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. 2021 Mar 18;18(1):1–10. doi: 10.5114/aoms/99839

The association between interleukin-1β gene polymorphisms and the risk of breast cancer: a systematic review and meta-analysis

Bei Wang 1,2,✉, Fenlai Yuan 1,2
PMCID: PMC8826693  PMID: 35154519

Abstract

Introduction

It is reported that there is a close association between interleukin-1β (IL-1β) gene polymorphisms and breast cancer risk. However, the results remain controversial.

Material and methods

Eligible published articles were searched in PubMed, Embase, and Web of Science databases up to June 2018. Odds ratios with 95% confidence intervals were used to identify potential links between IL-1β genetic polymorphisms and the risk of breast cancer.

Results

From our results, we found that three common polymorphisms in IL-1β (rs16944, rs1143634, rs1143627) had no significant associations with breast cancer risk in all genetic models. Based on the analysis from ethnic subgroups, there was a higher risk of breast cancer for rs16944 polymorphism in the recessive model and heterozygous model among Asians (TT vs. CC+CT: 1.229, 95% CI: 1.063–1.422, p = 0.005; TT vs. CT: 1.211, 95% CI: 1.057–1.388, p = 0.006). For the rs1143627 polymorphism, a significantly decreased breast cancer risk was observed in the dominant model only in Asians (CT+TT vs. CC: OR = 0.944, 95% CI: 0.897–0.994, p = 0.027). After stratifying patients according to the menopausal state, we found that polymorphism of rs1143627 correlated with reduced breast cancer risk among post-menopausal women in three genotype models: allele, recessive model and homozygous model (T vs C: 0.859, 95% CI: 0.753–0.98, p = 0.024; TT vs. CC+CT: 0.727, 95% CI: 0.576–0.918, p = 0.007; TT vs. CC: 0.743, 95% CI: 0.626–0.882, p = 0.001). As for other analyses with reference to source of controls and genotyping methods, no significant association between IL-1β polymorphism and breast cancer risk was demonstrated.

Conclusions

The rs16944 and rs1143627 polymorphisms are significantly associated with the risk of breast cancer only in Asian people and in post-menopausal women respectively.

Keywords: polymorphism, breast cancer, interleukin-1β, meta-analysis

Introduction

Breast cancer is a complex multiple process influenced by multiple factors. It is considered that specific gene polymorphisms have effects on gene transcription, mRNA stability and protein activity [1]. Multi-functional cytokines involved in the process of inflammatory and immunological responses are closely associated with the pathogenesis of autoimmune and malignant diseases, making them potential risks for breast cancer [2–4]. The interleukin 1 gene family located on chromosome 2q14.2 includes three members: IL-1α, IL-1β and IL-1 receptor antagonist (IL-RA) encoded by IL-1RN [5]. Interleukin 1α and IL-1β are potent proinflammatory cytokines, whereas IL-RA is an anti-inflammatory cytokine [6]. Interleukin 1β can be produced by various cells and it modifies the process of host response to microbial invasion, tissue injury and inflammation [7].

The interleukin 1β gene has three potentially functional SNPs: –31 (rs1143627, C>T), –511 (rs16944, C>T) in the promoter region and +3954 (rs1143634, C>T) in exon 5 [8, 9]. So far, many studies have been conducted to assess the relations between the three SNPs (rs16944, rs1143634 and rs1143627) in IL-1β and breast cancer risk [10–20]. However, the results remain conflicting. Therefore, we performed this meta-analysis in order to obtain a more precise evaluation of these links.

Material and methods

Literature search

Relevant studies published before June 1st, 2018 were identified through a search in PubMed, Embase, and Web of Science using a combination of the following terms: (“polymorphism” or “SNP” or “single nucleotide polymorphisms”), (“breast cancer” or “breast carcinoma” or “breast tumor”) and (“Interleukin-1” or “IL-1β” or “Interleukin-1 beta”). The references from the eligible articles or textbooks were also manually searched by us to find additional potential sources.

Inclusion and exclusion criteria

The criteria for studies in our meta-analysis are as follows: (a) studies concentrated on relations between IL-1β polymorphisms and breast cancer risk; (b) providing sufficient data for the frequencies of alleles and genotypes; (c) published in English. Studies were excluded when: (a) they were not case-control studies; (b) did not supply complete and essential information; (c) they were meta-analyses, reviews, or editorial articles.

Data extraction

Data were extracted from each publication independently by two authors (Wang and Yuan) based on the inclusion criteria mentioned above. For each study, the data were collected as follows: the first author, year of publication, country of origin, ethnicity, menopausal state, numbers of patients and controls, source of controls, mutation detection methods, genotyping methods, minor allele frequency (MAF), allele and genotype frequencies and the evidence of Hardy-Weinberg equilibrium (HWE) in controls. Disputes were settled by consulting with a third author if disagreements occured.

Statistical analysis

In order to assess the strength of associations between IL-1β gene polymorphisms and breast cancer susceptibility under five genetic models which include the allele model, dominant model, recessive model, homozygous model and the heterozygous model, crude odds ratios (ORs) with their corresponding 95% confidence intervals (CIs) were adopted [21–24]. Hardy-Weinberg equilibrium in control groups was estimated using the χ2 test. The pooled OR’s statistical significance was verified using the Z test with a two-tailed p < 0.05 which is regarded as statistically significant. Between-study variations and heterogeneities were evaluated using either Cochran’s Q-statistic or I2 test. When the result is a p-value < 0.05 or I2 > 50%, it indicates the existence of heterogeneity among studies; only in this circumstance was the random effects model (DerSimonian Laird method) used. If not, the fixed effects model (Mantel-Haenszel method) was performed. In order to explore sources of heterogeneity, we performed subgroup analysis with reference to ethnicity, menopausal state, source of controls and genotyping methods. Additionally, to investigate the sensitivity, we removed each study in turn to evaluate the quality and consistency of results. We used Begg’s funnel plot and Egger’s linear regression test to detect publication biases. All analyses were conducted with STATA version 12.0.

Results

Eligible studies

Four studies with controls deviating from HWE were excluded [12, 15, 18, 20]. Ten case-control studies were conducted by us to assess associations between IL-1β polymorphisms and breast cancer risk. As shown in Tables I and II, 6 studies were eligible for rs16944(C>T) including 2454 cases and 2720 controls [11, 12, 14, 16, 18, 19]. Details are as follows: 1) Caucasians, Asians, and Africans were investigated in 4, 2 and 1 studies respectively, 3 of which were associated with menopause. 2) Only 1 study was based on PB regarding the source of controls. 3) Genotype methods included TaqMan, PCR, sequencing and MALDI-TOF. For rs1143634(C>T), it was the same as above, but only two studies were qualified, which contained 996 cases and 620 controls [13, 14]. Finaly, for rs1143627(C>T), just 2413 cases and 2438 controls were involved in five studies [10, 15–17, 19]. Genotype distributions in controls of all studies were in accordance with HWE.

Table I.

Characteristics of case-control studies included in the meta-analysis

SNPs/First author Year Racial descent Country Menopausal state Source of controls Genotype methods Cases Controls
rs16944:
 Smith 2004 Caucasian UK N PB Taqman 141 261
 Hefler 2005 Caucasian Germany N HB PCR 269 227
 Liu 2006 Asian China N HB PCR 365 631
 Balasubramanian 2006 Caucasian UK N HB PCR 703 489
 Pooja 2012 Asian India Pre-menopausal HB Sequence 107 200
 Pooja 2012 Asian India Post-menopausal HB Sequence 93 200
 Gong 2013 Caucasian American Pre-menopausal HB MALDL-TOF 185 163
 Gong 2013 Caucasian American Post-menopausal HB MALDL-TOF 141 146
 Gong 2013 African American Pre-menopausal HB MALDL-TOF 237 195
 Gong 2013 African American Post-menopausal HB MALDL-TOF 213 208
 Zuo 2018 Asian China N HB Sequenom MassARRAY 530 628
rs1143634:
 Snoussi 2005 African Tunisia N HB PCR 305 200
 Hefler 2005 Caucasian Germany N HB PCR 269 227
 Balasubramanian 2006 Caucasian UK N HB PCR 691 420
 Pooja 2012 Asian India Pre-menopausal HB Sequence 107 200
 Pooja 2012 Asian India Post-menopausal HB Sequence 93 200
 Pooja 2012 Asian India N HB Sequence 200 200
rs1143627:
 Ito 2002 Asian Japan N HB PCR-CTPP 227 185
 Liu 2006 Asian China N HB PCR 365 631
 Lee 2006 Asian Korea Pre-menopausal PB PCR-CTPP 353 290
 Lee 2006 Asian Korea Post-menopausal PB PCR-CTPP 206 215
 Lee 2006 Asian Korea N PB PCR-CTPP 559 505
 Akisik 2007 Asian Turkey N N PCR-RFLP 126 110
 Gong 2013 Caucasian American Pre-menopausal HB MALDL-TOF 186 166
 Gong 2013 Caucasian American Post-menopausal HB MALDL-TOF 142 146
 Gong 2013 African American Pre-menopausal HB MALDL-TOF 239 195
 Gong 2013 African American Post-menopausal HB MALDL-TOF 216 210
 Zuo 2018 Asian China N HB Sequenom MassARRAY 530 628
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Table II.

Characteristics of case–control studies included in the meta-analysis

SNPs/First author Genetype distribution HWE test
Cases Controls
C T CC CT TT MAF C T CC CT TT MAF
rs16944:
 Smith 187 95 60 67 14 0.34 309 213 87 135 39 0.41 0.25
 Hefler 362 176 124 114 31 0.33 287 167 88 111 28 0.37 0.61
 Liu 358 372 94 170 101 0.49 699 563 197 305 129 0.45 0.58
 Balasubramanian 972 434 339 294 70 0.31 670 308 232 206 51 0.31 0.61
 Pooja 67 147 12 43 52 0.31 144 256 25 94 81 0.36 0.78
 Pooja 70 116 18 34 41 0.38 144 256 25 94 81 0.36 0.78
 Gong 254 116 88 78 19 0.31 215 111 72 71 20 0.34 0.71
 Gong 160 122 49 62 30 0.22 205 87 71 63 12 0.29 0.71
 Gong 221 253 56 109 72 0.47 178 212 44 90 61 0.46 0.33
 Gong 188 238 34 120 59 0.44 193 223 49 95 64 0.46 0.24
 Zuo 556 504 160 236 134 0.48 628 628 142 344 142 0.5 0.02
rs1143634:
 Snoussi 428 182 157 114 34 0.29 306 94 120 66 14 0.24 0.24
 Hefler 415 123 159 97 13 0.23 337 117 119 99 9 0.26 0.04
 Balasubramanian 1062 320 410 242 39 0.23 629 211 231 167 22 0.25 0.24
 Pooja 178 36 75 28 4 0.17 364 36 176 12 12 0.09 <0.01
 Pooja 160 26 76 18 4 0.13 364 36 176 12 12 0.09 <0.01
 Pooja 340 60 147 46 7 0.15 364 36 176 12 12 0.09 <0.01
rs1143627:
 Ito 219 235 58 103 66 0.48 155 215 28 99 58 0.42 0.18
 Liu 379 351 102 175 88 0.48 579 683 133 313 185 0.46 0.98
 Lee 344 362 96 152 105 0.49 285 295 70 145 75 0.49 0.99
 Lee 209 203 51 107 48 0.58 211 219 43 125 47 0.49 0.02
 Lee 553 565 147 259 153 0.49 496 514 113 270 122 0.49 0.12
 Akisik 99 153 18 63 45 0.29 98 122 21 56 33 0.45 0.75
 Gong 119 253 20 79 87 0.32 113 219 21 71 74 0.34 0.54
 Gong 122 162 30 62 50 0.43 87 205 12 63 71 0.29 0.71
 Gong 282 196 84 114 41 0.41 238 152 76 86 33 0.39 0.31
 Gong 266 166 81 104 31 0.38 246 174 77 92 41 0.41 0.16
 Zuo 510 550 136 238 156 0.48 630 626 144 342 142 0.49 0.03
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Meta-analysis

The critical results of the current meta-analysis are described in Table III. Based on data, three common polymorphisms in IL-1β (rs16944, rs1143634, rs1143627) were not significantly associated with breast cancer risk in all genetic models. To go a step further, the data were stratified into different subgroups according to ethnicity, menopausal state, source of controls and genotyping methods. In terms of analysis by ethnic subgroup, TT genotype of rs16944 polymorphism represented a higher risk of breast cancer compared to CT genotype and CC + TT genotype and this was only significant in Asian people (TT vs. CC + CT: 1.229, 95% CI: 1.063–1.422, p = 0.005, Figure 1; TT vs. CT: 1.211, 95% CI: 1.057–1.388, p = 0.006, Figure 2), not in Caucasian and African populations. Similarly, it is only in Asian people that a significantly decreased breast cancer risk was found in the dominant model (CT + TT vs. CC: OR = 0.944, 95% CI: 0.897–0.994, p = 0.027, Figure 3) for the rs1143627 polymorphism. With patients being stratified according to the menopausal state, we noted that the rs1143627 polymorphism correlated with reduced breast cancer risk among post-menopausal women in three genotype models: the allele model (T vs. C: 0.859, 95% CI: 0.753–0.98, p = 0.024), the recessive model (TT vs. CC + CT: 0.727, 95% CI: 0.576–0.918, p = 0.007, Figure 4), and the homozygous model (TT vs. CC: 0.743, 95% CI: 0.626–0.882, p = 0.001). No significant associations between IL-1β polymorphism and breast cancer risk were found in other stratified analyses based on source of controls and genotyping methods.

Table III.

Summary ORs and 95% CI of interleukin-1β polymorphisms and breast cancer risk

Variable Allele model Dominant model Recessive model Homozygous model Heterozygous model
OR (95% CI) P-valuea OR (95% CI) P-valuea OR (95% CI) P-valuea OR (95% CI) P-valuea OR (95% CI) P-valuea
rs16944:
 Overall 1.017 0.944–1.096 0.653 1.001 0.945–1.06 0.609 1.071 0.915–1.254 0.393 1.036 0.898–1.196 0.626 1.072 0.944–1.218 0.283
Ethnicity:
 Caucasion 0.986 0.837–1.16 0.862 0.97 0.863–1.09 0.813 1.029 0.703–1.507 0.082 0.988 0.657–1.487 0.956 1.039 0.847–1.273 0.714
 Asian 1.071 0.98–1.172 0.13 1.01 0.927–1.101 0.436 1.229 1.063–1.422 0.005 1.083 0.884–1.327 0.443 1.217 1.06–1.396 0.005
 African 1.017 0.927–1.104 0.791 1.043 0.938–1.161 0.979 0.937 0.763–1.15 0.533 1.041 0.889–1.219 0.615 0.901 0.743–1.092 0.289
rs1143634:
 Overall 1.072 0.783–1.466 0.665 1.036 0.777–1.382 0.807 1.27 0.863–1.87 0.226 1.277 0.758–2.151 0.359 1.243 0.86–1796 0.247
rs1143627:
 Overall 0.959 0.903–1.018 0.171 0.956 0.912–1.002 0.058 0.966 0.855–1.092 0.581 0.918 0.823–1.025 0.128 1.017 0.92–1.124 0.747
Ethnicity:
 Caucasion 0.918 0.726–1.159 0.472 0.94 0.792–1.115 0.475 0.879 0.611–1.265 0.488 0.879 0.617–1.252 0.473 0.943 0.779–1.143 0.553
 Asian 0.965 0.899–1.034 0.312 0.944 0.897–0.994 0.027 1.023 0.881–1.187 0.768 0.929 0.812–1.064 0.289 1.084 0.963–1.22 0.181
 African 0.988 0.873–1.118 0.849 1.024 0.924–1.135 0.651 0.866 0.632–1.186 0.37 0.932 0.689–1.259 0.645 0.845 0.639–1.118 0.239
Menopausal:
 Pre-menopausal 1.025 0.958–1.098 0.471 1.005 0.952–1.061 0.867 1.085 0.925–1.272 0.326 1.037 0.928-1.159 0.519 1.08 0.932–1.251 0.308
 Post-menopausal 0.859 0.753–0.98 0.024 0.913 0.787–1.058 0.225 0.728 0.575–0.922 0.008 0.743 0.626-0.882 0.001 0.812 0.653–1.011 0.062
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a

Two-side χ2 test, P < 0.05 was considered statistically significant.

Figure 1.

Figure 1

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OR of breast cancer in different ethnicities associated with rs16944 in IL-1β gene for the TT genotype compared with the CC + CT genotype

Figure 2.

Figure 2

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OR of breast cancer in different ethnicities associated with rs16944 in IL-1β gene for the TT genotype compared with the CT genotype

Figure 3.

Figure 3

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OR of breast cancer in different ethnicities associated with rs1143627 in IL-1β gene for the CT + TT genotype compared with the CC genotype

Figure 4.

Figure 4

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OR of breast cancer in different menopausal state associated with rs1143627 in IL-1β gene for the TT genotype compared with the CC + CT genotype

Publication bias

Egger’s test and Begg’s funnel plot were used so that we could evaluate the potential publication bias of the studied literature. No obvious evidence of publication bias was detected in IL-1β (rs16944, rs1143634, rs1143627) (Figure 5). Also, good results were obtained in the sensitivity analysis (Figure 6).

Figure 5.

Figure 5

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Begg’s funnel plot analysis of publication bias. A – rs16944 in TT vs. CC + CT model. B – rs16944 in TT vs. CT model. C – rs1143627 in CT + TT vs. CC model. D – rs1143627 in TT vs. CC + CT model

Figure 6.

Figure 6

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Sensitivity analysis. A – rs16944 in TT vs. CC + CT model. B – rs16944 in TT vs. CT model. C – rs1143627 in CT + TT vs. CC model. D – rs1143627 in TT vs. CC + CT model

Discussion

It is believed that cytokines are strongly connected to cancer pathogenesis accompanying increasing evidence, which suggests that they participate in tumor initiation, growth and metastasis [2]. Numerous studies about cytokine gene polymorphisms have been conducted to investigate their relations with many inflammatory and neoplastic diseases [14]. Emerging studies have reported the associations between IL-1β polymorphisms and breast cancer risk because of IL-1β’s crucial importance in breast cancer development. The size of samples in a single study was relatively small and the controls of some studies deviated from HWE, so the results were controversial. For example, Ito et al. first reported that rs1143627 was significantly associated with breast cancer risk [10] and another case-control study by Liu et al. verified this conclusion in the Chinese population [16]. However, Lee et al. and Akisik and Dalay did not find significant differences [15, 17]. Studies by Snoussi et al. and Pooja et al. revealed highly significant associations between rs1143634 and the aggressive phenotype of breast cancer [13, 18]. On the other hand, Hefler et al. and Balasubramanian et al. did not find significant differences [12, 14]. For rs16944, some studies indicated that rs16944 genotype reduced the risk of breast cancer, while others showed the opposite results.

Liu et al. conducted a meta-analysis to investigate the relations between three polymorphisms in IL-1β gene and the risk of breast cancer [25]. The variant genotype of rs1143627 was found to be associated with a significantly increased breast cancer risk while the polymorphisms rs16944 and rs1143634 did not represent any associations with breast cancer risk, which was inconsistent with our results. As Wang et al. mentioned [26], the data reported by Liu et al. conflicted with the data from some previous publications, so the results provided by Liu et al. were untrustworthy. Given this situation, we conducted an updated meta-analysis to investigate the associations between three polymorphisms in the IL-1β gene and breast cancer risk.

Some potential limitations in the present meta-analysis should be taken into consideration. First of all, compared to the one included in previous studies, this sample size was larger, but it was still relatively small for some SNPs and stratified analyses. Next, we could not conduct haplotype analysis and linkage disequilibrium, because more details about personal information regarding genotypes of the SNPs (rs16944, rs1143634 and rs1143627) in IL-1β gene were unavailable. Furthermore, it is difficult for us to evaluate potential interactions between gene-environment, gene-gene and multiple polymorphic loci in the same gene. Regardless of these limitations, our present meta-analysis includes a much larger number of eligible studies and a stratified analysis.

In conclusion, our current meta-analysis suggests that the rs16944 polymorphism is significantly associated with increased risk of breast cancer among the Asian population, while the rs1143627 polymorphism reduces the breast cancer risk in post-menopausal women. In the future, we need further large-scale and rigorous studies to validate these findings.

Acknowledgments

We give thanks to Mrs Dandan Chen for providing language help.

This work was supported by the National Natural Science Foundation of China (No.31801171), China Postdoctoral Science Foundation (No. 2019M650102), Wuxi Commission of Health and Family Planning (No. Q201829) and Top Talent Support Program for young and middle-aged people of Wuxi Health Committee (No. HB2020039).

Conflict of interest

The authors declare no conflict of interest.

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