REVIEW Association of Transforming Growth Factor-β1 rs1982073 Polymorphism with Susceptibility to Acute Renal Rejection: a Systematic Review and Meta-Analysis Farzaneh Najafi1, Seyed Alireza Dastgheib2, Jamal Jafari-Nedooshan3,*, Mansour Moghimi4, Naeimeh Heiranizadeh3, Mohammad Zare3, Elham Salehi5, Hossein Neamatzadeh6,7 Purpose: The association of rs1982073 (codon 10) polymorphism at Transforming Growth Factor- β1 (TGF-β1) gene with acute renal rejection (ARR) has been reported by several studies. However, the results were controver- sial. To derive a more precise estimation of this association, a meta-analysis was performed. Methods: The eligible literatures were identified through PubMed, Scopus, Web of Science, EMBASE, SciELO, WanFang, and CNKI databases up to July 01, 2019. The pooled odds ratios (ORs) with corresponding 95% confi- dence intervals (CIs) were used to calculate the strength of the association. Results: A total of 23 case-control studies with 795 ARR cases and 1,562 non-AR controls were selected. Pooled data revealed that there was no significant association between TGF-β1 codon 10 polymorphism and an increased risk of ARR in the overall population (C vs. T: OR=0.908, 95% CI 0.750-1.099, p = 0.322; CT vs. TT: OR=1.074, 95% CI 0.869-1.328, p = 0.507; CC vs.TT: OR=0.509, 95% CI=0.738-1.253, p = 0.770; CC+CT vs. TT: OR = 0.917, 95% CI 0.756-1.112, p = 0.376, and CC vs. CT+TT: OR=0.995, 95% CI 0.809-1.223, p = 0.959). Moreover, stratified analysis revealed no significant association between the TGF-β1 rs1982073 polymorphism and ARR risk by ethnicity and cases type (recipient and donor). Conclusion: The current meta-analysis demonstrated that the TGF-β1 rs1982073 polymorphism was not signifi- cantly associated with increased risk of ARR. However, studies with a larger number of subjects among different ethnic groups are needed to further validate the results. Keywords: Acute Renal Rejection; TGF-β1; Polymorphism; Meta-analysis. INTRODUCTION Acute renal rejection (ARR) has been identified as the main cause of renal graft dysfunction during the first year after transplantation(1–3). ARR is associated with chronic structural and functional damage, which causes loss of graft and decrease in patient survival. Moreover, it is associated with other conditions such as cardiovascular disease and overall mortality(4). The improvement of renal transplantation results in the last two decades is largely due to a progressive decrease in the incidence of acute rejection(5). Many scientists ac- knowledge that ARR is a multifactorial disease which mediated by complex immunological mechanisms and a network of interactions between cytokines regulates the immune response to transplanted renal(6,7). Several risk factors for ARR have been identified including low histocompatibility between donor and recipient, the age of donor and recipient, ethnicity, gender, ischemia time, delayed graft function, graft non-adherence, and reduced immunosuppression(8,9). In the recent years, there is an increasing body of re- 1Department of Internal Medicine, Shahid Sadoughi University of Medical Sciences, Yazd, Iran. 2Department of Medical Genetics, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran. 3Department of Surgery, Shahid Sadoughi University of Medical Sciences, Yazd, Iran. 4Department of Pathology, Shahid Sadoughi University of Medical Sciences, Yazd, Iran. 5Department of Basic Science, Faculty of Veterinary Medicine, Ardakan University, Ardakan, Iran. 6Department of Medical Genetics, Shahid Sadoughi University of Medical Sciences, Yazd, Iran. 7Mother and Newborn Health Research Center, Shahid Sadoughi University of Medical Sciences, Yazd, Iran. *Correspondence: Department of Surgery, Shahid Sadoughi Hospital, Ave Sina St, Shahid Ghandi Blvd, Yazd, Iran. Tel: +98-9372726153. Email: jamalnedooshan@yahoo.com Received July 2019 & Accepted April 2020 search highlighting the effects of genetic variants in different cytokines such as IL-2, IL-4, IL-10, TNF‐α, and TGF-β1 in development of ARR (10–12). TGF‐β1 is a multifunctional cytokine with immunosuppressive and fibrogenic properties. TGF-β1 belongs to a fami- ly of multi-functional polypeptides, produced by many cell types, including T lymphocytes, monocytes, vas- cular endothelium and fibroblasts(13,14). TGF-β1 has been conventionally recognized as a guardian against different organ acute rejection(15). The pivotal function of TGF-beta in the immune system is to maintain tol- erance via the regulation of lymphocyte proliferation, differentiation, survival and in both suppressive and in- flammatory immune responses(16,17). It has been known that TGF-β is a cytokine required for the induction and maintenance of transplantation tolerance. Central for transplantation tolerance is the role for TGF-β in the induction of Foxp3 and regulatory capacity in CD4(+) T cells(18,19). Moreover, TGF-β1 has been implicated in many different disorders development of various disor- ders, including coronary heart disease, human cancers, Urology Journal/Vol 18 No. 1/ January-February 2021/ pp. 1-10. [DOI: 10.22037/uj.v0i0.5437] rheumatoid arthritis, and asthma(20,21). The human TGF-β1 gene has previously been mapped to chromosome 19q13.1–13.3, consists of seven ex- ons and spanning a region of 23 kbp(22,23). Several common single nucleotide polymorphisms (SNPs) such as +869T>C, +915G>C, -509C>T, and codon 25 (+74G>C) have been identified at TGF-β1 gene(23). Among them, TGF-β1 rs1982073 (codon 10) polymor- phism has been extensively studied in organ translation outcomes(15). TGF-β1 rs1982073 polymorphism is lo- TGF-β1 rs1982073 and acute renal rejection-Najafi et al. Review 2 Table 1. Characteristics of studies included in the meta-analysis. First Author Country Subjects Genotyping Immunosuppressive AR/non-AR AR non-AR MAFs HWE (Ethnicity) Method Protocol Genotype Allele Genotype Allele TT CT CC T C TT CT CC T C Marshall 2000a UK R SSP-PCR CsA, AZA, 114/76 46 55 13 147 81 39 48 8 126 64 0.336 0.201 (Caucasian) Steroids Marshall 2000b UK D 77/68 34 32 11 100 54 30 24 14 84 52 0.382 0.037 (Caucasian) Alakulppi 2004 Finland R SSP-PCR CsA/FK506, 50/241 31 19 - - - 115 126 - - - NA NA (Caucasian) MMF/AZA, Steroids (CT+CC) (CT+CC) Ligeiro 2004a USA R SSP-PCR CsA, AZA, Steroids 31/35 12 12 7 36 26 14 15 6 43 27 0.385 0.571 (Caucasian) Ligeiro 2004b USA D 31/35 5 22 4 32 30 14 14 7 120 60 0.400 0.324 (Caucasian) Park 2004 Korea R SSP-PCR CsA, AZA, steroids 28/100 3 18 7 24 32 25 50 25 100 100 0.937 1.000 (Asian) Dmitrienko 2005 Canada R RFLP-PCR CsA/FK506, MMF/AZA 50/50 16 24 10 56 44 16 24 10 56 44 0.440 0.854 (Caucasian) , Steroids Guo 2005a China R Microarray CsA/FK506, MMF, 39/90 18 15 6 51 27 18 57 15 93 87 0.483 0.011 (Asian) Steroids Guo 2005b China D 39/90 6 33 0 45 33 (Asian) Chow 2005 China R SSP-PCR CsA 52/77 8 44 - - - 13 64 - - - NA NA (Asian) (CT+CC) (CT+CC) Gendzekhadze 2006 Venezuela R SSP-PCR CsA, MMF, 30/33 12 12 6 36 24 10 14 9 34 32 0.484 0.386 (Mixed) Steroids Hueso 2006 Spain R RFLP-PCR CsA/FK506, steroids, 14/63 6 5 3 17 11 20 28 15 68 58 0.460 0.402 (Caucasian) MMF/SRL Canossi 2007a Italy R SSP-PCR CsA, MMF/AZA, 25/61 4 15 6 23 27 14 29 18 57 65 0.532 0.725 (Caucasian) Steroids Canossi 2007b Italy D 20/50 5 12 3 13 26 11 52 48 0.480 0.768 (Caucasian) Brabcova 2007 Czech R SSP-PCR CsA/FK506, MMF, 190/246 32 91 67 155 225 34 128 84 196 296 0.601 0.179 (Caucasian) Steroids Grinyo 2008 Spain R AS-PCR CsA, MMF, Steroids 63/161 18 34 11 70 56 66 69 26 201 121 0. 272 0.272 (Caucasian) Mendoza 2008 Mexico R SSP-PCR CsA/FK506, AZA, 19/32 11 8 - - 25 7 - - NA NA (Mixed) Steroids (TT+CT) (CT+CC) Manchanda 2008a India R ARMS-PCR CsA, AZA, Steroids 18/82 1 11 6 13 23 19 45 18 83 81 0.493 0.376 (Asian) Manchanda 2008b India D 18/82 3 6 9 12 24 13 48 21 74 90 0.591 0.011 (Asian) Karimi 2012 Iran(Asian) R ARMS-PCR CsA, MMF, Steroids 29/71 5 8 16 18 40 17 24 30 58 84 0.591 0.011 Seyhun 2012 Turkey R SSP-PCR CsA/FK506, MMF, 19/71 6 10 3 22 16 16 31 24 63 79 0.556 0.330 (Caucasian) Steroids Saigo 2014 Japan(Asian) R DS NA 24/111 5 16 3 26 22 22 51 36 95 123 0.564 0.612 Seyhun 2015 Turkey R SSP-PCR CSA, TAC/ MPA, 28/62 6 15 7 27 29 16 28 18 60 64 0.516 0.450 (Caucasian) MMF, AZA 28 18 60 64 0.516 0.450 Abbreviations: ARR: Acute Renal Rejection; R: Recipient; D: Donor; PCR: Polymerase Chain Reaction; SSP: Single Specific Primer; RFLP: Restriction Fragment Length Polymorphism; AS: Allele-specific; ARMS: Amplification Refractory Mutation System; DS: Direct Sequencing; AR: Acute Rejection; non- AR: Non Acute Rejection; NA: Not Applicable; MAFs: Minor Allele Frequencies; HWE: Har- dy–Weinberg equilibrium. Vol 18 No 1 January-February 2021 3 cated at position 10 (exon 1) in the signal peptide and has a central role in exporting of the newly synthesized protein through endoplasmic reticulum (ER) membrane (24). In the recent decade, an increasing number of stud- ies are being conducted on the impact of TGF-β1 rs1982073 (codon 10) polymorphism on the clinical outcomes of renal transplantation(15,25). Nevertheless, the results of these studies were not always consistent and controversial. For example, li et al., reported that TGF-β1 rs1982073 polymorphism might be useful in predicting the risk of ARR. By contrast, Karimi et al., in a case-control study showed that TGF-β1 rs1982073 (codon 10) polymorphism was not significantly asso- ciated with risk of ARR in the Iranian patients(26). To clarify the association between TGF-β1 rs1982073 polymorphism and ARR risk, we performed this me- ta-analysis of all eligible published studies. MATERIALS AND METHODS Literature Search Strategy A comprehensive literature search in PubMed, Scopus, EMBASE, Cochrane Library, Web of Science, Elsevi- er, SciELO, SID, WanFang, VIP, Chinese Biomedical Database (CBD) and Chinese National Knowledge In- frastructure (CNKI) to identify all eligible studies on TGF-β1 rs1982073 polymorphism with risk of ARR published up to July 01, 2019. The combination of fol- lowing keywords and terms were adopted in the elec- tronic searches: (‘’Acute Renal’’ OR ‘’Renal Graft Rejection’’ OR “Acute Renal Rejection” OR “Renal Allograft Rejection”) AND (“Transforming growth Factor-β1” OR ‘’TGF-β1’’) AND (‘’Codon 10’’ OR ‘’+869T>C’’ OR ‘’+10T>C’’ OR ‘’T869C’’ OR ‘’rs1982073’’ OR ‘’Leu10>Pro10’’) AND (‘’Gene’’ OR “Single Nucleotide Polymorphism” OR “SNPs” OR ‘’Genotype’’ OR ‘’Allele’’ OR ‘’Variation’’ OR “Variant” OR ‘’Mutation’’). Moreover, a manual search of the reference lists performed to retrieved articles for Heterogeneity Odds Ratio Publication Bias Subgroup Genetic Model Type of Model I2 (%) PH OR 95% CI Z test POR PBeggs PEggers Overall Population C vs. T Random 52.32 0.004 0.908 0.750-1.099 -0.999 0.322 0.944 0.521 CT vs. TT Fixed 33.07 0.076 1.074 0.869-1.328 0.664 0.507 0.381 0.249 CC vs.TT Fixed 0.00 0.509 0.961 0.738-1.253 -0.293 0.770 0.871 0.880 CC+CT vs. TT Fixed 36.87 0.047 0.917 0.756-1.112 -0.885 0.376 0.096 0.056 CC vs. CT+TT Fixed 6.70 0.372 0.995 0.809-1.223 -0.051 0.959 0.032 0.163 By Ethnicity Caucasians C vs. T Random 57.76 0.009 0.852 0.662-1.096 -1.248 0.212 0.212 0.196 CT vs. TT Fixed 2.45 0.420 1.137 0.889-1.452 1.024 0.306 9.303 0.290 CC vs.TT Fixed 0.00 0.918 0.969 0.712-1.319 -0.199 0.842 0.837 0.902 CC+CT vs. TT Fixed 28.16 0.161 0.943 0.719-1.238 -0.423 0.672 0.246 0.253 CC vs. CT+TT Fixed 0.00 0.947 0.917 0.715-1.176 -0.681 0.496 0.114 0.074 Asians C vs. T Random 53.86 0.043 1.048 0.738-1.487 0.262 0.793 0.133 0.042 CT vs. TT Random 62.30 0.014 1.133 0.528-2.432 0.321 0.748 0.548 0.162 CC vs.TT Fixed 50.94 0.057 1.031 0.588-1.809 0.107 0.915 1.000 0.921 CC+CT vs. TT Random 58.65 0.024 1.159 0.582-2.309 0.421 0.674 0.229 0.037 CC vs. CT+TT Random 53.81 0.043 1.060 0.560-2.004 0.178 0.858 0.386 0.122 By Subjects Recipient C vs. T Fixed 19.61 0.235 0.960 0.837-1.101 -0.588 0.556 0.692 0.950 CT vs. TT Fixed 32.29 0.110 0.984 0.776-1.249 -0.130 0.897 0.234 0.348 CC vs.TT Fixed 0.00 0.450 0.983 0.734-1.315 -0.118 0.906 1.000 0.797 CC+CT vs. TT Random 41.16 0.044 0.889 0.661-1.196 -0.778 0.437 0.095 0.127 CC vs. CT+TT Fixed 0.00 0.699 1.019 0.814-1.275 0.162 0.872 0.095 0.468 Donor C vs. T Random 84.00 ≤0.001 0.687 0.313-1.511 -0.933 0.351 0.806 0.597 CT vs. TT Fixed 23.63 0.264 1.495 0.941-2.374 1.703 0.083 1.000 0.867 CC vs.TT Fixed 2.04 0.395 0.866 0.460-1.632 -0.444 0.657 0.462 0.639 CC+CT vs. TT Fixed 0.00 0.458 1.261 0.815-1.950 1.041 0.298 1.000 0.518 CC vs. CT+TT Fixed 57.47 0.052 0.866 0.505-1.486 -0.523 0.601 0.806 0.778 Genotyping Methods SSP-PCR C vs. T Random 58.99 0.009 0.817 0.621-1.076 -1.437 0.151 0.152 0.291 CT vs. TT Fixed 12.18 0.328 1.106 0.845-1.446 0.733 0.463 0.008 0.054 CC vs.TT Fixed 0.00 0.822 0.929 0.666-1.296 -0.433 0.665 0.755 0.672 CC+CT vs. TT Fixed 28.29 0.167 0.862 0.678-1.097 -1.208 0.227 0.046 0.027 CC vs. CT+TT Fixed 0.00 0.619 0.908 0.710-1.162 -0.767 0.443 0.062 0.089 ARMS-PCR C vs. T Fixed 0.00 0.947 1.648 1.092-2.486 2.379 0.017 1.000 0.425 CT vs. TT Fixed 23.61 0.270 1.128 0.464-2.738 0.265 0.791 1.000 0.527 CC vs.TT Fixed 0.00 0.597 2.195 0.941-5.116 1.820 0.069 0.296 0.241 CC+CT vs. TT Fixed 0.00 0.412 1.542 0.695-3.419 0.287 1.066 1.000 0.495 CC vs. CT+TT Fixed 0.00 0.711 2.010 1.133-3.567 2.386 0.017 1.000 0.694 HWE* C vs. T Random 54.61 0.004 0.877 0.712-1.080 -1.233 0.218 0.773 0.435 CT vs. TT Random 11.86 0.318 1.151 0.906-1.462 1.152 0.249 0.324 0.251 CC vs.TT Fixed 0.00 0.482 0.954 0.718-1.268 -0.322 0.748 0.820 0.789 CC+CT vs. TT Random 41.50 0.031 0.949 0.715-1.261 -0.360 0.719 0.107 0.068 CC vs. CT+TT Fixed 5.570 0.388 0.989 0.794-1.232 -0.097 0.922 0.014 0.160 Table 2. Summary risk estimates for association of TGF-β1 rs1982073 polymorphism with risk of ARR. Abbreviations: ARR: acute renal rejection; PCR: Polymerase Chain Reaction; SSP: Single Specific Primer; ARMS: Amplification Refractory Mutation System. *By excluding HWE violating studies. TGF-β1 rs1982073 and acute renal rejection-Najafi et al. additional potential studies. Publication language was restricted to English, Chinese, and Farsi. Moreover, non-English publications were translated and included in the meta-analysis. Inclusion and Exclusion Criteria The inclusion criteria for the gene association studies in this meta-analysis were as follows: 1) studies with case-control or cohort design; 2) only full-text published studies; 3) studies evaluated the association of TGF-β1 rs1982073 (codon 10) polymorphism with ARR risk; 4) provided the genotype distribution in both cases and controls for estimating an odds ratio (OR) with 95% confidence interval (CI); and 5) at least two comparison groups (ARR group vs. non-AR group). The exclusion criteria were as follows: 1) case only studies (without controls); 2) non-human studies; 3) family‐based, sib- ling, twins and linkage studies; 4) studies without de- tails of genotype frequencies, which were unable to calculate ORs; 5) studies on other polymorphisms of TGF-β1 gene; 6) abstracts, case reports, case series, letters, comments, conference presentations, posters, editorials, reviews, and previous meta-analyses; and 7) duplica¬tion of the previous publication; and 8) dupli- cates or overlapping studies. If the authors published two or more studies using the same data or overlapping data, the newest publication or the publication with the largest sample size was selected. There was no any lim- itation by ethnicity, race, placed or geography area. Data Extraction Two authors (HN and MJA) carefully extracted data from all eligible studies using a standardized form. Then, they have checked the data extraction results and reached consensus. Any disagreement between the two authors was resolved by discussion with a third author. The following data were collected from each study: first author, year of publication, country of origin, ethnic- ity (Asians, Caucasians, African, Mixed population), type of cases (recipient and donor), genotyping method, number of cases and controls, genotypes frequencies of cases and controls, minor allele frequencies (MAFs) and Hardy-Weinberg equilibrium test in control sub- jects (non-ARR). In this meta-analysis the diverse eth- nicities were categorized as Caucasian, Asian, Africans, and Mixed population (unknown or more than one ra- cial group). Statistical Analysis An ethical approval was not necessary as this study was a meta-analysis based on previous studies. The strength of the association between TGF-β1 rs1982073 (codon 10) polymorphism and ARR risk was measured by odds ratios (ORs) with 95% confidence intervals (CIs). The statistical significance of the pooled OR was determined using the Z-test. Pooled estimates of the OR were obtained by calculating a weighted average of OR from each study. The pooled ORs was calcu- lated under all five genetic models, i.e., allele (C vs. T), homozygote (CC vs. TT), heterozygote (CT vs. Figure 1. Flow diagram of selecting eligible studies for the meta-analysis. TGF-β1 rs1982073 and acute renal rejection-Najafi et al. Review 4 Vol 18 No 1 January-February 2021 20 TT), dominant (CC+CT vs. TT) and recessive (CC vs. CT+TT). Between-study heterogeneity was estimated by Cochran’s χ2 based Q-statistic test, in which it was considered to be statistically significant at P ≤ 0.05. In addition, I2-value was used to quantify the proportion of heterogeneity, with the range of 0 to 100% (‘‘I2<25% represents no heterogeneity, I2 = 25–50% represents moderate heterogeneity, I2 = 50–75% represents large heterogeneity, and I2>75% represents extreme heter- ogeneity). Accordingly, when between-study hetero- geneity existed (p < 0.05, I2 > 50%) a random-effects model weighted (the DerSimonian-Laird method) was applied to give a more conservative result; otherwise, a fixed-effects model weighted (the Mantel-Haenszel method) method was selected. Fisher’s exact test was used to assess the Hardy-Weinberg equilibrium (HWE) in the control group, in which the significance set at P<0.05. A stratification analysis was conducted by eth- nicity, type of subjects, genotyping methods and HWE to found out the source of heterogeneity. To check the stability and reliability of the pooled ORs, a sensitivity analysis was performed by omitting a single study each time from the all selected studies and reanalyzing the remainder. Begg’s funnel plot a scatter plot of effect against a measure of study size and Egger’s test were used to determine the presence of publication bias in the current meta-analysis; which P<0.05 indicated that the result was statistically significant. All statistical analy- ses were performed using Comprehensive Meta-Analy- sis (CMA) Software version 2.0 (Biostat, Englewood, NJ). All tests were two-sided, and the P values of < 0.05 were considered statistically significant. RESULTS Studies Characteristics As shown in Figure 1, initially, a total of 403 results were identified by electronic and manual searches up to July 01, 2019. After reading the titles and abstracts, 365 were excluded because they were obviously irrele- vant papers to TGF-β1 rs1982073 polymorphism or du- plicates. Then, 19 articles were excluded because they were case reports, case only studies, reviews, previous meta-analysis, did not report usable data. Finally, a total of 23 case-control studies in 18 publications with 795 ARR cases and 1,562 non-AR controls were selected (10,11,26–41). The characteristics of each study are summa- rized in Table 1. All eligible studies were published in English between November, 2000 and June, 2015. Among them, 13 studies were based on Caucasian pop- ulations (5,410 cases and 6,438 controls), eight stud- ies were based on Asian populations (3,137 cases and 3,700 controls), and two studies were based on mixed populations (331 cases and 405 controls). The included studies were performed in UK, USA, Canada, China, Venezuela, Italy, Czech, India, Iran, Spain and Turkey. The genotypes and allele frequency was not applicable Review 438 Figure 2. Forest plot for association of TGF-β1 rs1982073 polymorphism with risk of ARR in the overall population. A: allele model (C vs. T); B: recessive model (CC vs. CT+TT). TGF-β1 rs1982073 and acute renal rejection-Najafi et al. Vol 18 No 1 January-February 2021 5 for three studies. The allele, genotype and minor allele frequency (MAF) distributions in the cases and controls are shown in Table 1. Moreover, the distribution of genotypes in the controls was in agreement with Har- dy-Weinberg equilibrium (HWE) for all selected stud- ies, except for four studies (Table 1). Quantitative Data Synthesis The summary of the meta-analysis of the association of between TGF-β1 rs1982073 polymorphism and risk of ARR are shown in Table 2. Pooled data revealed that there was no significant association between TGF-β1 rs1982073 polymorphism and an increased risk of ARR under all five genetic models, i.e., allele (C vs. T: OR = 0.908, 95% CI 0.750-1.099, p = 0.322, Fig 2A), hete- rozygote (CT vs. TT: OR = 1.074, 95% CI 0.869-1.328, p = 0.507), homozygote (CC vs.TT: OR = 0.509, 95% CI 0.738-1.253, p = 0.770), dominant (CC+CT vs. TT: OR = 0.917, 95% CI 0.756-1.112, p = 0.376), and reces- sive (CC vs. CT+TT: OR = 0.995, 95% CI 0.809-1.223, p = 0.959, Fig 2B). Moreover, we performed subgroup analyses by ethnicity, type of cases (recipient and do- nor) and genotyping methods. When stratified by eth- nicity, no significant association was found in Cauca- sian and Asian populations (Figure 3A, 3B). Moreover, subgroup analysis type of cases (recipient and donor) revealed that TGF-β1 rs1982073 polymorphism was not significantly associated with ARR risk in recipient and donor groups (Table 2). However, there was a sig- nificant association between TGF-β1 rs1982073 poly- morphism and an increased risk of ARR in ARMS-PCR group of studies (C vs. T: OR = 1.648, 95% CI 1.092- 2.486, p = 0.017 and CC vs. CT+TT: OR=0.2.010, 95% CI 1.133-3.567, p = 0.017), but in SSCP-PCR group of studies. Between-Study Heterogeneity Test As shown in Table 2, there was a significant be- tween-study heterogeneity only under the allele mod- el (I2 =52.32; PH=0.004) in the overall population. We conducted subgroup analysis by ethnicity, type of cases, genotyping methods and HWE to found the potential source of heterogeneity in the meta-analysis. Results showed that the heterogeneity was significant- ly decreased by type of cases and genotyping methods. However, after subgroup analysis by ethnicity and ex- cluding HWE-violating studies a moderate to high het- erogeneity was appeared, indicating that ethnicity and HWE might be potential source of between-study heter- ogeneity in the current met-analysis (Table 2). Sensitivity Analysis We performed a sensitivity analysis to assess the in- fluence of each individual study on the pooled ORs by sequential omission of individual studies. However, the corresponding pooled ORs were not materially altered by removing any individual study. Moreover, we have Figure 3. Forest plot for association of TGF-β1 rs1982073 polymorphism with risk of ARR by ethnicity. A: Caucasians (homozygote model: CC vs.TT); B: Asians (recessive model: CC vs. CT+TT). TGF-β1 rs1982073 and acute renal rejection-Najafi et al. Review 6 Vol 18 No 1 January-February 2021 7 performed sensitivity analysis by excluding HWE-vio- lating studies. As shown in Table 2 and Figure 4, sen- sitivity analysis showed that the initial results were not considerably adjusted by omitting the HWE-violating studies. Therefore, the sensitivity analysis confirmed that the results of the present meta-analysis were sta- tistically stable. Publication Bias Publication bias was assessed by Begg’s funnel plot and Egger’s test. The shape of the funnel plots did not revealed any asymmetry under all five genetic models in the overall population (Figure 3). Then, Egger’s test was performed to provide statistical evidence of funnel plot asymmetry. The results indicated a lack of publication bias under all five genetic models, i.e., allele (PBeggs = 0.661; PEggers = 0.856), heterozy- gote (PBeggs = 0.381; PEggers = 0.508), homozygote (PBeggs = 0.661; PEggers = 0.991, Figure 5A), dom- inant (PBeggs = 0.191; PEggers = 0.199, Fig 2B) and recessive (PBeggs = 0.137; PEggers = 0.485). DISCUSSION To date, the cause of ARR has not yet been fully clari- fied. In recent years, numerous studies have revealed an association between TGF-β1 rs1982073 and ARR risk (15). However, the relationship remains controversial. In the current meta-analysis, a total of 23 case-control studies with 795 ARR cases and 1,562 non-AR controls were selected. After pooling the data from all eligible studies, we have shown that TGF-β1 rs1982073 poly- morphism was not significantly associated with an in- creased risk of ARR in the overall population and by ethnicity. Moreover, our subgroup analysis revealed that ARR was not associated with genotype of TGF-β1 rs1982073 polymorphism in renal recipients or donors (Table 2). Thus, our results indicated that TGF-β1 rs1982073 polymorphism might not be useful biomark- er to identify patients predisposed to ARR. The current meta-analysis results are inconsistent with a previous meta-analysis in that revealed that TGF-β1 rs1982073 polymorphism was not significantly asso- ciated with risk of ARR. Ge et al., in a meta-analysis have found a positive association between TGF-β1 rs1982073 polymorphism and ARR. In recent years, some studies already studied potential associations TGF-β1 rs1982073 polymorphism with risk of ARR. However, by including recently published studies which have strong reverse association with TGF-β1 rs1982073 polymorphism, our pooled data did not show a significant association between TGF-β1 rs1982073 polymorphism and ARR in the overall population under all five genetic models. Omrani et al., showed that the TGF-β1 rs1982073 polymorphism did not play a major role in kidney allograft survival(42). In a meta-analysis, Warlé et al., also failed to show a significant associa- tion of TGF-β1 rs1982073 (codon 10) and rs1800471 (codon 25) polymorphisms with an increased risk of acute liver graft rejection(43). Hueso et al., found an independent association between T allele at TGF-β1 rs1982073 polymorphism in recipient and independent predictors of subclinical rejection (SCR)(28). Moreover, Cho et al., reported that TGF-β1 rs1982073 (codon 10) and rs1800471 (codon 25) polymorphisms were not sig- nificantly associated with an increased risk of develop- ment of chronic allograft nephropathy in Korean renal transplant recipients(25). Therefore, our findings are in accordance with the mentioned studies revealed that C allele of TGF-β1 rs1982073 loci was not associat- ed with an increased risk of ARR. However, this result was contradictory to studies performed by Chow et al., Park et al., and Ge et al., which observed an increased risk in renal transplant recipients. In 2005, Chow et al., have demonstrated that the CC genotype of TGF-β1 rs1982073 polymorphism was a potential risk factor for failure of kidney allograft function(41). In 2004, Park et al., have evaluated the association of TGF-β1 polymor- phisms with ARR risk in renal transplant recipients and their donors. They found that the CC genotype in the renal transplant recipients were associated with recur- rent acute rejection episodes in Korean population(38). Ge et al., have found recipient TGF-β1 haplotypes were significantly associated with an increased risk of acute rejection in solid organ transplant recipients, particu- TGF-β1 rs1982073 and acute renal rejection-Najafi et al. Figure 4. Forest plot for association of TGF-β1 rs1982073 polymorphism with risk of ARR after excluding HWE-violating studies under the heterozygote model (CT vs. TT). larly in patients receiving cardiac allograft. In addition, they revealed that the donor TGF-β1 rs1982073 pol- ymorphism was significantly associated with acute re- jection of solid organ transplant in recipients under the heterozygote and dominant models, especially among patients in CsA/ FK 506 group compared with those in CsA group(44). As shown in Table 2, there was a global variation for MAFs of TGF-β1 rs1982073 in the healthy subjects, suggesting a potential subgroup analysis in the world- wide population. However, analysis by ethnicity did not show a significant association between TGF-β1 rs1982073 and ARR under all five genetic models. In addition, most of the selected studies were conducted in Caucasian population, which might be caused to reduce the potential effects of subgroup analysis by ethnicity. Between-study heterogeneity is a common issue when interpreting the pooled data of meta-analyses(45–47). It could be attributable to differences in several factors such as environmental factors, criteria or methodo- logical factors in study design, sample size, source of controls, type of cases, genotyping methods, and so on (48,49). In the present meta-analysis there was a signifi- cant heterogeneity under two genetic models. However, after subgroup analysis a moderate to high heterogene- ity appeared in the Asians under four genetic models, indicating that ethnicity might be potential source of between-study heterogeneity in the met-analysis. All of the studies included in this meta-analysis met our inclusion criteria. In spite of these, several limita- tions that exist in the current meta-analysis have to be addressed. First, the sample size was relatively small which may lead to a relatively small power. Second, we only selected published studies electronically in English, Chinese, and Farsi, so it is possible that some pertinent studies published in other languages or un- published studies with negative results may have been missed. Therefore, publication bias may exist; even no statistical evidence suggests publication bias in the cur- rent meta-analysis. Third, there were only two studies that evaluated the association in mixed populations and subgroup analysis was performed only in Caucasians and Asians. Therefore, it is unknown whether the re- sults will extend to other populations such as Africans and mixed populations. Fourth, only small numbers of studies were included in some subgroups such as donors and ARMS-PCR group of studies. Therefore, these sub- group analyses may not have enough statistical power with the small sample size and the conclusions may be biased. Fifth, our study was designed to analyze the association of TGF-β1 rs1982073 polymorphism with ARR; however, a haplotype analysis may be more pow- erful to find a significant association between TGF-β1 polymorphisms (such as rs1982073 and rs1800471 polymorphisms) and ARR risk. Moreover, due to lack of data, we did not evaluate the effects of gene-gene and gene-environment interactions on development of ARR. CONCLUSIONS This meta-analysis result revealed that TGF-β1 rs1982073 (codon 10) polymorphism was not signifi- cantly associated with an increased risk of ARR. More- over, there was no significant association by ethnicity and genotypes of recipients or donors. Thus, our results indicated that TGF-β1 rs1982073 polymorphism might not be useful biomarker to identify patients predisposed to ARR. Data from large-scale, multicenter, epidemio- logical studies are still needed to validate our findings and the molecular mechanism for the association need to be elucidated in future studies. CONFLICTING INTERESTS The authors declared no potential conflicts of interest with respect to the research or publication of this article. REFERENCES 1. Cecka JM. The OPTN/UNOS Renal Transplant Registry. Clin Transpl. 2005:1-16. 2. Lemoine M, Titeca Beauport D, Lobbedez T, Choukroun G, Hurault de Ligny B, Hazzan M, et al. Risk Factors for Early Graft Failure and Death After Kidney Transplantation in Recipients Older Than 70 Years. Kidney Int Rep. 2019;4:656-666. 3. Köhnke R, Kentrup D, Schütte-Nütgen K, Schäfers M, Schnöckel U, Hoerr V, et al. Update on imaging-based diagnosis of acute renal allograft rejection. Am J Nucl Med Mol Imaging. 2019;9:110-126. 4. Israni A, Leduc R, Holmes J, Jacobson PA, Lamba V, Guan W, et al. Single-Nucleotide Polymorphisms, Acute Rejection, and Severity of Tubulitis in Kidney Transplantation, Accounting for Center-to-Center Variation. Transplantation. 2010;90:1401–8. 5. Pallardó Mateu LM, Sancho Calabuig A, Capdevila Plaza L, Esteve AF. Acute rejection and late renal transplant failure: Risk factors and prognosis. Nephrol Dial Transplant. 2004;19:38-42. 6. Melo Z, Ruiz-Pacheco JA, Mendoza-Cerpa CA, Echavarria R. Immunopathology of kidney transplantation. In Pathophysiology- Altered Physiological States; IntechOpen: London, UK, 2017. 7. Wasowska BA. Mechanisms involved in antibody- and complement-mediated allograft rejection. Immunol Res. 2010;47:25-44. 8. García P, Huerfano M, Rodríguez M, Caicedo A, Berrío F, Gonzalez C. acute rejection in renal transplant patients of a hospital in bogota, colombia. Int J Organ Transplant Med. 2016;7:161-166. 9. Zimmerman D, House AA, Kim SJ, Booth RA, Zhang T, Ramsay T, et al. The risk of acute rejection following kidney transplant by 25-hydroxyvitamin D and 1,25-dihydroxyvitamin D Status: A prospective cohort study. Can J Kidney Health Dis. 2017;4:2054358117699822. 10. Seyhun Y, Mytilineos J, Turkmen A, Oguz F, Kekik C, Ozdilli K, et al. Influence of Cytokine Gene Polymorphisms on Graft Rejection in Turkish Patients with Renal Transplants from Living Related Donors. Transplant Proc. 2012;44:1241-9. 11. Mendoza-Carrera F, Ojeda-Durán S, Angulo E, Rivas F, Macías-López G, Buen EP, et al. Influence of cytokine and intercellular adhesion molecule-1 gene polymorphisms on acute rejection in pediatric renal transplantation. TGF-β1 rs1982073 and acute renal rejection-Najafi et al. Review 8 Vol 18 No 1 January-February 2021 9 Pediatr Transplant. 2008;12:755-61. 12. Hu Q, Tian H, Wu Q, Li J, Cheng X, Liao P. Interleukin-10-1082 G/a polymorphism and acute renal graft rejection: a meta-analysis. Ren Fail. 2016;38:57-64. 13. Martins-Green M, Petreaca M, Wang L. Chemokines and Their Receptors Are Key Players in the Orchestra That Regulates Wound Healing. Adv Wound Care (New Rochelle). 2013;2:327-347. 14. Stoll C, Mengsteab S, Stoll D, Riediger D, Gressner AM, Weiskirchen R. Analysis of polymorphic TGFBI codons 10, 25, and 263 in a German patient group with non-syndromic cleft lip, alveolus, and palate compared with healthy adults. BMC Med Genet. 2004;5:15. 15. Ge Y-Z, Wu R, Lu T-Z, Jia R-P, Li M-H, Gao X-F, et al. Combined Effects of TGFB1 +869 T/C and +915 G/C polymorphisms on acute rejection risk in solid organ transplant recipients: a systematic review and meta- analysis. Coleman WB, editor. PLoS One. 2014;9:e93938. 16. Travis MA, Sheppard D. TGF-β Activation and Function in Immunity. Annu Rev Immunol. 2014;32:51-82. 17. Li MO, Wan YY, Sanjabi S, Robertson A-KL, Flavell RA. Transforming growth factor-beta regulation of immune responses. Annu Rev Immunol. 2006;24:99-146. 18. Regateiro FS, Howie D, Cobbold SP, Waldmann H. TGF-β in transplantation tolerance. Curr Opin Immunol. 2011;23:660- 9. 19. Sanjabi S, Oh SA, Li MO. Regulation of the immune response by TGF-β: From conception to autoimmunity and infection. Cold Spring Harb Perspect Biol. 2017;9:a022236. 20. Gagliardo R, Chanez P, Gjomarkaj M, La Grutta S, Bonanno A, Montalbano AM, et al. The Role of Transforming Growth Factor-β1 in Airway Inflammation of Childhood Asthma. Int J Immunopathol Pharmacol. 2013;26:725- 38. 21. Wynn TA, Ramalingam TR. Mechanisms of fibrosis: therapeutic translation for fibrotic disease. Nat Med. 2012;18:1028-40. 22. Rathod SB, Tripathy AS. TGF-β1 gene - 509C > T promoter polymorphism modulates TGF-β1 levels in hepatitis E patients. Meta Gene. 2015;6:53-8. 23. Frydecka D, Misiak B, Pawlak-Adamska E, Karabon L, Tomkiewicz A, Sedlaczek P, et al. Sex differences in TGFB-β signaling with respect to age of onset and cognitive functioning in schizophrenia. Neuropsychiatr Dis Treat. 2015;11:575-84. 24. Tsukumo Y, Tsukahara S, Saito S, Tsuruo T, Tomida A. A novel endoplasmic reticulum export signal: Proline at the +2 position from the signal peptide cleavage site. J Biol Chem. 2009;284:27500-10. 25. Cho JH, Huh S, Kwon TG, Choi JY, Hur IK, Lee EY, et al. Association of C-509T and T869C polymorphisms of transforming growth factor-beta1 gene with chronic allograft nephropathy and graft survival in Korean renal transplant recipients. Transplant Proc. 2008;40:2355-60. 26. Karimi MH, Daneshmandi S, Pourfathollah AA, Geramizadeh B, Yaghobi R, Rais-Jalali GA, et al. A study of the impact of cytokine gene polymorphism in acute rejection of renal transplant recipients. Mol Biol Rep. 2012;39:509-15. 27. Gendzekhadze K, Rivas-Vetencourt P, Montano RF. Risk of adverse post-transplant events after kidney allograft transplantation as predicted by CTLA-4 + 49 and TNF-α − 308 single nucleotide polymorphisms: A preliminary study. Transpl Immunol. 2006;16:194-9. 28. ueso M, Navarro E, Moreso F, Beltrán-Sastre V, Ventura F, Grinyó JM, et al. Relationship between subclinical rejection and genotype, renal messenger RNA, and plasma protein transforming growth factor-beta1 levels. Transplantation. 2006;81:1463-6. 29. Canossi A, Piazza A, Poggi E, Ozzella G, Di Rocco M, Papola F, et al. Renal Allograft Immune Response Is Influenced by Patient and Donor Cytokine Genotypes. Transplant Proc. 2007 39:1805-12. 30. Brabcova I, Petrasek J, Hribova P, Hyklova K, Bartosova K, Lacha J, et al. Genetic variability of major inflammatory mediators has no impact on the outcome of kidney transplantation. Transplantation. 2007;84:1037-44. 31. Grinyó J, Vanrenterghem Y, Nashan B, Vincenti F, Ekberg H, Lindpaintner K, et al. Association of four DNA polymorphisms with acute rejection after kidney transplantation. Transpl Int. 2008;21:879-91. 32. Manchanda PK, Mittal RD. Analysis of cytokine gene polymorphisms in recipient’s matched with living donors on acute rejection after renal transplantation. Mol Cell Biochem. 2008;311:57-65. 33. Saigo K, Akutsu N, Maruyama M, Otsuki K, Hasegawa M, Aoyama H, et al. Study of Transforming Growth Factor-β1 Gene, mRNA, and Protein in Japanese Renal Transplant Recipients. Transplant Proc. 2014;46:372-5. 34. Seyhun Y, Ciftci HS, Kekik C, Karadeniz MS, Tefik T, Nane I, et al. Genetic association of interleukin-2, interleukin-4, interleukin-6, transforming growth factor-β, tumour necrosis factor-α and blood concentrations of calcineurin inhibitors in Turkish renal transplant patients. Int J Immunogenet. 2015;42:147-60. 35. Marshall SE, McLaren AJ, Haldar NA, Bunce M, Morris PJ, Welsh KI. The impact of recipient cytokine genotype on acute rejection after renal transplantation. Transplantation. 2000;70:1485-91. 36. Alakulppi NS, Kyllönen LE, Jäntti VT, Matinlauri IH, Partanen J, Salmela KT, et al. Cytokine gene polymorphisms and risks of acute rejection and delayed graft function after kidney transplantation. Transplantation. TGF-β1 rs1982073 and acute renal rejection-Najafi et al. 2004;78:1422–8. 37. Ligeiro D, Sancho M, Papoila A, Barradinhas A, Almeida A, Calão S, et al. Impact of donor and recipient cytokine genotypes on renal allograft outcome. Transplant Proc. 2004;36:827-9. 38. Park JY, Park MH, Park H, Ha J, Kim SJ, Ahn C. TNF-α and TGF-β1 gene polymorphisms and renal allograft rejection in Koreans. Tissue Antigens. 2004;64:660-6. 39. Dmitrienko S, Hoar DI, Balshaw R, Keown PA. Immune Response Gene Polymorphisms in Renal Transplant Recipients. Transplantation. 2005;80:1773-82. 40. Guo Y, Tan J, Li R, Liu S, Li Y, Ying K, et al. [Impacts of donor and recipient’s SNP of cytokine and cytokine receptor on early acute renal allograft rejection]. Zhonghua Yi Xue Za Zhi. 2005;85:3126-33. 41. Ming Chow K, Chun Szeto C, Poon P, Yan Lau W, Mac-Moune Lai F, Kam-Tao Li P, et al. Transforming Growth Factor-β1 Gene Polymorphism in Renal Transplant Recipients. Ren Fail. 2005;27:671-5. 42. Omrani MD, Mokhtari M-R, Bagheri M, Ahmadpoor P. Association of interleukin-10, interferon-gamma, transforming growth factor-beta, and tumor necrosis factor- alpha gene polymorphisms with long-term kidney allograft survival. Iran J Kidney Dis. 2010;48:141-6. 43. Warlé MC, Metselaar HJ, Hop WCJ, Tilanus HW. Cytokine gene polymorphisms and acute liver graft rejection: A meta-analysis. Liver Transpl. 2005;11:19-26. 44. Ge YZ, Yu P, Jia R-P, Wu R, Ding A-X, Li L-P, et al. Association between transforming growth factor beta-1 +869T/C polymorphism and acute rejection of solid organ allograft: A meta-analysis and systematic review. Transpl Immunol. 2014;30:76-83. 45. Niktabar SM, Latifi SM, Moghimi M, Jafari- Nedooshan J, Aghili K, Miresmaeili SM, et al. Association of vitamin D receptor gene polymorphisms with risk of cutaneous melanoma. A meta-analysis based on 40 case-control studies. Dermatol Rev/Przegl Dermatol. 2019;106:268–79. 46. Karimi-Zarchi M, Moghimi M, Abbasi H, Hadadan A, Salimi E, Morovati-Sharifabad M, et al. Association of MTHFR 677C>T polymorphism with susceptibility to ovarian and cervical cancers: A systematic review and meta-analysis. Asian Pac J Cancer Prev. 2019;20:2569-2577. 47. Bahrami R, Shajari A, Aflatoonian M, Noorishadkam M, Akbarian-Bafghi MJ, Morovati-Sharifabad M, et al. Association of REarranged during Transfection (RET) c.73 + 9277T > C and c.135G > a Polymorphisms with Susceptibility to Hirschsprung Disease: A Systematic Review and Meta-Analysis. Fetal Pediatr Pathol. 2019:1-15. [Epub ahead of print]. 48. Jafari-Nedooshan J, Moghimi M, Zare M, Heiranizadeh N, Morovati-Sharifabad M, Akbarian-Bafghi MJ, et al. Association of Promoter Region Polymorphisms of IL-10 Gene with Susceptibility to Lung Cancer: Systematic Review and Meta-Analysis. Asian Pac J Cancer Prev. 2019;20:1951–7. 49. Farbod M, Karimi-Zarchi M, Heiranizadeh N, Seifi-Shalamzari N, Akbarian-Bafghi MJ, Jarahzadeh MH, et al. Association of TNF-α -308G>A Polymorphism with Susceptibility to Cervical Cancer and Breast Cancer: a Systematic Review and Meta-analysis. Klinicka Onkologie. 2019;32:170–80. TGF-β1 rs1982073 and acute renal rejection-Najafi et al. Review 10