Abstract
Background
Accumulating evidence suggests that the rs1800625 polymorphism in RAGE promoter region might be associated with cancer risk; however, data from different studies show conflicting results. Here, a meta-analysis was conducted to evaluate the associations between RAGE rs1800625 polymorphism and cancer risk.
Material/Methods
We searched Embase (Excerpt Medica Database), PubMed, and CNKI (Chinese National Knowledge Infrastructure) databases until March 15, 2019 to identify potential studies for the meta-analysis.
Results
Eighteen eligible studies were included in the current meta-analysis, representing 6246 cases and 6819 controls. Pooled analysis showed positive correlation between the RAGE rs1800625 polymorphism and susceptibility of cancer in recessive genetic model [CC versus TC+TT: odds ratio (OR)=1.397, 95% confidence interval (CI): 1.031–1.894, P=0.031]. Subgroup analysis revealed this association in the Asian, but not Caucasian population, and this correlation was not detected in either breast or lung cancer. Sensitivity analysis indicated unstable results, which should be interpreted with caution. No publication bias was observed.
Conclusions
In conclusion, the RAGE rs1800625 polymorphism was associated with increased overall cancer risk in Asians in recessive genetic model. However, large-scale and well-designed studies in different populations and diverse cancer types are needed for a precise conclusion.
MeSH Keywords: Disease Susceptibility; Meta-Analysis; Polymorphism, Genetic
Background
Receptor for advanced glycation end product (RAGE), also called as advanced glycation end product receptor (AGER), is a transmembrane receptor expressed in a number of cells, belonging to the immunoglobulin superfamily of receptors. Advanced glycation end product (AGE) is a ligand that binds RAGE to amplify immune and inflammatory responses. A number of other ligands of RAGE were reported recently, including amyloid-β, amphoterin, collagen IV, S100 proteins, and integrin Mac-1 [1]. RAGE-ligand interactions are known to elicit oxidative stress, evoked inflammatory, proliferative, angiogenic reactions, and essential processes in the pathogenesis of various types of cancers [2]. Moreover, RAGE was reported to be increased in several solid tumors [3,4].
The RAGE gene is located on chromosome 6p21.3, containing 1.7 kb in the 5′ flanking region and 11 exons ranging 3.27 kb in length. RAGE gene polymorphisms are correlated with the level of circulating RAGE [5]. To date, several RAGE polymorphisms have been identified including rs2070600 (82G>S), rs1800624 (−374 T>A), and rs1800625 (−429 C>T), and were found to be correlated with susceptibility to cancers [6]. The RAGE rs1800625 polymorphism has been widely reported to be correlated with cancer risk, including breast, lung, gastric, cervical, and hepatocellular carcinoma. However, these studies showed controversial results in different types of cancer, or even within the same type of cancer. Some meta-analysis studies summarized this correlation with limited studies and cancer types [7,8]. Yin et al. [7] reported positive correlation between the RAGE rs1800625 polymorphism and lung cancer risk; however, only 2 studies were included. In another meta-analysis by Zhao et al. [8], no remarkable correlation was found in either breast or lung cancer.
The current meta-analysis study pooled 18 eligible case-control studies to evaluate the association between RAGE rs1800625 polymorphism and cancer risk in different ethnic populations and different cancer types.
Material and Methods
Literature search
Embase (Excerpt Medica Database, a biomedical and pharmacological bibliographic database), PubMed, and CNKI (Chinese National Knowledge Infrastructure) databases were searched until March 15, 2019 to explore eligible studies with the keywords “RAGE OR AGER OR receptor for advanced glycation end products” and “polymorphism OR rs1800625 OR −429T>C OR −429A>G OR −429T/C OR −429A/G” and “cancer OR tumor OR carcinoma OR metastasis”. Reference lists were manually examined to explore relevant publications.
Inclusion and exclusion criteria
Inclusion criteria included: 1) case-control study, 2) association between RAGE rs1800625 polymorphism and cancer risk, 3) sufficient genotype information. Exclusion criteria included: 1) reviews, 2) insufficient genotype information, 3) duplicated study, 4) study deviated from Hardy-Weinberg equilibrium (HWE).
Data extraction
To independently carry out meta-analyses, the following data were extracted from all eligible articles: year, first author name, region, sample size, ethnicity, male ratio, age, cancer type, genotyping method, genotype, minor allele frequency (MAF), and P value for HWE.
Statistical analysis
All data were analyzed using STATA 12.0 (STATA Corporation, College Station, TX, USA). The odds ratio (OR) and 95% confidence intervals (CIs) were calculated to determine the correlation between RAGE rs1800625 polymorphism and cancer risk determined with Z test. Four genetic models were applied: allelic (C versus T), dominant (CC+TC versus TT), recessive (CC versus TC+TT), and additive (CC versus TT) genetic models. HWE of the control group was evaluated by χ2 test. I2 statistic and Cochran Q test were applied to examine the heterogeneity, and random effect model was applied in this meta-analysis. Meta regression analysis was used to estimate the risk factors of heterogeneity. Sensitivity analysis was conducted through sequential deletion of a single study. Funnel plot, Begg’s test, and Egger’s test were applied to determine publication bias.
Results
Characteristics of the included 18 case-control studies
The study selection was carried out as shown in Figure 1. A total of 62 studies were screened from the databases. Studies not related to polymorphism (N=8), not related to cancer (N=21), not relevant to rs1800625 polymorphism (N=8), without control (N=1), with insufficient frequency information (N=2), and reviews (N=4) were excluded. Finally, 18 studies with 6246 cases and 6819 controls were included in this meta-analysis [9–26]. The characteristics of the included 18 studies are listed in Tables 1 and 2.
Figure 1.
Flow diagram of literature search and selection of studies.
Table 1.
Characteristics of 18 studies included in this meta-analysis.
| Author | Year | Region | Ethnicity | Cancer | Method | Sample size | Age | ||
|---|---|---|---|---|---|---|---|---|---|
| Case | Control | Case | Control | ||||||
| Hu D et al. | 2019 | Mainland China | Asian | Gastric cancer | PCR-LDR | 369 | 493 | – | – |
| Lee CY et al. | 2018 | Taiwan | Asian | Cervical cancer | TaqMan | 201 | 320 | 48.8±13.5 | 44.0±10.2 |
| Yamaguchi K et al. | 2017 | Japan | Asian | Lung cancer | TaqMan | 189 | 303 | 64.3±11.0 | 55.5±7.8 |
| Li T et al. | 2017 | Mainland China | Asian | Gastric cancer | PCR-RFLP | 200 | 207 | 54.43±11.77 | 53.23±4.34 |
| Wang D et al. | 2017 | Mainland China | Asian | Hepatocellular carcinoma | PCR-LDR | 540 | 540 | 51.5±6.7 | 50.4±6.8 |
| Yue L et al. | 2016 | Mainland China | Asian | Breast cancer | PCR-LDR | 524 | 518 | 53.76±12.62 | 56.49±10.04 |
| Wang H et al. | 2015 | Mainland China | Asian | Lung cancer | PCR-RFLP | 275 | 126 | 59.8±10.4 | 57.1±11.2 |
| Su SC et al. | 2015 | Taiwan | Asian | Hepatocellular carcinoma | TaqMan | 265 | 300 | 62.99±11.97 | 62.75±10.33 |
| Su S | 2015 | Taiwan | Asian | Oral squamous cell carcinoma | TaqMan | 618 | 592 | – | – |
| Chocholatý M et al. | 2015 | Czech Republic | Caucasian | Renal cell carcinoma | PCR-RFLP | 214 | 154 | 63±11 | 57±10 |
| Pan H et al. | 2014 | Mainland China | Asian | Breast cancer | PCR-LDR | 509 | 504 | 55.63±10.14 | 56.27±9.29 |
| Pan H et al. | 2013 | Mainland China | Asian | Lung cancer | PCR-LDR | 819 | 803 | 57.35±10.51 | 57.04±9.72 |
| Wang X et al. | 2012 | Mainland China | Asian | Lung cancer | PCR-RFLP | 562 | 764 | – | – |
| Xu Q et al. | 2012 | Mainland China | Asian | Cervical cancer | TaqMan | 488 | 715 | 54.6±5.7 | 54.5±2.61 |
| Hashemi M et al. | 2012 | Iran | Caucasian | Breast cancer | ARMS-PCR | 71 | 93 | 45.25±11.75 | 43.25±12.97 |
| Krechler T et al. | 2010 | Czech Republic | Caucasian | Pancreas cancer | PCR-RFLP | 99 | 154 | 64±11 | 57±10 |
| Tesarová P et al. | 2007 | Czech Republic | Caucasian | Breast cancer | PCR-RFLP | 120 | 92 | 61.2±11.9 | 56.2±9.2 |
| Tóth EK et al. | 2007 | Hungary | Caucasian | Colorectal cancer | PCR-RFLP | 183 | 141 | 65.7±10.5 | 68.4±6.6 |
PCR-RFLP – polymerase chain reaction-restriction fragment length polymorphism; PCR-LDR – polymerase chain reaction-ligase detection reaction; ARMS-PCR – amplification refractory mutation system-polymerase chain reaction.
Table 2.
Genotype frequencies of RAGE rs1800625 in 18 studies included in this meta-analysis.
| Author | Year | Ethnicity | Cancer | Sample size | Genotype (case) | Genotype (control) | MAF | HWE | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Case | Control | TT | TC | CC | TT | TC | CC | Case | Control | |||||
| Hu D et al. | 2019 | Asian | Gastric cancer | 369 | 493 | 324 | 44 | 1 | 410 | 77 | 6 | 6.23% | 9.03% | 0.277 |
| Lee CY et al. | 2018 | Asian | Cervical Cancer | 201 | 320 | 181 | 19 | 1 | 270 | 48 | 2 | 5.22% | 8.13% | 0.932 |
| Yamaguchi K et al. | 2017 | Asian | Lung cancer | 189 | 303 | 160 | 24 | 5 | 254 | 44 | 5 | 8.99% | 8.91% | 0.066 |
| Li T et al. | 2017 | Asian | Gastric cancer | 200 | 207 | 184 | 13 | 3 | 184 | 22 | 1 | 4.75% | 5.80% | 0.698 |
| Wang D et al. | 2017 | Asian | Hepatocellular carcinoma | 540 | 540 | 403 | 107 | 30 | 417 | 113 | 10 | 15.46% | 12.31% | 0.471 |
| Yue L et al. | 2016 | Asian | Breast cancer | 524 | 518 | 330 | 174 | 20 | 360 | 143 | 15 | 20.42% | 16.70% | 0.861 |
| Wang H et al. | 2015 | Asian | Lung cancer | 275 | 126 | 195 | 76 | 4 | 100 | 26 | 0 | 15.27% | 10.32% | 0.197 |
| Su SC et al. | 2015 | Asian | Hepatocellular carcinoma | 265 | 300 | 216 | 44 | 5 | 277 | 22 | 1 | 10.19% | 4.00% | 0.434 |
| Su S et al. | 2015 | Asian | Oral squamous cell carcinoma | 618 | 592 | 509 | 102 | 7 | 532 | 57 | 3 | 9.39% | 5.32% | 0.280 |
| Chocholatý M et al. | 2015 | Caucasian | Renal cell carcinoma | 214 | 154 | 142 | 57 | 15 | 109 | 39 | 6 | 20.33% | 16.56% | 0.300 |
| Pan H et al. | 2014 | Asian | Breast cancer | 509 | 504 | 379 | 124 | 6 | 365 | 130 | 9 | 13.36% | 14.68% | 0.507 |
| Pan H et al. | 2013 | Asian | Lung cancer | 819 | 803 | 447 | 303 | 69 | 485 | 289 | 29 | 26.92% | 21.61% | 0.077 |
| Wang X et al. | 2012 | Asian | Lung cancer | 562 | 764 | 201 | 274 | 87 | 229 | 387 | 148 | 39.86% | 44.70% | 0.496 |
| Xu Q et al. | 2012 | Asian | Cervical cancer | 488 | 715 | 129 | 188 | 171 | 182 | 344 | 189 | 54.30% | 50.49% | 0.314 |
| Hashemi M et al. | 2012 | Caucasian | Breast cancer | 71 | 93 | 59 | 11 | 1 | 85 | 8 | 0 | 9.15% | 4.30% | 0.665 |
| Krechler T et al. | 2010 | Caucasian | Pancreas cancer | 99 | 154 | 71 | 26 | 2 | 109 | 39 | 6 | 15.15% | 16.56% | 0.300 |
| Tesarová P et al. | 2007 | Caucasian | Breast cancer | 120 | 92 | 85 | 32 | 3 | 63 | 26 | 3 | 15.83% | 17.39% | 0.875 |
| Tóth EK et al. | 2007 | Caucasian | Colorectal cancer | 183 | 141 | 4 | 44 | 135 | 5 | 35 | 101 | 85.79% | 84.04% | 0.376 |
Association of the RAGE rs1800625 polymorphism and cancer risk
In the overall analysis, the RAGE rs1800625 polymorphism was correlated with increased cancer risk in the recessive genetic model (CC versus TC+TT: OR=1.397, 95% CI: 1.031–1.894, P=0.031), but not in the allelic (C versus T), dominant (CC+TC versus TT), or additive (CC versus TT) genetic models (Figure 2, Table 3).
Figure 2.
Forest plots for meta-analysis of the RAGE rs1800625 polymorphism and cancer risk.
Table 3.
Meta-analysis of RAGE rs1800625 polymorphism and cancer susceptibility.
| Genetic model | PQ | I2 | OR | 95% CI | PZ |
|---|---|---|---|---|---|
| Overall | |||||
| C vs. T | 0.000 | 74.8% | 1.139 | 0.982, 1.321 | 0.085 |
| CC+TC vs. TT | 0.000 | 69.6% | 1.105 | 0.936, 1.305 | 0.240 |
| CC vs. TC+TT | 0.002 | 56.4% | 1.397 | 1.031, 1.894 | 0.031 |
| CC vs. TT | 0.001 | 59.8% | 1.423 | 0.996, 2.033 | 0.053 |
| Ethnicity | |||||
| Asian | |||||
| C vs. T | 0.000 | 81.0% | 1.139 | 0.956, 1.357 | 0.146 |
| CC+TC vs. TT | 0.000 | 77.2% | 1.090 | 0.898, 1.324 | 0.384 |
| CC vs. TC+TT | 0.000 | 66.6% | 1.491 | 1.018, 2.183 | 0.040 |
| CC vs. TT | 0.000 | 69.4% | 1.465 | 0.960, 2.236 | 0.077 |
| Caucasian | |||||
| C vs. T | 0.373 | 5.8% | 1.128 | 0.901, 1.412 | 0.294 |
| CC+TC vs. TT | 0.532 | 0.0% | 1.141 | 0.862, 1.511 | 0.355 |
| CC vs. TC+TT | 0.600 | 0.0% | 1.156 | 0.770, 1.736 | 0.485 |
| CC vs. TT | 0.562 | 0.0% | 1.354 | 0.715, 2.565 | 0.353 |
| Disease | |||||
| Lung cancer | |||||
| C vs. T | 0.000 | 85.7% | 1.125 | 0.807, 1.567 | 0.487 |
| CC+TC vs. TT | 0.004 | 77.3% | 1.075 | 0.771, 1.498 | 0.671 |
| CC vs. TC+TT | 0.000 | 84.9% | 1.523 | 0.631, 3.679 | 0.350 |
| CC vs. TT | 0.000 | 87.4% | 1.521 | 0.561, 4.128 | 0.410 |
| Breast | |||||
| C vs. T | 0.062 | 59.1% | 1.105 | 0.827, 1.477 | 0.500 |
| CC+TC vs. TT | 0.087 | 54.4% | 1.127 | 0.828, 1.533 | 0.448 |
| CC vs. TC+TT | 0.561 | 0.0% | 1.075 | 0.633, 1.826 | 0.789 |
| CC vs. TT | 0.463 | 0.0% | 1.126 | 0.661, 1.920 | 0.662 |
Cochran Q test and I2 statistical test were applied to examine the heterogeneity, and random effect model was applied in this meta-analysis. The correlation between RAGE rs1800625 polymorphism and cancer risk was determined using Z test.
Stratification based on ethnicity revealed similar results in Asian but not in the Caucasian population. Moreover, stratification by cancer type did not find any significant correlation in either breast or lung cancer (Table 3).
Meta-regression analysis was carried out to screen risk factors of the heterogeneity considering publication year, ethnicity (Asian versus Caucasian), and genotyping method [polymerase chain reaction (PCR)-restriction fragment length polymorphism (RFLP), PCR-ligase detection reaction (LDR), and amplification refractory mutation system (ARMS)-PCR, versus TaqMan] as possible covariates. However, none of these mentioned covariates remarkably contributed to the heterogeneity (data not shown).
Sensitivity analysis
Sensitivity analysis indicated that the positive correlation found in recessive genetic model in pooled analysis and in Asian subgroup was unstable (Figure 3). After omitting the studies by Wang et al. (2017), Pan et al. (2013), or Xu et al. (2012), the RAGE rs1800625 polymorphism was not correlated with cancer risk in recessive genetic model.
Figure 3.
Sensitivity analysis for meta-analysis of the RAGE rs1800625 polymorphism and cancer risk.
Publication bias
Egger’s and Begg’s tests were applied to determine publication bias, and no publication bias existed (Figure 4, Table 4), indicating that this meta-analysis was reliable.
Figure 4.
Funnel plots of the associations between the RAGE rs1800625 polymorphism and cancer risk.
Table 4.
Publication bias analysis of this meta-analysis.
| Genetic model | Test | t | 95% CI | P |
|---|---|---|---|---|
| C vs. T | Begg’s test | 0.880 | ||
| Egger’s test | 0.37 | −1.858, 2.634 | 0.719 | |
| CC+TC vs. TT | Begg’s test | 0.880 | ||
| Egger’s test | 0.32 | −1.916, 2.588 | 0.756 | |
| CC vs. TC+TT | Begg’s test | 0.940 | ||
| Egger’s test | 0.54 | −0.879, 1.483 | 0.595 | |
| CC vs. TT | Begg’s test | 0.940 | ||
| Egger’s test | 0.86 | −0.749, 1.765 | 0.404 |
Discussion
The objective of this meta-analysis was to investigate any possible relationship of the RAGE rs1800625 polymorphism with cancer susceptibility. We found that the RAGE rs1800625 polymorphism might be closely associated with increased risk of human cancer in the Asian population. However, subgroup analysis did not support this positive correlation in either lung or breast cancer in Asians. Sensitivity analysis revealed unstable results, and therefore, these conclusions should be interpreted with caution.
Heterogeneity represents a major problem in meta-analyses. Herein, we performed stratified analysis by cancer type and ethnicity. Decreased heterogeneity was observed in Caucasian population in all 4 genetic models, and in breast cancer in some genetic models. These results suggest that ethnicity and cancer type may partially explain the source of heterogeneity, although we failed to confirm our hypothesis with statistical evidence in the meta-regression analysis considering ethnicity, publication year, and genotyping method as possible covariates. Moreover, even in the same subgroup of lung cancer, Wang et al. [16] and Pan et al. [19] both recruited squamous cell cancer, small cell cancer, and adenocarcinoma. Wang et al. [21] only studied non-small cell lung cancer (NSCLC) and Yamaguchi et al. [11] only focused on adenocarcinoma. These studies might contribute to the existence of heterogeneity.
Different cancer types might affect the overall result. In the current meta-analysis, gastric, cervical, lung, breast, hepatocellular carcinoma, pancreas, and colorectal cancers were included. However, only breast and lung cancers were included in 4 different studies, and gastric cancer, cervical cancer, and hepatocellular carcinoma were included in 2 studies. Stratified analysis based on cancer type was only performed for lung and breast cancer. Male ratio in different cancers might also influence the results. Among the included 18 studies, 6 studies focused on breast or cervical cancer [10,14,20,22,24,26], which did not include male patients. In the studies by Yamaguchi et al. [11], Li et al. [13], Chocholatý et al. [18], Krechler et al. [23], and Tóth et al. [25] involving lung, gastric, renal, pancreas, and colorectal cancers respectively, the male ratios were not consistent between cases and controls. Moreover, the sample size among these included studies varied from less than 100 to more than 800. In the stratified analysis of breast cancer, studies by Hashemi et al. [22] and Tesarová et al. [24] involved less than 100 controls, and both studies showed no significant association, which might affect the overall OR of the subgroup. The mean age between cases and controls were not well matched in some studies. In the study by Yamaguchi et al. [11], the mean age of cases was 64.3±11.0, while the mean age of controls was 55.5±7.8, and similar results were found in the studies by Krechler et al. [23] and Tesarová et al. [24]. The MAF varied significantly among studies, even in the same ethnic populations. In Asian population, the MAF varied from 4.00% to 50.49% [15,20], while in the Caucasian population, it varied from 4.30% to 84.04% [22,25]. Finally, the genotyping methods might also contribute to the overall result. PCR-LDR, TaqMan, PCR-RFLP, and ARMS-PCR were used by different studies. These factors together might make the overall heterogeneity complicated and influence the pooled result. Rigorously designed studies with larger sample size might help clarify this association between RAGE rs1800625 polymorphism and cancer risk.
Several potential limitations existed in the current meta-analysis. First, selection bias might exist, as eligible articles in English language were screened. In this meta-analysis, only 5 articles were included for the Caucasian population, and this bias might influence the null result for Caucasian population. Second, we only performed stratified analysis for lung and breast cancers but not all types of cancer, due to limited number of studies. Third, not all published studies on the correlation between the RAGE rs1800625 polymorphism and susceptibility of cancer were included. Studies by Zhang et al. [27] and Kádár et al. [28] were ruled out due to insufficient genotype information for the calculation of OR. Fourth, this meta-analysis was not adjusted by gender, age, and environment factors like circulating soluble RAGE. Breast cancer was gender specific and was not suitable for comparison with other types of cancer. Fifth, only about 28% of the studies included Caucasian population; therefore, it is not surprising that stratification analysis showed similar results in Asian, but not Caucasian population. The Caucasian population is not representative and therefore it is hard to extrapolate the result to the general population. Sixth, there were significant age differences between case and control groups in some studies and no adjustment was made in our analysis to account for this.
Conclusions
The RAGE rs1800625 polymorphism was associated with increased overall cancer risk in Asians in a recessive genetic model. However, this polymorphism might not be correlated with lung or breast cancer risk in Asians. Nonetheless, large-scale and well-designed studies in different populations and diverse cancer types are needed for a precise conclusion.
Footnotes
Source of support: Departmental sources
Conflict of interest
None.
References
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