The association between C509T, T869C, G915C gene polymorphisms of transforming growth factor-β1 and systemic lupus erythematosus risk: A meta-analysis : Medicine

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Research Article: Systematic Review and Meta-Analysis

The association between C509T, T869C, G915C gene polymorphisms of transforming growth factor-β1 and systemic lupus erythematosus risk: A meta-analysis

Chen, Ning MSa; Dai, Yijun MDb; He, Juanjuan MSb; Zhou, Meng MDb; Sun, Hong MDc; Lin, Ling MSa; Gao, Fei BSb; Lin, He BSb; Yan, Qing MSb,*

Author Information
Medicine 102(11):p e33321, March 17, 2023. | DOI: 10.1097/MD.0000000000033321
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1. Introduction

Systemic lupus erythematosus (SLE), the most common type of lupus, is a chronic and complex autoimmune disease that causes systemic or widespread inflammation. It is characterized by production of antibodies against the nuclear components along with many other clinical manifestations.[1] Women are more likely to suffer from SLE than men and are expected to develop this disease during the reproductive period.[2] Some predisposing factors including genetic, environmental and hormonal factors are considered as the triggers of SLE.[2,3] A diverse array of genetic factors contribute to the development and progression of SLE,[4] but the exact cause of SLE still remains to be unknown.

Cytokines is critical in the pathogenesis of SLE by initiating inflammation and regulating immune responses.[5] Transforming growth factor-beta 1 (TGF-β1) is one of the major cytokines that play an important role in the regulation of cell growth, differentiation, extracellular matrix formation, and immunosuppression.[6,7] Preliminary human studies have reported that the expression of TGF-β1 in SLE might be dysregulated. The production of TGF- β1 in the lymphocytes of SLE patients is reduced compared to those without SLE.[8] Importantly, the total TGF-β1 levels in SLE patients with high disease activity and severe organ damage remained to be low.[9,10] Three-quarters (75%) of SLE patients are completely or partially resistant to TGF-β1.[11] The abnormality of B cells in patients with SLE might be related to aberrant cellular regulation.[12] TGF-β1 knockout mice developed an SLE-like disorder with various autoantibodies and Sjögren’s syndrome-like lymphoproliferative disease, suggesting the possible involvement of TGF-β1 in the pathogenesis of SLE.[13] Several TGF-β1 gene polymorphisms have been identified, including 3 (C-988A, G-800A, and C-509) in the promoter region, 3 (C263T, T869C, and G915C) in the coding region, and position 72 (C insertion) in the non-translated region.[14] Among these, C509T, T869C, and G915C polymorphisms are related to serum TGF-β1 levels.[15,16] In recent years, the relationship between TGF-β1 polymorphism and the risk of SLE has been extensively studied.[12,17–25] However, the results are conflicting as some studies[19,20,22–24] revealed TGF-β1 polymorphisms as risk factors of SLE, whereas other studies[12,17,18,21,25] failed to detect a potential association relationship. Guarnizo-Zuccardi et al[19] reported that there is an association between TGF-β1 codon 25C with SLE susceptibility in Colombian population. Wang et al[20] study showed that T869C polymorphism of the TGF-β1 gene is involved in the development of autoantibodies and the occurrence of aseptic necrosis in patients with SLE. As far as we know, there are no previous meta-analyses that focused on the linkage between TGF-β1 polymorphisms and SLE risk. Hence, a meta-analysis was conducted to evaluate the association between C509T, T869C, and G915C gene polymorphisms of TGF-β1 and SLE susceptibility.

2. Methods

The current meta-analysis study was conducted according to the preferred reporting items for systematic reviews and meta-analysis guidelines (Stang, 2010).

2.1. Search strategy

The EMBASE, PubMed, Cochrane Library, China national knowledge infrastructure, and Wanfang databases were searched for relevant English and Chinese publications using the following search terms: “transforming growth factor-beta1/ transforming growth factor-β1/TGF-β1,” “polymorphism/polymorphisms,” and “systemic lupus erythematosus/SLE” from database inception till November 2022. Two researchers conducted independent literature query and any inconsistency between them were resolved through discussion.

2.2. Inclusion and exclusion criteria

All included studies met the following criteria: Case-control studies; Studies evaluating the association between TGF-β1 polymorphisms (C509T, T869C, G915C) and SLE risk; and; Individual genotype frequencies for which polymorphisms can be extracted. The exclusion criteria were as follows: Repeated or overlapping studies; and Studies with no demanded data available.

2.3. Data extraction and quality assessment

Depending on the selection criteria, all relevant raw data were extracted from each eligible study by 2 researchers independently, and any differences in retrieving results were discussed until a consensus was reached. The following information was extracted from each study: name of the first author, year of publication, country of origin, race, genotyping methods, number of cases and controls, and genotypic frequency of cases and controls. The Newcastle-Ottawa Scale criteria[26] was used to assess the quality of included studies, wherein the scores of ≥6 indicated high quality.

2.4. Statistical methods

Chi-square test was used to determine whether the genotypic frequency of TGF-β1 gene in the control group was in accordance with the Hardy–Weinberg equilibrium (HWE), wherein P > .05 meant no HWE violation. P values, odds ratios (OR), and corresponding 95% confidence intervals (CI) were calculated using the Mantel-Haenszel statistic for allelic, homozygous, heterozygous, dominant or recessive models. The relationship between TGF-β1 C509T, T869C, and G915C gene polymorphisms and SLE risk was evaluated by calculating the pooled ORs with their 95% CIs. There was significant heterogeneity if P < .1, and then a random-effects model was applied; otherwise, a fixed-effects model was used. Subgroup analysis was performed based on HWE in controls and ethnicity to detect the source of heterogeneity. Each study was separately omitted to investigate the impact of individual study on the summary estimates. Potential publication bias were evaluated by Begg and Egger tests. Statistical analysis was conducted using STATA 14.0 software (Stata Corporation, College Station, TX). A 2-sided P value of < .05 revealed a statistically significant difference.

3. Results

3.1. Characteristics of selected studies

Our initial search yielded 417 potential articles. After deleting duplicate articles, 350 articles were still retained for abstract and full-text screening. Of these 350 articles, 323 were excluded due to irrelevant titles and abstracts, and 15 were excluded after reading the full-texts. Ultimately 12 articles[12,17–25,27,28] with 1308 cases and 1714 controls met the inclusion criteria and the data were extracted for conducting a meta-analysis. Of the 12 studies, 7 were Caucasians,[12,17,21,23,25] 4 were Asians[18,20,22,24] and one was Latin American.[19] The detailed information with regard to study selection process was illustrated in Figure 1. The characteristics of the studies included in this meta-analysis were summarized in Table 1. As shown in Table 2, the Newcastle-Ottawa Scale score was between 7 and 9, which indicated that all included studies were of very high quality.

Table 1 - Characteristics of studies included in this meta-analysis.
First author Year Country Ethnicity Genotyping Sample size (case/control) SLE Control
Schotte 12 * 2003 Germany Caucasian MS-PCR 203/158 172 30 1 131 22 5 G915C: 0.003
Caserta 17 2004 USA Caucasian PCR-RFLP 23/32 11 8 4 13 10 9 C509T: 0.039
Lu 18 *,, 2004 China Asian PCR 138/182 89/19/38 47/55/81 2/64/19 164/7/48 18/89/107 0/86/27 C509T: 0.53 T869C: 0.292
Guarnizo 19 *, 2007 Colombia Latin American SSP-PCR 120/102 78/33 41/70 1/17 92/27 10/60 0/15 T869C: 0.051
G915C: 0.603
Wang1 20 2007 Japan Asian PCR-RFLP 196/106 46 106 44 18 58 30 T869C: 0.264
Wang2 29 2007 China Asian PCR-RFLP 80/95 C509T: 0.184
Manolova 22 2013 Bulgaria Caucasian PCR-RFLP 149/134 22 79 48 29 57 48 C509T: 0.127
Sayed 23 2014 Egypt Caucasian ARMS-PCR 56/40 14 24 18 8 20 12 T869C: 0.949
Dar 25 *, 2015 India Asian PCR 13/80 6/0 7/9 0/4 16/0 64/79 0/1 T869C: <0.001
G915C: <0.001
Rezaei 24 *, 2015 Iran Caucasian SSP-PCR 55/138 47/9 7/40 1/6 119/27 17/91 2/20 T869C: <0.001 G915C: 0.146
Paradowska-Gorycka 26 , 2019 Poland Caucasian TaqMan 216/552 24/40 65/82 57/26 55/202 238/255 259/94 C509T: 0.976 T869C: 0.379
Hristova 28 2021 Bulgaria Caucasian TaqMan 59/95 15 28 16 14 45 36 C509T: 0.991
ARMS-PCR = amplification refractory mutation system-polymerase chain reaction, HWE = Hardy-weinberg equilibrium, MS-PCR = mutagenically separated polymerase chain reaction, NA = not available, PCR = polymerase chain reaction, PCR-RFLP = polymerase chain reaction-restriction fragment length polymorphism, SLE = systemic lupus erythematosus, SSP-PCR = sequence-specific primers polymerase chain reaction.
* Indicates G915C and its genotype includes GG, GC, and CC.
Indicates C509T and its genotype includes CC, TC, and TT.
Indicates T869C and its genotype includes TT, TC, and CC.

Table 2 - Quality assessment of included studies by NOS.
Author Year Selection Comparability Exposure
Schotte[12] 2003 ★★★★ ★★ ★★★
Caserta[17] 2004 ★★★ ★★ ★★
Lu[18] 2004 ★★★★ ★★ ★★
Guarnizo[19] 2007 ★★★ ★★ ★★
Wang1[20] 2007 ★★★★ ★★ ★★★
Wang2[29] 2007 ★★★ ★★ ★★★
Manolova[22] 2013 ★★★ ★★ ★★
Sayed[23] 2014 ★★★ ★★ ★★★
Dar[25] 2015 ★★★★ ★★ ★★★
Rezaei[24] 2015 ★★★ ★★ ★★
Paradowska-Gorycka[26] 2019 ★★★★ ★★ ★★★
Hristova[28] 2021 ★★★★ ★★ ★★★

Figure 1.:
Flow diagram of study selection process.

3.2. Quantitative analysis

Table 3 showed pooled ORs and heterogeneity test results with regard to the correlation of C509T, T869C, and G915C polymorphisms of TGF-β1 with SLE risk.

Table 3 - Quantitative analyses of TGF-β1 polymorphisms (C509T, T869C, and G915C) in systemic lupus erythematosus risk.
Variables N* Allelic model Dominant model Recessive model
OR (95% CI) P value OR (95% CI) P value OR (95% CI) P value
C509T T vs C TC/TT vs CC TT vs TC/CC
Total 6 1.08 (0.8–1.45) .003 1.17 (0.93–1.46) .133 1.06 (0.64–1.76) .004
 Caucasian 4 1.25 (0.99–1.59) .228 1.37 (1.05–1.79) .843 1.29 (0.72–2.30) .049
 Asian 2 0.78 (0.37–1.69) .004 0.75 (0.35–1.59) .081 0.74 (0.23–2.38) .006
HWE in controls
 Yes 5 1.04 (0.75–1.45) .002 1.15 (0.92–1.45) .093 1.03 (0.59–1.81) .002
 No 1 1.46 (0.67–3.19) - 1.86 (0.49–6.99) - 1.34 (0.45–3.95) -
T869C C vs T TC/CC vs TT CC vs TC/TT
Total 7 1.05 (0.91–1.20) .368 1.12 (0.88–1.41) .265 1.04 (0.71–1.50) .079
 Caucasian 3 1.13 (0.92–1.40) .571 1.38 (0.99–1.93) .385 0.99 (0.67–1.45) .779
 Asian 3 0.98 (0.79–1.22) .115 0.88 (0.59–1.32) .213 1.55 (0.55–4.37) .004
 Latin American 1 0.97 (0.66–1.41) - 0.95 (0.52–1.72) - 0.96 (0.45–2.03) -
HWE in controls
 Yes 5 1.03 (0.89–1.20) .31 1.11 (0.87–1.41) .173 0.98 (0.76–1.27) .799
 No 2 1.13 (0.77–1.68) .212 1.24 (0.54–2.85) - 4.36 (0.10–194.24) .002
G915C C vs G GC/CC vs GG CC vs GC/GG
Total 5 1.32 (0.56–3.10) .001 1.31 (0.48–3.58) <.001 0.50 (0.14–1.77) .255
 Caucasian 2 0.84 (0.54–1.30) .471 0.93 (0.57–1.49) .713 0.34 (0.07–1.56) .004
 Asian 2 0.75 (0.33–1.70) .122 1.03 (0.05–23.44) .052 - -
 Latin American 1 1.27 (0.92–1.75) - 4.95 (2.33–10.52) - 2.57 (0.10–63.86) -
HWE in controls
 Yes 2 2.17 (0.57–8.32) .013 2.34 (0.52–10.58) .010 1.68 (0.26–10.93) .727
 No 3 0.76 (0.49–1.17) .301 0.81 (0.49–1.32) 0.100 0.15 (0.02–1.31) -
The figures in bold indicate statistically significant values.
Bold number indicates statistical significance.
CIs = confidence intervals, HWE = Hardy–Weinberg equilibrium, ORs = odds ratios, TGF-β1 = transforming growth factor-β1.
*Number of comparisons.
P value of Q-test for heterogeneity test. Random-effects model was used when P value for heterogeneity test <.10; otherwise, fixed-effects model was used.

Association between TGF-β1 C509T polymorphism and SLE risk overall, there were 6 studies with 595 cases and 1090 controls investigating the relationship between TGF-β1 C509T polymorphism and SLE risk. The result revealed that TGF-β1 C509T polymorphism showed no association with SLE risk (OR = 1.17, 95% CI = 0.93–1.46, PH = 0.133), as shown in Figure 2A. Furthermore, separate analyses according to genotype showed consistent results (T vs C: OR = 1.08, 95% CI = 0.8–1.45, PH = 0.003; TC/TT vs CC: OR = 1.17, 95% CI = 0.93–1.46, PH = 0.133; and TT vs TC/CC: OR = 1.06, 95% CI = 0.64–1.76, PH = 0.004) (Table 3). When stratified by ethnicity and HWE, no significant associations were observed in all genetic models (Table 3).

Figure 2.:
Forest plot for the association between SLE risk and TGF-β1 gene polymorphisms in the overall population. (A) C509T (TC/TT vs CC), (B) T869C (TC/CC vs TT), and (C) G915C (GC/CC vs GG). SLE = systemic lupus erythematosus, TGF-β1 = transforming growth factor-β1.

Association between TGF-β1 T869C polymorphism and SLE risk overall, the results concluded from 7 studies with 794 cases and 1200 controls revealed that TGF-β1 T869C polymorphism was not linked with SLE risk (OR = 1.12, 95% CI = 0.88–1.41, PH = 0.265, as shown in Fig. 2B). Furthermore, separate analyses according to genotype showed consistent results (C vs T: OR = 1.05, 95% CI = 0.91–1.20, PH = 0.368; TC/CC vs TT: OR = 1.12, 95% CI = 0.88–1.41, PH = 0.265; and CC vs TC/TT: OR = 1.04, 95% CI = 0.71–1.50, PH = 0.079) (Table 3). Subgroup analyses stratified by ethnicity and HWE suggested no significant correlation of SLE with all genetic models (Table 3).

Association between TGF-β1 G915C polymorphism and SLE risk pooled analysis of 6 studies with 529 cases and 660 controls revealed that TGF-β1 G915C polymorphism had no association with SLE risk (OR = 1.31, 95% CI = 0.48–3.58, PH < 0.001, as shown in Fig. 2C). Furthermore, separate analyses according to genotype showed consistent results (C vs G: OR = 1.32, 95% CI = 0.56–3.10, PH = 0.001; GC/CC vs GG: OR = 1.31, 95% CI = 0.48–3.58, PH < 0.001; and CC vs GC/GG: OR = 0.50, 95% CI = 0.14–1.77, PH = 0.255) (Table 3). When stratified by ethnicity and HWE, no significant association with SLE was observed in all genetic models (Table 3).

3.3. Sensitivity analysis

The influence of each study on the overall OR was evaluated by sensitivity analysis. This procedure confirmed the robustness of the overall results (Fig. 3).

Figure 3.:
Sensitivity analysis to examine the influence of individual study on pooled results. (A) C509T (TC/TT vs CC), (B) T869C (TC/CC vs TT), and (C) G915C (GC/CC vs GG).

3.4. Publication bias

Funnel plot with Begg and Egger tests were performed to assess the publication bias of the included studies. The results showed no evidence of publication bias for studies evaluating the relationship between TGF-β1 gene polymorphisms of C509T (Begg test P = .707; Egger test P = .830; Fig. 4A), T869C (Begg test P = .260; Egger test P = .190; Fig. 4B), and G915C (Begg test P = 1.000; Egger test P = .920; Fig. 4C) and SLE risk.

Figure 4.:
Funnel plot for publication bias. Each point represents a separate study for the indicated association. (A) TGF-β1 C509T (TC/TT vs CC), (B) TGF-β1 T869C (TC/CC vs TT), and (C) TGF-β1 G915C (GC/CC vs GG). TGF-β1 = transforming growth factor-β1.

4. Discussion

The association between TGF-β1 gene polymorphisms and susceptibility to infectious diseases such as cancer and autoimmune diseases has been reported by several studies.[30–33] The TGF-β1 level in the blood of SLE patients remains lower than that in healthy controls. [10,23] However, several studies have revealed conflicting results regarding the relationship between TGF-β1 gene polymorphism and SLE. As far as we know, this is the first comprehensive meta-analysis to assess the influence of TGF-β1 gene polymorphism on the occurrence and development of SLE. This meta-analysis included 12 studies covering 1308 SLE patients and 1714 healthy controls, and showed no correlation of TGF-β1 C509T, T869C, and G915C gene polymorphisms with SLE susceptibility. The current study results combined with other various genetic studies on SLE would contribute to further reveal the polygenic nature of this disease.[23,24] Furthermore, the results of publication bias and subgroup analysis also supported the robustness of our results.

Several studies have investigated the impact of TGF-β1 gene polymorphism (C509T, T869C, and G915C) on SLE, but contradictory results were observed. Dar et al[24] found that SLE was significantly correlated with TGF-β1 T869C CC genotype, but no correlation was observed between SLE and TGF-β1 G915C gene variation. Rezaei et al[21] reported a significant increase in the frequency of TGF-β1 (T869C and G915C) TC haplotypes in the SLE group showed when compared with non-SLE control group after Bonferroni correction. Consistent with our results, the study by Schotte et al[12] suggested no relationship between TGF-β1G915C gene polymorphism and SLE in the German population. Wang et al[29] reported no correlation between TGF-β1 T869C gene polymorphism and SLE risk in Japanese patients. Additionally, Lu et al[18] observed no linkage between TGF-β1 G915C and T869C gene polymorphisms and SLE incidence in Taiwanese population. However, our findings were inconsistent with the results of the study conducted by Guarnizo-Zuccardi et al[19] This indicated that the frequencies of allele C and CG genotype in codon 25 of TGF-β1 gene in Colombian SLE patients were higher than those of healthy controls, which might be due to ethnic differences between populations. The non-TGF-β1 gene-associated relationship could also be attributed to the polygenic contribution of SLE disease, which meant that a single gene only plays a partial role, but in general, multiple genes had a cumulative effect on the occurrence of this disease.[29,34,35]

All studies included in this meta-analysis met the inclusion criteria. However, some limitations of this meta-analysis should be mentioned. Firstly, the sample size of this meta-analysis was relatively small, which might result in a relatively lower statistical power. Secondly, only the studies published in Chinese and English were selected. Even though statistical evidence suggested no publication bias in this meta-analysis, publication bias might still exist because studies with negative results tended to be not published. Thirdly, raw data with regard to other SLE-associated risk factors in the included studies were not available, limiting the assessment on gene-environment and gene-gene interactions. Fourthly, certain subgroup analysis was analyzed based on very limited number of studies, and further research was warranted to confirm these results. Finally, we detected significant statistical heterogeneity in some analyses; however, further analyses according to the ethnicity showed a significant decrease in Caucasian people, indicating that ethnicity may contribute to the significant statistical heterogeneity. Nevertheless, more studies are required to validate our findings because there are limited data for subgroup analysis.

5. Conclusion

The results of this meta-analysis revealed that the existing literature might not support the association relationship between TGF-β1 gene polymorphism (C509T, T869C, and G915C) and SLE risk. Considering the impact of polygenes on SLE, more case-control studies with other necessary genetic polymorphism information could provide reliable evidence regarding the role of TGF-β1 polymorphism in SLE susceptibility.

Author contributions

Conceptualization: Ning Chen.

Data curation: Yijun Dai, Juanjuan He, Hong Sun, He Lin, Qing Yan.

Formal analysis: Ning Chen, Meng Zhou, Ling Lin, Qing Yan.

Investigation: Yijun Dai, Juanjuan He.

Software: Meng Zhou, Ling Lin.

Supervision: Fei Gao.

Validation: Fei Gao.

Writing – original draft: Ning Chen.

Writing – review & editing: Hong Sun, Qing Yan.


confidence intervals
Hardy–Weinberg equilibrium
odds ratios
systemic lupus erythematosus
transforming growth factor-β1


[1]. Mok CC, Lau CS. Pathogenesis of systemic lupus erythematosus. J Clin Pathol. 2003;56:481–90.
[2]. D’Cruz D. Autoimmune diseases associated with drugs, chemicals and environmental factors. Toxicol Lett. 2000;112-113:421–32.
[3]. Dooley MA, Hogan SL. Environmental epidemiology and risk factors for autoimmune disease. Curr Opin Rheumatol. 2003;15:99–103.
[4]. Deapen D, Escalante A, Weinrib L, et al. A revised estimate of twin concordance in systemic lupus erythematosus. Arthritis Rheum. 1992;35:311–8.
[5]. Tsao BP. Update on human systemic lupus erythematosus genetics. Curr Opin Rheumatol. 2004;16:513–21.
[6]. Moustakas A, Pardali K, Gaal A, et al. Mechanisms of TGF-beta signaling in regulation of cell growth and differentiation. Immunol Lett. 2002;82:85–91.
[7]. Rubtsov YP, Rudensky AY. TGFbeta signalling in control of T-cell-mediated self-reactivity. Nat Rev Immunol. 2007;7:443–53.
[8]. Ohtsuka K, Gray JD, Stimmler MM, et al. Decreased production of TGF-beta by lymphocytes from patients with systemic lupus erythematosus. J Immunol. 1998;160:2539–45.
[9]. Becker-Merok A, Eilertsen GO, Nossent JC. Levels of transforming growth factor-beta are low in systemic lupus erythematosus patients with active disease. J Rheumatol. 2010;37:2039–45.
[10]. Jin T, Almehed K, Carlsten H, et al. Decreased serum levels of TGF-beta1 are associated with renal damage in female patients with systemic lupus erythematosus. Lupus. 2012;21:310–8.
[11]. Elbeldi-Ferchiou A, Ben Ahmed M, Smiti-Khanfir M, et al. Resistance to exogenous TGF-beta effects in patients with systemic lupus erythematosus. J Clin Immunol. 2011;31:574–83.
[12]. Schotte H, Willeke P, Rust S, et al. The transforming growth factor-beta1 gene polymorphism (G915C) is not associated with systemic lupus erythematosus. Lupus. 2003;12:86–92.
[13]. Dang H, Geiser AG, Letterio JJ, et al. SLE-like autoantibodies and Sjögren’s syndrome-like lymphoproliferation in TGF-beta knockout mice. J Immunol. 1995;155:3205–12.
[14]. Cambien F, Ricard S, Troesch A, et al. Polymorphisms of the transforming growth factor-beta 1 gene in relation to myocardial infarction and blood pressure. The Etude Cas-Témoin de l’Infarctus du Myocarde (ECTIM) study. Hypertension. 1996;28:881–7.
[15]. Awad MR, El-Gamel A, Hasleton P, et al. Genotypic variation in the transforming growth factor-beta1 gene: association with transforming growth factor-beta1 production, fibrotic lung disease, and graft fibrosis after lung transplantation. Transplantation. 1998;66:1014–20.
[16]. Grainger DJ, Heathcote K, Chiano M, et al. Genetic control of the circulating concentration of transforming growth factor type beta1. Hum Mol Genet. 1999;8:93–7.
[17]. Caserta TM, Knisley AA, Tan FK, et al. Genotypic analysis of the TGF beta-509 allele in patients with systemic lupus erythematosus and Sjogren’s syndrome. Ann Genet. 2004;47:359–63.
[18]. Lu LY, Cheng HH, Sung PK, et al. Single-nucleotide polymorphisms of transforming growth factor-beta1 gene in Taiwanese patients with systemic lupus erythematosus. J Microbiol Immunol Infect. 2004;37:145–52.
[19]. Guarnizo-Zuccardi P, Lopez Y, Giraldo M, et al. Cytokine gene polymorphisms in Colombian patients with systemic lupus erythematosus. Tissue Antigens. 2007;70:376–82.
[20]. Wang B, Morinobu A, Kanagawa S, et al. Transforming growth factor beta 1 gene polymorphism in Japanese patients with systemic lupus erythematosus. Kobe J Med Sci. 2007;53:15–23.
[21]. Moher D, Liberati A, Tetzlaff J, et al. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Ann Intern Med. 2009;151:264–9
[22]. Manolova II, Stanilova S. Gene polymorphisms of immunoregulatory cytokines IL-10 and TGF-β1 in systemic lupus erythematosus. In: Tech R, ed. Genes and Autoimmunity–Intracellular Signaling and Microbiome Contribution; 2013.
[23]. Sayed SK, Galal SH, Herdan OM, et al. Single nucleotide polymorphism T869C of transforming growth factor-beta 1 gene and systemic lupus erythematosus: association with disease susceptibility and lupus nephritis. Egypt J Immunol. 2014;21:9–21.
[24]. Rezaei A, Ziaee V, Sharabian FT, et al. Lack of association between interleukin-10, transforming growth factor-beta gene polymorphisms and juvenile-onset systemic lupus erythematosus. Clin Rheumatol. 2015;34:1059–64.
[25]. Dar SA, Janahi EM, Haque S, et al. Superantigen influence in conjunction with cytokine polymorphism potentiates autoimmunity in systemic lupus erythematosus patients. Immunol Res. 2016;64:1001–12.
[26]. Paradowska-Gorycka A, Roszak M, Stypinska B, et al. IL-6 and TGF-beta gene polymorphisms, their serum levels, as well as HLA profile, in patients with systemic lupus erythematosus. Clin Exp Rheumatol. 2019;37:963–75.
[27]. Guo P, Liu S, Sun X, et al. Association of TGF-ss1 polymorphisms and chronic hepatitis C infection: a meta-analysis. BMC Infect Dis. 2019;19:758.
[28]. Hristova M, Kamenarska Z, Dzhebir G, et al. The role of IL-17 rs2275913, IL-17RC rs708567 and TGFB1 rs1800469 SNPs and IL-17A serum levels in patients with lupus nephritis. Rheumatol Int. 2021;41:2205–13.
[29]. Wang Y, Xiao L, Zhou Y, et al. Association analysis of the promoter -509C/T gene polymorphisms of transforming growth factor-β1 with systemic lupus erythematosus in the Hans in Guizhou province. Chinese J Immunol. 2007;23:415–7.
[30]. Martelossi Cebinelli GC, Paiva Trugilo K, Badaro Garcia S, et al. TGF-beta1 functional polymorphisms: a review. Eur Cytokine Netw. 2016;27:81–9.
[31]. Bhatt M, Kumar S, Siddiqui MH, et al. Influence of cytokine gene polymorphism on the risk of rheumatic heart disease - A meta-analysis. Immunol Lett. 2018;194:69–78.
[32]. Chen G, Hu C, Lai P, et al. Association between TGF-beta1 rs1982073/rs1800469 polymorphism and lung cancer susceptibility: an updated meta-analysis involving 7698 cases and controls. Medicine (Baltim). 2019;98:e18028.
[33]. Fayad A, Hassan E, Salem T. Melatonin receptor 1beta gene polymorphism rs10830963, serum melatonin, TNF-alpha, IL-6, IL-1beta, in Egyptian patients with systemic lupus erythematosus. Egypt J Immunol. 2019;26:101–12.
[34]. Mohammadi S, Saghaeian Jazi M, Zare Ebrahimabad M, et al. Interleukin 10 gene promoter polymorphisms (rs1800896, rs1800871 and rs1800872) and haplotypes are associated with the activity of systemic lupus erythematosus and IL10 levels in an Iranian population. Int J Immunogenet. 2019;46:20–30.
[35]. Vira H, Pradhan V, Umare V, et al. Role of polymorphisms in MMP-9 and TIMP-1 as biomarkers for susceptibility to systemic lupus erythematosus patients. Biomark Med. 2019;13:33–43.

gene polymorphisms; meta-analysis; risk; systemic lupus erythematosus; transforming growth factor-β1

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