,178], from 1983 to 1995 [57,63,78,83,93,94,95,100,101,109,111,112,122,127,128,129,136,139,141,143,144,145,146,148,151,154,155,156,165,167,170,176,179,180,183,185,186], from 1996 to 2008 [58,60,72,75,85,87,90,91,99,102,103,105,106,107,108,110,113,114,115,120,121,124,126,131,135,137,140,147,150,152,157,159,160,161,163,164,166,172,173,174,182,187,188], and from 2009 to 2021 [56,59,64,68,69,74,77,82,88,89,92,97,98,104,116,117,118,119,123,125,132,138,142,149,158,162,168,169,171,175,181,184,189,190].
Studies were categorized based on the socio-demographic index (SDI) into five categories: high SDI [126,127,128,129,130,131,132,133,134,135,136,137,139,142,143,144,145,146,147,148,151,153,154,155,157,158,159,161,163,165,166,167,170,171,176], high–middle SDI [91,94,95,96,99,100,102,103,107,109,112,116,138,140,141,149,150,152,173,174,175], middle SDI [92,93,97,104,105,106,108,110,113,114,115,117,118,119,120,121,122,123,156,160,162,164,168,169,172], low–middle SDI [59,62,68,74,82,83,87,125], and low SDI [56,57,58,60,61,64,66,67,69,70,71,72,75,76,77,78,79,80,81,84,85,86,88,89,90,98,101,124].

3.3. Outcomes

The pooled prevalence of EBV in patients with BL was 59.4% (95% CI, 54.1–64.6%, n = 4837), as illustrated in Figure 2.

3.4. Subgroup Analyses

Based on the subgroup analyses of the prevalence of EBV in patients with BL over four time intervals, we found a gradually decreasing prevalence of EBV in patients with BL, which was 64.2% (95% CI: 52.0 to 75.6; p < 0.01) from 1969 to 1982, then 60.9% (95% CI: 50.3 to 71.1; p < 0.01) from 1983 to 1995, then 60.7% from 1996 to 2008, and finally 54.0% (95% CI: 42.2 to 65.5; p < 0.01) that had a lower prevalence than the pooled prevalence within the period from 2009 to 2021 (Table 3 and Figure S1A–D). Furthermore, subgroup analyses based on the methods of EBV detection revealed a significantly increased prevalence when compared to the pooled prevalence in the nucleic acid hybridization at 81.7% (95% CI: 67.8 to 92.5; p < 0.01), 74.7% (95% CI: 60.0 to 87.1; p < 0.01) in the PCR method, and 60.0% (95% CI: 45.8 to 73.5; p < 0.01) in the immunofluorescence method. On the other hand, the prevalence in immunoassay, in situ hybridization (ISH), combined ISH with PCR, and Southern blot revealed a significantly lower prevalence: 54.7% (95% CI: 34.2 to 74.5; p < 0.01), 54.3% (95% CI: 46.3 to 62.1; p < 0.01), 53.2% (95% CI: 52.9 to 63.3; p = 0.01), and 47.1% (95% CI: 31.7 to 62.8; p < 0.01), respectively (Table 3 and Figure S1E–K). The subgroup analysis based on different geographical locations revealed a significantly increased prevalence when compared to the pooled prevalence only in Sub-Saharan Africa and Northern Africa, at 76.5% (95% CI: 67.0 to 84.9; p < 0.01) and 69.3% (95% CI: 58.1 to 79.4; p < 0.01), respectively (Figure 3). Southern America, Southern Asia, Northern America, Europe, Eastern Asia, and South-eastern Asia showed a decrease in prevalence when compared to the pooled prevalence at 58.4% (95% CI: 50.0 to 66.6; p < 0.01), 54.7% (95% CI: 30.5 to 77.9; p = 0.12), 54.3% (95% CI: 34.5 to 73.5; p < 0.01), 49.5% (95% CI: 36.9 to 62.5; p < 0.01), 29.5% (95% CI: 19.9 to 40.1; p < 0.01), and 29.1% (95% CI: 11.0 to 51.2; p = 0.15), respectively (Table 3 and Figure S1L–S). The subgroup analysis based on the socio-demographic index (SDI) revealed a significantly increased prevalence when compared to the pooled prevalence in both the middle and low SDI, at 60.1% (95% CI: 52.4 to 67.5; p < 0.01) and 82.7% (95% CI: 74.4 to 89.8; p = 0), respectively. On the other hand, countries with high SDI, high–middle SDI, and low–middle SDI showed a significant decrease in prevalence, at 43.0% (95% CI: 33.3 to 52.9; p = 0), 54.5% (95% CI: 40.0 to 68.6; p = 0), and 49.9% (95% CI: 31.4 to 68.5; p = 0), respectively (Table 3 and Figure S1T–X).

3.5. Quality Assessment

In Tables S2–S4, the quality assessment of the included studies was presented in detail. Generally, of the included studies, 65.2%, 29.6%, and 5.2% were high-, moderate-, and low-quality studies, respectively. The funnel plot and Egger’s test results revealed evidence of a publication bias for the prevalence of EBV in BL (p = 0.0034) (Figure 4).

3.6. Heterogeneity and Sensitivity Analysis

In sensitivity analyses, the highest EBV prevalence in patients with BL was observed when considering only case-control studies (67.6%; 95% CI: 58.0 to 76.5) [56,58,59,61,62,65,66,67,70,71,74,75,76,78,85,86,90,92,93,94,95,96,97,98,100,103,105,108,114,115,117,122,130,152,153,171,178,179,182], followed by considering only studies where the age was less than 18 years old (64.9%; 95% CI: 55.4 to 74.0) [56,58,59,61,62,63,69,70,72,73,74,75,77,78,81,89,90,94,95,98,99,102,103,104,105,107,108,109,110,113,115,116,117,120,122,126,135,137,138,163,165,166,169,174], excluding small studies with less than 100 subjects (64.0%; 95% CI: 40.3 to 84.9) [56,58,59,67,75,104,121,171], and excluding outlier studies (61.0%; 95% CI: 55.8 to 66.1) [56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,164,166,167,168,169,170,171,172,173,174,175,176,178,179,180,181,182,183,184,185,186,187,188,189,190]. In contrast, the lowest EBV prevalence in patients with BL was found when considering only cohort studies (48.4%; 95% CI: 35.9 to 61.1) [64,89,99,110,120,125,126,138,142,149,154,161,167,169,176,177,181], followed by considering only cross-sectional studies (54.4%; 95% CI: 50.1 to 64.6) [57,60,63,68,69,72,73,77,79,80,81,82,83,84,87,88,91,101,102,104,106,107,109,111,112,113,116,118,119,121,123,124,127,128,129,131,132,133,134,135,136,137,139,140,141,143,144,145,146,147,148,150,151,155,156,157,158,159,160,162,163,164,165,166,168,170,172,173,174,180,183,184,185,186,187,188,189,190], and excluding low- and moderate-quality studies (58.7%; 95% CI: 51.8 to 65.3) [56,57,58,59,61,62,63,68,69,72,73,75,77,80,81,82,83,84,88,89,91,92,94,95,96,97,98,102,103,104,106,107,109,112,113,116,117,118,119,120,121,122,123,124,128,129,131,132,134,135,136,138,139,140,141,146,147,148,150,151,154,155,156,157,158,159,160,162,163,164,165,166,168,170,172,173,174,175,178,180,183,184,185,186,187,188,189,190] (Table 4 and Figure S2A–G).
As depicted in the Galbraith plot (Figure 5), three outlier studies in estimating the prevalence of EBV in patients with BL were determined. The results showed significant heterogeneity at 97%, p < 0.001.

4. Discussion

EBV was found to be associated with human cancer when it was discovered in BL. This was a result of BL cell isolation. EBV has been extensively characterized due to purported links to a variety of human diseases, including BL, HL, post-transplant and AIDS-related lymphomas, and nasopharyngeal carcinoma [7,191,192]. Our results revealed a high prevalence of EBV in patients with BL, at 59.4% in all BL patients worldwide. However, as shown in our study, the prevalence of EBV in patients with BL varies by region; we found the highest prevalence in Sub-Saharan Africa (76.5%) and Northern Africa (69.3%), while the prevalence in Southern America (58.4%), Southern Asia (54.7%), Northern America (54.3%), Europe (49.7%), Eastern Asia (29.5%), and South-eastern Asia (29.1%) were lower than the pooled prevalence. We can explain the variations in EBV prevalence among patients with BL worldwide, as more than 95% of people in the world acquire the Epstein–Barr virus, a herpes virus belonging to the gamma subfamily, within the first ten years of life. Primary exposure to infections occurs in childhood in Africa and other developing countries, probably as a result of different cultural norms compared to developed countries [115,193].
The Epstein–Barr virus infection persists asymptomatically for the entirety of the host’s life, maintaining the immune system and this deceptive virus constantly in balance. In our study, the incidence of BL was higher in children (≤18) at 64.9% compared to adults; this corresponds to many studies that report that BL is more common in children [194,195]. Our results revealed that the incidence of BL among males is much higher than in females (34.7%), which is commensurate with several studies that report that BL is more prevalent in males compared to females [104,120,123,195]. This result is in agreement with Yakimchuk et al., which reported that estrogen has an anti-proliferative effect on BL cells through estrogen receptor β (ERβ) signalling [196]. Our study revealed a significant publication bias for EBV prevalence in patients with BL, and that is in agreement with some studies exploring the prevalence of EBV in different diseases, such as multiple sclerosis (p < 0.05) [197] and breast cancer (p = 0.006) [198], while that is in disagreement with some studies such as for gastric carcinoma (p = 0.912) [199], Hodgkin’s lymphoma (p = 0.162) [200], and EBV-associated epithelial tumors (p = 0.23617) [201].
Interestingly, our study shows a significant decline in EBV prevalence over four time periods (13 years), with the prevalence decreasing from 64.2% in the period from 1969 to 1982, to 54% in the period from 2009 to 2021. This decrease in incidence could be attributed to the development and widespread use of EBV vaccines, as well as improved sanitation, living habits, and personal hygiene [202,203]. There are many methods used to detect EBV, but these methods are different depending on whether they are faster, are more sensitive, or provide more informative than previous assays [204]. Our study revealed that the most used method in EBV detection was the microscopic examination (in situ hybridization (ISH) in 59 studies and immunofluorescence in 18 studies) method followed by molecular methods (PCR in 17 studies, nucleic acid hybridization in nine studies, ISH+PCR in nine studies, and Southern blot in seven studies), and, finally, immunoassay methods in seven studies. This result confirms that ISH is the methodology of choice for the detection of EBV in tissue sections [205,206,207]. Our results revealed a higher prevalence of EBV in patients with BL in both low and middle SDI countries, at 82.7% and 60.1%, respectively. A study showed that the highest incidence and mortality burden occurred in EBV-attributed BL in low and low–middle SDI areas [208]. The reasons for the increases in the burden of malignancies related to EBV infection appear to be growing populations, an increase in life expectancy, and changing age structure [209].

5. Conclusions

In conclusion, based on the comprehensive systematic and meta-analysis of the available data on the prevalence of EBV in patients with BL until January 2021, the prevalence was 59.4% in all patients with BL. Due to factors such as cultural habits, personality hygiene, limited use of developed EBV vaccines, and malaria endemic areas, Sub-Saharan Africa (76.5%) and Northern Africa (69.3%) revealed the highest prevalence (hot spots) in comparison to the rest of the world. Countries with middle and low SDI have a higher prevalence of EBV in patients with BL. Despite the fact that the EBV prevalence in patients with BL has decreased significantly from 64.2% in 1969 to 1982 to 54% from 2009 to 2021, as well as there being a higher incidence in younger (≤18) patients than adults, EBV detection should be used as a routine test in hot spots as well as in all young people because it will help in predicting whether populations are at a high risk of increasing the number of BL cases corresponding to EBV infection.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/diagnostics13122068/s1, Figure S1: Subgroup analyses estimating the prevalence of EBV at (A–D) different times, using (E–K) different EBV detection methods, in (L–S) different regions, and based on the (T–X) socio-demographic index (SDI); Figure S2: Sensitivity analyses (A) excluding small studies, (B) excluding low- and moderate-quality studies, (C) considering only cross-sectional studies, (D) considering only case-control studies, (E) considering only cohort, (F) considering only studies where the age was less than 18 years old, and (G) excluding outlier studies estimating the prevalence of EBV in patients with BL; Table S1: Search strategies; Table S2: Quality assessment of the included cross-sectional studies; Table S3: Quality assessment of the included case-control studies; Table S4: Quality assessment of the included cohort studies.

Author Contributions

Conceptualisation, M.A.I., F.A.H. and M.F.J.; methodology, M.A.I., N.H.I. and M.J.A.-K.; validation, M.A.I. and M.F.J.; formal analysis, M.A.I., M.J.A.-K. and N.H.I.; investigation, M.A.I., M.J.A.-K. and N.H.I.; resources, F.A.H. and M.F.J.; data curation, M.A.I., N.H.I. and M.J.A.-K.; writing—original draft preparation, M.J.A.-K. and N.H.I.; writing—review and editing, M.A.I., F.A.H., A.T. and M.F.J.; visualization, M.A.I. and M.F.J.; supervision, F.A.H., A.A.M.Y. and M.F.J.; funding acquisition, F.A.H. and M.F.J. All authors have read and agreed to the published version of the manuscript.

Funding

This work was funded by Universiti Sains Malaysia, RU Top Down grant 1001/PPSP/8070016 to F.A.H.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data are contained within the article or Supplementary Material.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. PRISMA flow diagram of study selection.
Figure 1. PRISMA flow diagram of study selection.
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Figure 2. Forest plots presenting the prevalence of Epstein–Barr virus in patients with Burkitt lymphoma [55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189].
Figure 2. Forest plots presenting the prevalence of Epstein–Barr virus in patients with Burkitt lymphoma [55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189].
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Figure 3. Global prevalence of Epstein–Barr virus in patients with Burkitt lymphoma.
Figure 3. Global prevalence of Epstein–Barr virus in patients with Burkitt lymphoma.
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Figure 4. Funnel plots estimating the prevalence of EBV in patients with BL revealed significant publication bias [55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189].
Figure 4. Funnel plots estimating the prevalence of EBV in patients with BL revealed significant publication bias [55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189].
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Diagnostics 13 02068 g005 550
Figure 5. Galbraith plots show two outlier studies in estimating the prevalence of EBV in patients with BL [55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189].
Figure 5. Galbraith plots show two outlier studies in estimating the prevalence of EBV in patients with BL [55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189].
Diagnostics 13 02068 g005
Table
Table 1. Epidemiology features of EBV-associated neoplasia.
Table 1. Epidemiology features of EBV-associated neoplasia.
Gene ExpressedProductEBV Latency Programs
Latency OLatency ILatency IIaLatency IIbLatency III
EBNA1Protein–++++
EBNA2Protein–++++
EBNA3Protein––+++
EBNA-LPProtein––+++
LMP1Protein––+++
LMP2Protein––+++
BARTsProtein––+++
EBERsncRNAs+++++
Associated MalignanciesMemory B cells
-
BL
-
EBVaGC
-
NPC
-
T/NK LPD
-
EBV + DLBCL, NOS
-
cHL
-
NLPHL
-
AIDS-associated B-cell lymphoma
-
DLBCL
-
PTLD
EBNA: EB viral nuclear antigen; EBNA-LP: EB viral nuclear antigen leader protein; LMP: latent membrane protein; BARTs: BamHI A rightward transcripts; EBERs: Epstein–Barr virus-encoded small RNAs; ncRNAs: non-coding RNAs; BL: Burkitt Lymphoma; EBVaGC: Epstein–Barr virus-associated gastric cancer; NPC: nasopharyngeal cancer; LPD: lymphoproliferative disorder; DLBCL, NOS: diffuse large B-cell lymphoma, not otherwise specified; cHL: classic Hodgkin’s Lymphoma; NLPHL: nodular lymphocyte-predominant Hodgkin’s lymphoma;PTLD: post-transplant lymphoproliferative disorder. “+” indicates the protein is expressed, while “−” indicates that the protein is not expressed.
Table
Table 2. Major characteristics of the included studies.
Table 2. Major characteristics of the included studies.
NoStudy IDType of StudyCountryAge (Mean ± SD/Range) (Years)Type of ParticipantsNumber of BL
Patients (% Female)		EBV Positivity in BL
# (%)		Sample TypeMethod of Detection of EBV
Age Group# (%)
1Abdelrahim, 2018
[175]		Cross-sectionalMalaysia22.2 ± 14.5 (32)≤183 (60)5 (60)0 (0)FFPEISH
45 < Age > 182 (40)
2Aguilar, 2017
[56]		Case-controlMalawi7.8 ± 2.9 (6.23)≤18271 (100)271 (41)260 (95.9)SeraqSAT
3Aitken, 1994
[57]		Cross-sectionalNew GuineaNRNRNR56 (NR)56 (100)FFPEPCR
4Akyol, 1997
[91]		Cross-sectionalTurkey17 ± 19.1 (59)≤187 (63.6)11 (18.2)6 (54.5)FFPEISH
45 < Age > 181 (9.1)
≥451 (9.1)
NR2 (18.5)
5Al-Fahdawi, 2016
[92]		Case-controlIraq21.8 ± 11.7 (34)≤1819 (31.7)60 (31.7)26 (43.3)FFPEPCR
45 < Age > 1814 (68.3)
6Ambrosio, 2014
[181]		CohortKenya, Spain, and ItalyNRNRNR71 (NR)41 (57.7)FFPEISH
7Anwar, 1995
[93]		Case-controlEgypt9.1 ± 4.9 (20)≤1838 (92.7)41 (41.5)30 (73.2)FFPE and fresh tumor biopsiesISH
45 < Age > 183 (7.3)
8Araujo, 1996
[105]		Case-controlBrazil5.9 ± 3 (13)≤1854 (100)54 (34.6)47 (87)FFPEISH
9Ayala, 2019
[184]		Cross-sectionalKenya and Italy16.6 ± 13.2 (42)≤1815 (68.2)22 (50)8 (36.4)FFPEISH
45 < Age > 186 (27.3)
≥451 (4.5)
10Bacchi, 1996
[106]		Cross-sectionalBrazil36 ± 8.5 (21)45 < Age > 185 (100)5 (20)2 (40)FFPEISH
11Banatvala, 1972
[177]		CohortEast AfricaNRNRNR9 (NR)0 (0)SeraImmunofluorescence
12Barriga, 1988
[185]		Cross-sectionalGhana and USANRNRNR56 (NR)34 (60.7)Fresh tumor biopsiesSouthern blotting
13Bellan, 2005
[182]		Case-controlKenya, France, and ItalyRange, 66NRNR31 (NR)18 (58.1)FFPEISH
14Bingler, 2008
[126]		CohortUSA2.1 ± 2.5 (5.1)≤184 (100)4 (50)3 (75)FFPEPCR
15Boyle, 1991
[176]		CohortAustraliaNRNRNR7 (NR)5/6 (83.3)FFPE and fresh tumor biopsiesPCR
16Căinap, 2012
[138]		CohortRomaniaNR≤1817 (100)17 (NR)8 (47.1)SeraSerological IgG VCA antibody
17Camilleri-Broët, 1995
[139]		Cross-sectionalFranceNRNRNR19 (NR)16 (84.2)FFPEPCR
18Carbone, 1993
[141]		Cross-sectionalItalyNRNRNR5 (NR)3 (60)FFPEISH
19Carbone, 1996
[140]		Cross-sectionalItalyNRNRNR66 (NR)16 (24.2)Fresh tumor
biopsies		ISH
20Carpenter, 2008
[58]		Case-controlUganda7 ± 3 (13.5)≤18325 (100)325 (39.1)173 (53.2)SeraChemiluminescent immunoassay
21Cavdar, 1993
[94]		Case-controlTurkey5.5 (12)≤1872 (100)72 (31.9)18/19 (94.7)Fresh tumor
biopsies		Southern blotting + PCR
22Cavdar, 1994
[95]		Case-controlTurkeyMedian 5 (4.5)≤1881 (100)81 (30)29/32 (90.6)Fresh tumor
biopsies		Immunofluorescence
23Chabay, 2002
[107]		Cross-sectionalArgentinaRange, 13.75≤1812 (100)12 (NR)3 (25)FFPEISH + PCR
24Chan, 1995
[156]		Cross-sectionalChina40.8 ± 24.9 (81)≤1810 (55.6)18 (44.4)5 (27.8)FFPEISH
45 < Age > 188 (44.4)
25Chao, 1997
[157]		Cross-sectionalTaiwan33 ± 24.3 (72)≤186 (33.3)18 (33.3)10 (55.6)FFPEISH
45 < Age > 187 (38.9)
≥455 (27.8)
26Chen, 2016
[158]		Cross-sectionalTaiwanMedian 27 (82)≤1821 (38.9)54 (33)11 (20.4)FFPEISH
NR33 (66.1)
27Cho, 2008
[159]		Cross-sectionalSouth Korea36 (NR)NRNR26 (38.5)3 (11.5)FFPEISH
28Coghill, 2020
[59]		Case-controlGhana8.3 (17)≤18150 (100)150 (36.7)33 (22.0)SeraMicroarray
29Cool, 1997
[60]		Cross-sectionalKenyaRange, 56NRNR21 (NR)17/17 (100)FFPEISH
30De-Thé, 1978
[61]		Case-controlUganda6.6 ± 2.6 (9)≤1814 (100)14 (35.7)6 (42.9)Fresh tumor
biopsies		Nucleic acid hybridization
31Deyhimi, 2014
[123]		Cross-sectionalIran21 (79)NRNR18 (27.8)9 (50)FFPEISH + PCR
32Donati, 2006
[110]		CohortBrazil6.2 (NR)≤1858 (100)58 (34.5)36 (62.1)Fresh tumor
biopsies		HIS-FISH
33Drut, 1994
[109]		Cross-sectionalArgentinaNR≤1816(100)16 (50)4 (25.0)FFPEPCR
34Edwards, 1994
[127]		Cross-sectionalUSANRNRNR4 (NR)2 (50)Fresh tumor
biopsies		PCR
35Feng, 2007
[160]		Cross-sectionalChinaMedian 18.5 (31)≤181 (50)2 (50)2 (100)FFPENucleic acid hybridization
45 < Age > 181 (50)
36Gerber, 1976
[62]		Case-controlGhanaRange, 12≤1846 (100)46 (NR)22 (47.8)SeraImmunofluorescence
37Geser, 1983
[63]		Cross-sectionalUganda and Sudan7.5(15)≤1874 (100)74 (80)51/53 (96.2)Fresh tumor biopsiesNucleic acid hybridization
38Gonin, 2011
[142]		CohortFranceNRNRNR18 (NR)4 (22.2)FFPEISH
39Gotlieb-
Stematsky, 1976
[96]		Case-controlIsrael7.2 ± 4.9 (16)≤1815 (93.8)16 (37.5)11/12 (91.7)SeraImmunofluorescence
45 < Age > 181 (6.2)
40Granai, 2020
[64]		CohortUgandaNRNRNR24 (NR)18 (75)FFPEIHC
41Grässer, 1994
[143]		Cross-sectionalUKNRNRNR3 (NR)3 (100)FFPE and fresh tumor biopsiesISH
42Guarner, 1991
[128]		Cross-sectionalUSA36.7 ± 3.9 (10)45 < Age > 186 (100)6 (NR)6 (100)FFPEISH
43Gulley, 1995
[129]		Cross-sectionalUSA35 (46)NRNR4 (50)2 (50)FFPEISH
44Gutterrez, 1992
[111]		Cross-sectionalSouth America (Brazil, Chile, and Argentina)7.4 ± 5.1 (27)≤1837 (94.9)39 (18)20 (51.3)Fresh tumor biopsiesSouthern blotting
45 < Age > 182 (5.1)
45Habeeb, 2021
[97]		Case-controlSyria11.5 (56)4 to 1237 (92.5)40 (27.5)22 (55)FFPEISH
48-603 (7.5)
46Hamilton-Dutoit, 1991
[112]		Cross-sectionalArgentina45 ± 18.5 (59)45 < Age > 184 (57.1)7 (14.3)1/5 (20)FFPEISH
≥453 (42.9)
47Hamilton-Dutoit, 1993a
[144]		Cross-sectionalDenmark37.2 ± 13.1 (64)≤181 (5.3)19 (0)11 (58)Fresh tumor biopsiesSouthern blotting
45 < Age > 185 (26.3)
≥4513 (68.4)
48Hamilton-Dutoit, 1993b
[145]		Cross-sectionalDenmarkNRNRNR35 (NR)12 (34.3)FFPEISH
49Hassan, 2006
[114]		Case-controlBrazilRange, 14NRNR35 (NR)25 (71.4)FFPEISH+PCR
50Hassan, 2008
[113]		Cross-sectionalBrazilMedian 5 (12)≤1854 (100)54 (33.3)33 (61.1)FFPE and fresh tumor biopsiesISH + PCR
51Henle, 1969
[66]		Case-controlKenyaNRNRNR92 (NR)82 (89.1)SeraImmunofluorescence
52Henle, 1970
[76]		Case-controlKenyaNRNRNR79 (NR)28 (35.4)Fresh tumor biopsiesImmunofluorescence
53Henle, 1971
[67]		Case-controlKenyaNRNRNR156 (NR)120 (76.9)SeraImmunofluorescence
54Henle, 1976
[65]		Case-controlUganda and GhanaNRNRNR54 (NR)15 (27.8)SeraImmunofluorescence
55Hirshaut, 1973
[178]		Case-controlUganda and USANRNRNR36 (NR)22 (61.1)SeraImmunofluorescence
56Hishima, 2006
[161]		CohortJapan34.7 (16)45 < Age > 186 (100)6 (0)1 (16.7)Fresh tumor biopsiesISH
57Huang, 2009
[162]		Cross-sectionalChina27.8 ± 20.6 (69)≤187 (33.3)21 (19.0)6 (28.6)FFPEISH
45 < Age > 1810 (47.6)
≥454 (19.1)
58Hummel, 1995
[146]		Cross-sectionalGermanyNRNRNR36 (NR)11 (30.6)FFPEISH
59Iliyasu, 2014
[68]		Cross-sectionalNigeriaNRNRNR28 (NR)23 (82.1)FFPEISH
60Joab, 1991
[179]		Case-controlFrance & ChinaNRNRNR22 (NR)11 (50)SeraImmunofluorescence
61Kabyemera, 2013
[98]		Case-controlTanzaniaRange, 14≤1832 (100)32 (56.2)19 (59.4)BloodPCR
62Kaymaz, 2017
[69]		Cross-sectionalKenyaMedian 8.2 (12)≤1828 (100)28 (29.0)26 (92.9)Fresh tumor biopsiesPCR
63Kersten, 1998
[147]		Cross-sectionalNetherlandsNRNRNR10 (NR)4 (40)FFPEISH
64Kim, 2005
[163]		Cross-sectionalSouth KoreaRange, 17.2≤1819 (100)19 (NR)0 (0)FFPEISH
65Klein, 1969
[71]		Case-controlKenyaNRNRNR20 (NR)18 (90)SeraImmunofluorescence
66Klein, 1970
[70]		Case-controlKenya7.2 ± 3.0 (12)≤1819 (100)19 (42.1)15 (78.9)SeraImmunofluorescence
67Klumb, 2004
[115]		Case-controlBrazilRange, 8≤1837 (100)37 (32.4)21/29 (72.4)FFPE and fresh tumor biopsiesPCR
68Labrecque, 1999
[72]		Cross-sectionalMalawi7.1 (10)≤1846 (100)46 (39.1)46 (100)FFPE and fresh tumor biopsiesISH
69Lam, 1999
[164]		Cross-sectionalChina47.7 ± 31.8 (61)≤182 (66.7)3 (100)1 (33.3)FFPEISH
≥451 (33.3)
70Lara, 2014
[116]		Cross-sectionalArgentinaRange, 15≤1827 (100)27 (37.0)10 (37.0)FFPEISH
71Lee, 1991
[165]		Cross-sectionalTaiwanRange, 14≤1811 (100)11 (NR)0 (0)FFPESouthern blotting
72Lehtinen, 1992
[183]		Cross-sectionalFinland and Tanzania28.3 (65)NRNR35 (42.9)14 (40)FFPEISH
73Levine, 1971
[130]		Case-controlUSA13.7 ± 9.2 (40)≤1823 (79.3)29 (41.4)24 (82.8)SeraImmunofluorescence
45 < Age > 186 (20.7)
74Liebowitz, 1998
[131]		Cross-sectionalUSANRNRNR3 (NR)3 (100)Fresh tumor biopsiesPCR
75Lindahl, 1974
[73]		Cross-sectionalAfrica7.6 ± 2.9 (10)≤1827 (100)27 (44.4)26 (96.3)FFPE and fresh tumor biopsiesNucleic acid hybridization
76Mansoor, 1997
[124]		Cross-sectionalPakistan10.7 ± 5.7 (18)≤189 (90)10 (30)8 (80)FFPEISH
45 < Age > 181 (10)
77Marchini, 1994
[148]		Cross-sectionalSwedenNRNRNR16 (NR)2 (12.5)SeraELISA
78Mbulaiteye, 2014
[132]		Cross-sectionalUSANR0–1924 (26)91 (13)24/82 (29.3)FFPEISH
20–3414 (15)
35–5926 (29)
≥6017 (19)
NR10 (11)
79Minnicelli, 2012
[117]		Case-controlBrazilMedian 5 (12)≤1862 (100)62 (30.6)33/61 (54.1)FFPEISH
80Mitarnun, 2004
[172]		Cross-sectionalThailand35.6 (31)45 < Age > 185 (100)5 (40)3 (60)FFPEISH + PCR
81Monteiro, 2009
[118]		Cross-sectionalBrazilRange, 95≤157/10 (70)12 (33.3)10 (83.3)FFPEISH
>153/10 (30)
82Monteiro, 2019
[119]		Cross-sectionalBrazil23,8 (95)NRNR12 (33.3)8/12 (66.7)FFPEISH
83Muddathir, 2020
[74]		Case-controlSudanRange, 11≤1834 (100)34 (38.2)15 (44.1)FFPEIHC
84Mundo, 2017
[149]		CohortItaly14.2 (38)NRNR10 (50)4 (40)FFPEISH
85Mutalima, 2008
[75]		Case-controlMalawi7.1 ± 2.6 (15)≤18148 (100)148 (40)128/138 (92.8)SeraImmunofluorescence
86Navari, 2015
[189]		Cross-sectionalItalian and African35 ± 22.8 (74.5)≤188 (26.7)30 (7/20 [35%])17 (56.7)FFPEDASL
45 < Age > 189 (30)
≥4510 (33.3)
NR3 (10)
87Ndede1, 2019
[77]		Cross-sectionalKenyaNR≤1833 (100)33 (21.2)32 (97)Sera + fresh tumor biopsiesELISA + IHC
88Niedobitek, 1995
[78]		Case-controlUganda and Malawi8.2 ± 3.9 (14)≤1817 (100)17 (53)17 (100)FFPEISH
89Nomure, 2008
[166]		Cross-sectionalJapan6.2 ± 2.7 (10)≤1812 (100)12 (25)10 (83.3)FFPEISH
90Nonoyama, 1973
[79]		Cross-sectionalKenyaNRNRNR23 (NR)22 (95.7)Fresh tumor biopsiesNucleic acid hybridization
91Nonoyama, 1974
[133]		Cross-sectionalUSANRNRNR3 (NR)0 (0)Fresh tumor biopsiesNucleic acid hybridization
92Nonoyama, 1975
[80]		Cross-sectionalKenyaNRNRNR26 (NR)22 (84.6)Fresh tumor biopsiesNucleic acid hybridization
93Okano, 1992
[167]		CohortJapan14.7 ± 12.7 (35)≤186 (85.7)7 (42.9)4 (57.1)Fresh tumor biopsiesSouthern blotting
45 < Age > 181 (14.3)
94Olweny, 1977
[81]		Cross-sectionalUganda7.2 (13)≤1834 (100)34 (32.3)27 (79.4)Fresh tumor biopsiesNucleic acid hybridization
95Ometto, 1997
[150]		Cross-sectionalItalyNRNRNR5 (NR)4 (80.0)FFPEPCR
96Onwubuya, 2015
[82]		Cross-sectionalNigeria16.9 (50)0–206 (85.7)7 (28.6)2 (28.6)FFPEISH
41–601 (14.3)
97Ouyang, 2019
[168]		Cross-sectionalChinaNRNRNR22 (NR)14 (63.6)FFPEISH
98Pagano, 1973
[134]		Cross-sectionalUSANRNRNR27 (NR)22 (81.5)Fresh tumor biopsiesNucleic acid hybridization
99Pallesen, 1991
[151]		Cross-sectionalDenmark39.3 ± 6.4 (12)45 < Age > 188 (61.5)3 (0)2 (66.7)Fresh tumor biopsiesISH
100Parolini, 2002
[152]		Case-controlItalyNRNRNR12 (0)12 (100)FFPEISH
101Pearson, 1969
[153]		Case-controlSwedenNRNRNR7 (NR)3 (37.5)SeraImmunofluorescence
102Pedersen, 1991
[154]		CohortDenmarkNRNRNR12 (NR)2/7 (28.6)FFPEISH
103Peh, 2001
[173]		Cross-sectionalMalaysiaNRNRNR8 (0)3 (37.5)FFPEISH
104Peh, 2004
[174]		Cross-sectionalMalaysiaRange, 15≤1822 (100)22 (22.7)6 (27.3)FFPEISH
105Peylan-Ramu, 2001
[99]		CohortIsraelMedian, 5≤1832 (100)32 (25)11 (34.4)FFPEISH
106Piccaluga, 2016
[190]		Cross-sectionalItaly and AfricaNRNRNR30 (NR)13 (43.3)FFPEDASL
107Pizza, 2008
[120]		CohortBrazil6±2.7 (13)≤1853 (100)53 (24.5)33/50 (66.0)FFPEISH
108Prevot, 1992
[83]		Cross-sectionalCameroon, GabonNRNRNR14 (NR)10 (83.3)FFPEISH
109Qin, 2018
[169]		CohortChinaNR≤18105 (100)105 (15.2)18/59 (30.5)Fresh tumor
biopsies		ISH
110Queiroga, 2008
[121]		Cross-sectionalBrazil23.1 (93)≤16149 (47.9)311 (28.9)134/298 (45)FFPEISH
>16143 (46)
NR19 (6.1)
111Quintanilla-Martínez, 1997
[187]		Cross-sectionalMexico and EuropeanNRNRNR5 (NR)2 (40)FFPEISH + PCR
112Rao, 2000
[188]		Cross-sectionalSouthern India and Argentina7.4 ± 5.1 (23)≤1839 (92.9)42 (33.3)28 (66.7)FFPEISH + PCR
45 < Age > 182 (4.7)
NR1 (2.4)
113Razzouk, 1996
[135]		Cross-sectionalUSARange, 13≤189 (100)9 (33.3)1 (11.1)Fresh tumor
biopsies		Immunofluorescence
114Rea, 1994
[155]		Cross-sectionalFranceNRNRNR9 (NR)5/8 (62.5)FFPEISH
115Reedman, 1974
[84]		Cross-sectionalKenya8.7 ± 5.3 (19)≤1816 (84.2)19 (NR)11/19 (57.9)Fresh tumor
biopsies		CF
45 < Age > 182 (10.5)
NR1 (5.3)
116Riverend, 1984
[122]		Case-controlCuba7.6 ± 3.3 (9)≤187 (100)7 (42.9)6 (85.7)FFPECF
117Rowe, 1986
[180]		Cross-sectionalFrance, Algeria, La Rcunion, and England12.1±13.4 (51)≤1814 (82.3)17 (29.4)9 (53)Fresh tumor
biopsies		Immunofluorescence
45 < Age > 182 (11.8)
≥451 (5.9)
118Sakurai, 1983
[170]		Cross-sectionalJapan15.8 ± 16.7 (41)≤184 (80)5 (40)1/4 (25)Fresh tumor biopsiesELISA
≥451 (20)
119Satou, 2015
[171]		Case-controlJapanRange, 85NRNR150 (20.7)33 (22)FFPEISH
120Shiramizu, 1991
[186]		Cross-sectionalGhana & USANRNRNR54 (NR)35 (64)Fresh tumor
biopsies		Southern blotting
121Sinha, 2016
[125]		CohortIndiaNR≤187 (77.7)9 (NR)3 (33.3)PlasmaPCR
NR3 (33.3)
122Stevens, 2001
[85]		Case-controlMalawiNRNRNR12 (NR)12 (100)BloodPCR
123Subar, 1988
[136]		Cross-sectionalUSANRNRNR16 (NR)6 (37.5)Fresh tumor
biopsies		Southern blotting
124Sulitzeanu, 1988
[100]		Case-controlIsraelNRNRNR14 (NR)10 (71.4)seraLMI
125Sutherland, 1978
[86]		Case-controlUgandaNRNRNR9 (NR)1 (11.1)Fresh tumor biopsiesImmunofluorescence
126Syrjänen, 1992
[101]		Cross-sectionalTanzaniaRange, 15NRNR29 (14/27 [51.9%])20 (69)FFPEPCR
127Tacyildiz, 1998
[102]		Cross-sectionalTurkey5.9 (NR)≤1830 (100)30 (NR)28 (93.3)FFPEPCR
128Tao, 1998
[87]		Cross-sectionalGhanaNRNRNR10 (NR)7 (70)Fresh tumor biopsiesPCR
129Teitell, 2005
[137]		Cross-sectionalUSA8.9 ± 4.6 (14)≤1814 (100)14 (14.3)4 (28.6)FFPEISH + PCR
130Tinguely, 2000
[103]		Case-controlTurkey4.8 (9.5)≤1830 (100)30 (NR)14 (46.7)FFPEISH+PCR
131Tumwine, 2010
[88]		Cross-sectionalUgandaNRNRNR86 (NR)79(91.9)FFPEISH
132Uccini, 2018
[104]		Cross-sectionalIraq5.9 ± 3.1≤18125 (100)125 (21.1)100 (80)FFPEISH
133Westmoreland, 2017
[89]		CohortMalawi9.3±3.8≤1888 (100)88 (34.1)76 (86.4)Fresh tumor biopsies and seraISH
134WG, 1996
[108]		Case-controlBrazilMedian, 6≤1813/24 (54.1)24 (8/15 [53.3%])17 (70.8)FFPEISH
135Xue, 2002
[90]		Case-controlMalawi7 ± 2.4 (6)≤187 (100)7 (57.1)4/5 (80)Fresh tumor biopsiesISH
NR: not reported; #: number of cases; FFPE: formalin-fixed paraffin-embedded; ISH: in situ hybridization; qSAT: quantitative suspension array technology; PCR: polymerase chain reaction; HIS-FISH: histology fluorescence in situ hybridization; IHC: immunohistochemistry; ELISA: enzyme-linked immunosorbent assay; DASL: cDNA-mediated annealing, selection, extension, and ligation; CF: complement fixation; and LMI: leukocyte migration inhibition.
Table
Table 3. Subgroup analysis of prevalence of EBV in patients with BL.
Table 3. Subgroup analysis of prevalence of EBV in patients with BL.
SubgroupsPrevalence of EBV
[95% CI]		Studies
Number 		Positive for EBVHeterogeneity
I2, %p Value
Time Interval Trend
    From 1969 to 198264.2 [52.0–75.6]2149795.0<0.01
    From 1983 to 199560.9 [50.3–71.1]3747395.0<0.01
    From 1996 to 200860.7 [51.7–69.3]4393997.0<0.01
    From 2009 to 202154.0 [42.2–65.5]34100598.0<0.01
Methods of EBV detection
    Nucleic acid hybridization81.7 [67.8–92.5]917886.0<0.01
    Polymerase chain reaction (PCR)74.7 [60.0–87.1]1725591.0<0.01
    Immunofluorescence60.0 [45.8–73.5]1853996.0<0.01
    Immunoassay54.7 [34.2–74.5]745390.0<0.01
    In situ hybridization (ISH)54.3 [46.3–62.1]59105897.0<0.01
    ISH+PCR53.2 [52.9–63.3]912160.00.01
    Southern blot47.1 [31.7–62.8]711092.0<0.01
Geographical location
    Sub-Saharan Africa76.5 [67.0–84.9]35150077.0<0.01
    Northern Africa69.3 [58.1–79.4]1434489.0<0.01
    Southern America58.4 [50.0–66.6]1844384.0<0.01
    Southern Asia54.7 [30.5–77.9]32066.00.05
    Northern America54.3 [34.5–73.5]129784.0<0.01
    Europe49.7 [36.9–62.5]1812291.0<0.01
    Eastern Asia29.5 [19.9–40.1]1611986.0<0.01
    South-eastern Asia29.1 [11.0–51.2]41262.00.05
Socio-demographic Index
    High SDI43.0 [33.3–52.9]3525083.00
    High–middle SDI54.5 [40.0–68.6]2120187.00
    Middle SDI60.1 [52.4–67.5]2564182.0<0.01
    Low–middle SDI49.9 [31.4–68.5]811587.00
    Low SDI82.7 [74.4–89.8]28134394.00
BL: Burkitt lymphoma; EBV: Epstein-Barr virus; CI: Confidence interval; SCI: socio-demographic index.
Table
Table 4. Sensitivity analyses.
Table 4. Sensitivity analyses.
Strategies of Sensitivity AnalysesPrevalence
[95% CIs] (%)		Difference of Pooled Prevalence Compared to the Main ResultNumber of Studies AnalyzedTotal Number of SubjectsHeterogeneity
I2, %p Value
Excluding small studies (<100)64.0 [40.3–84.9]4.6% higher8161399%<0.001
Excluding low- and moderate-quality studies58.7 [51.8–65.3]0.7 lower88338393%<0.01
Considering only cross-sectional studies54.4 [50.1–64.6]5% lower79211491%<0.01
Considering only case-control
studies		67.6 [58.0–76.5]8.2% higher39221897%<0.01
Considering only cohort
studies		48.4 [35.9–61.1]11% lower1747585%<0.01
Considering only studies where the age was less than 18 years old64.9 [55.4–74.0]5.5% higher44218795%<0.01
Excluding outlier studies61.0 [55.8–66.1]1.6% higher132479892%<0.01
CIs: confidence intervals.
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Al-Khreisat, M.J.; Ismail, N.H.; Tabnjh, A.; Hussain, F.A.; Mohamed Yusoff, A.A.; Johan, M.F.; Islam, M.A. Worldwide Prevalence of Epstein–Barr Virus in Patients with Burkitt Lymphoma: A Systematic Review and Meta-Analysis. Diagnostics 2023, 13, 2068. https://doi.org/10.3390/diagnostics13122068

AMA Style

Al-Khreisat MJ, Ismail NH, Tabnjh A, Hussain FA, Mohamed Yusoff AA, Johan MF, Islam MA. Worldwide Prevalence of Epstein–Barr Virus in Patients with Burkitt Lymphoma: A Systematic Review and Meta-Analysis. Diagnostics. 2023; 13(12):2068. https://doi.org/10.3390/diagnostics13122068

Chicago/Turabian Style

Al-Khreisat, Mutaz Jamal, Nor Hayati Ismail, Abedelmalek Tabnjh, Faezahtul Arbaeyah Hussain, Abdul Aziz Mohamed Yusoff, Muhammad Farid Johan, and Md Asiful Islam. 2023. "Worldwide Prevalence of Epstein–Barr Virus in Patients with Burkitt Lymphoma: A Systematic Review and Meta-Analysis" Diagnostics 13, no. 12: 2068. https://doi.org/10.3390/diagnostics13122068

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