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. 2016 Jul 15;7(39):62954–62965. doi: 10.18632/oncotarget.10617

Two common functional catalase gene polymorphisms (rs1001179 and rs794316) and cancer susceptibility: evidence from 14,942 cancer cases and 43,285 controls

Kang Liu 1,#, Xinghan Liu 1,#, Meng Wang 1,#, Xijing Wang 1, Huafeng Kang 1, Shuai Lin 1, Pengtao Yang 1, Cong Dai 1, Peng Xu 1, Shanli Li 1, Zhijun Dai 1
PMCID: PMC5325339  PMID: 27449288

Abstract

Recent studies have focused on the associations of catalase polymorphisms with various types of cancer, including cervical and prostate cancers. However, the results were inconsistent. To obtain a more reliable conclusion, we evaluated the relationship between the two common catalase gene polymorphisms (rs1001179 and rs794316) and cancer risk by a meta-analysis. Our meta-analysis included 37 published studies involving 14,942 cancer patients and 43,285 cancer-free controls. Odds ratios (ORs) and 95% confidence intervals (CIs) were used to evaluate the cancer risk. The results demonstrated that the rs1001179 polymorphism was associated with an increased cancer risk in the recessive and homozygote models (TT vs. CC: OR = 1.19, P = 0.01; TT vs. CT+CC: OR = 1.19, P <0.001). Furthermore, stratified analyses revealed a significant association between the rs1001179 polymorphism and prostate cancer in all models except the homozygote comparison. An association of the rs794316 polymorphism with cancer risk was detected in two genetic models (TT vs. AA: OR = 1.34, 95% CI = 1.03–1.74, P <0.001; TT vs. AT+AA: OR = 1.39, 95% CI = 1.09–1.77, P = 0.01). Additional well-designed studies with large samples should be performed to validate our results.

Keywords: catalase, polymorphism, cancer, susceptibility, meta-analysis

INTRODUCTION

Worldwide, cancer is currently the main cause of death and a public health problem that seriously threatens human health [1]. Biological and epidemiological studies have shown that carcinogenesis is a sophisticated, multivariate process resulting from interactions between genetic and environmental factors [2]. However, the exact mechanism of carcinogenesis has not been fully elucidated. Many aspects of malignant cancers, including carcinogenesis, aberrant growth, metastasis, and angiogenesis, have been attributed to reactive oxygen species (ROS) [3]. Such ROS-mediated damage to cellular macromolecules is thought to accumulate as a function of age, thus promoting carcinogenesis [4, 5].

Catalase (CAT) is an important endogenous antioxidant enzyme that decomposes hydrogen peroxide to oxygen and water, thus limiting the deleterious effects of ROS[6]; accordingly, the CAT gene may play an important role in substance metabolism. CAT is located on the nuclear chromosome 11p13, and polymorphisms in this gene have been reported to associate with the development of many types of cancer, such as invasive cervical cancer and prostate cancer [7].

The rs1001179 polymorphism (C-262T) is located in the promoter region of CAT, where it influences transcription factor binding and alters the basal transcription and consequent expression of the encoded enzyme [8]. The rs794316 polymorphism (A-15 T) has been identified in the promoter region near the CAT start site, and the endogenous variability of this promoter likely plays a role in the host response to oxidative stress [9]. A large number of previous studies in humans have suggested a possible correlation between genetic polymorphisms of CAT and susceptibility to cancers, such as prostate cancer [10–14], breast cancer [15], and hepatocellular carcinoma [16–19]. However, those studies published inconsistent results. Accordingly, we conducted a meta-analysis to combine data from all of the available case-control studies in order to validate the association of CAT polymorphisms with cancer risk.

RESULTS

Characteristics of included studies

A flow chart of the study selection process is shown in Figure 1. Initially, 374 articles were identified. After reading the titles and abstracts of all the articles, 310 were excluded (164 articles were not related to cancer patients, 137 articles were not case-control studies and 9 articles were about other polymorphisms). After searching through the full texts of the remaining articles, an additional 15 were excluded, including 9 articles that contained no useful data and 6 articles that had re-reported data. Finally, a total of 37 studies from 29 published articles, involving 14,942 cases and 43,285 cancer-free controls, were included in this meta-analysis. The eligible studies presented data for several different cancer types, including prostate cancer, hepatocellular carcinoma, breast cancer, and colorectal cancer. Among these studies, 10 were based on Asian populations [9, 13, 15–17, 20–22], 20 on Caucasian populations [7, 10, 11, 14, 18, 23–33], 1 on an African population [14], and 6 on mixed-ethnicity populations [12, 19, 31, 34–36]. Furthermore, in 3 studies, the genotype distributions of the control groups departed from Hardy-Weinberg equilibrium (HWE) [7, 10, 20]. The characteristics of the eligible studies are presented in Table 1.

Figure 1. Flow diagram of included studies for the meta-analysis.

Figure 1

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CNKI = China National Knowledge Infrastructure.

Table 1. Characteristics of the studies included in the meta-analysis.

First author Year Country Ethnicity Genotyping medthod Source of control Cancer type Total sample size (case/control) HWE SNP
Sousa 2016 Brazil Mixed Taqman hospital HCC 106/139 0.44 rs1001179
Castaldo 2015 Portugal Caucasian PCR population CC 119/106 0.00 rs1001179
Geybels 2015 Netherland Caucasian PCR population PC 1529/25184 0.00 rs1001179
Liu 2015 China Asian PCR-RFLP hospital HCC 266/248 0.68 rs1001179
Saadat 2015 Iran Caucasian PCR population BC 407/395 0.40 rs1001179
Su-1 2015 China Asian PCR-RFLP hospital HCC 301/186 0.49 rs1001179
Su-2 2015 China Asian PCR-RFLP hospital HCC 99/294 0.83 rs1001179
Banescu 2014 Romania Caucasian PCR-RFLP population CML 168/321 0.47 rs1001179
Aynali 2013 Turkey Caucasian PCR-RFLP hospital Laryngeal cancer 25/23 0.13 rs1001179
Tefik 2013 Turkey Caucasian PCR-RFLP population PC 155/195 0.07 rs1001179
Ding 2012 China Asian PCR population PC 1417/1008 0.86 rs1001179
Farawela 2012 Egypt Caucasian PCR-RFLP population NHL 100/100 0.49 rs1001179
Karunasinghe 2012 New Zealand Mixed Taqman population PC 258/567 0.42 rs1001179
Tsai 2012 Taiwan Asian PCR hospital BC 260/224 0.44 rs1001179
Chang 2012 China Asian PCR-RFLP population CRC 880/848 0.00 rs794316
Nahon 2011 France Caucasian Taqman hospital HCC 84/55 0.62 rs1001179
Ezzikouri 2010 France Mixed PCR-RFLP population HCC 96/222 0.59 rs1001179
He-1 2010 USA Caucasian Taqman population BCC 270/796 0.89 rs1001179
He-2 2010 USA Caucasian Taqman population Melanoma 211/796 0.89 rs1001179
He-3 2010 USA Caucasian Taqman population SCC 266/796 0.89 rs1001179
Tang 2010 USA Mixed Taqman population Pancreatic cancer 551/602 0.97 rs1001179
Wu 2010 Taiwan Asian PCR-RFLP hospital OCC 122/122 0.18 rs794316
Funke 2009 Germany Caucasian PCR population CRC 632/605 0.11 rs1001179
Li 2009 USA Caucasian Taqman population BC 497/493 1.00 rs1001179
Quick-1 2008 USA Caucasian HM L/I MS population BC 569/974 0.70 rs1001179
Quick-2 2008 USA Mixed HM L/I MS population BC 47/108 0.22 rs1001179
Rajaraman-1 2008 USA Caucasian Taqman hospital Glioma 330/438 0.57 rs1001179
Rajaraman-2 2008 USA Caucasian Taqman hospital Meningioma 120/438 0.57 rs1001179
Rajaraman-3 2008 USA Caucasian Taqman hospital Acoustic neuroma 63/438 0.57 rs1001179
Choi-1 2007 USA Caucasian HM L/I MS population PC 463/1233 0.26 rs1001179
Choi-2 2007 USA African HM L/I MS population PC 27/120 0.60 rs1001179
Cebrian 2006 UK Caucasian Taqman population BC 2171/2262 0.96 rs1001179
Ho 2006 China Asian PCR-RFLP hospital LC 230/240 0.44 rs1001179
Lightfoot 2006 USA/UK Mixed Taqman population NHL 909/1437 0.96 rs1001179
Ahn 2005 USA Caucasian HM L/I MS population BC 1008/1056 0.93 rs1001179
Lee-1 2002 South Korea Asian PCR-RFLP population GC 80/108 0.47 rs794316
Lee-2 2002 South Korea Asian PCR-RFLP population HCC 106/108 0.47 rs794316
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PCR: polymerase chain reaction; RFLP: restriction fragment length polymorphism; HM L/I MS: high-throughput, matrixassisted, laser desorption/ionization time-of-flight mass spectrometry; HCC: hepatocellular carcinoma; CC: cervical cancer; BC: breast cancer; CML: chronic myeloid leukemia; NHL: non-Hodgkin lymphoma; BCC: basal cell carcinoma; SCC: squamous cell carcinoma; PC: Prostate cancer; CRC: colorectal cancer; OCC: Oral cavity cancer; GC: gastric cancer; LC: lung cancer; SNP: single-nucleotide polymorphisms; HWE: Hardy-Weinberg equilibrium.

Meta-analysis of CAT polymorphisms and cancer risk

As shown in Table 2, the minor allele frequencies varied widely among cancer patients across the eligible studies, ranging from 0.04 to 0.50 for rs1001179 polymorphism and 0.31 to 0.43 for rs794316 polymorphism. The average minor allele frequencies for these polymorphisms were 0.19 and 0.40, respectively.

Table 2. Genotype Distribution and Allele Frequency of CAT polymorphisms in Cases and Controls.

First author Genotype (N) Allele frequency (N) MAF
Case Control Case Control
total AA AB BB total AA AB BB A B A B
rs1001179
Sousa 2016 106 68 35 3 139 103 32 4 171 41 238 40 0.19
Castaldo 2015 119 58 25 36 106 65 27 14 141 97 157 55 0.41
Geybels 2015 1529 887 539 103 25184 15794 8108 1282 2313 745 39696 10672 0.24
Liu 2015 266 239 27 0 248 223 24 1 505 27 470 26 0.05
Saadat 2015 407 261 129 17 395 240 132 23 651 163 612 178 0.20
Su-1 2015 301 273 27 1 186 168 18 0 573 29 354 18 0.05
Su-2 2015 99 92 7 0 294 264 29 1 191 7 557 31 0.04
Banescu 2014 168 105 49 14 321 168 132 21 259 77 468 174 0.23
Aynali 2013 25 13 10 2 23 12 11 0 36 14 35 11 0.28
Tefik 2013 155 58 64 33 195 107 68 20 180 130 282 108 0.42
Ding 2012 1417 1316 99 2 1008 940 67 1 2731 103 1947 69 0.04
Farawela 2012 100 26 49 25 100 28 53 19 101 99 109 91 0.50
Karunasinghe 2012 258 144 99 15 567 350 195 22 387 129 895 239 0.25
Tsai 2012 260 225 35 0 224 202 22 0 485 35 426 22 0.07
Nahon 2011 84 62 21 1 55 32 19 4 145 23 83 27 0.14
Ezzikouri 2010 96 76 14 6 222 173 45 4 166 26 391 53 0.14
He-1 2010 270 161 97 12 796 512 252 32 419 121 1276 316 0.22
He-2 2010 211 129 75 7 796 512 252 32 333 89 1276 316 0.21
He-3 2010 266 160 96 10 796 512 252 32 416 116 1276 316 0.22
Tang 2010 551 349 174 28 602 366 207 29 872 230 939 265 0.21
Funke 2009 632 374 235 23 605 348 231 26 983 281 927 283 0.22
Li 2009 497 295 176 26 493 303 167 23 766 228 773 213 0.23
Quick-1 2008 569 345 197 27 974 598 333 43 887 251 1529 419 0.22
Quick-2 2008 47 34 13 0 108 97 10 1 81 13 204 12 0.14
Rajaraman-1 2008 330 195 124 11 438 251 164 23 514 146 666 210 0.22
Rajaraman-2 2008 120 73 39 8 438 251 164 23 185 55 666 210 0.23
Rajaraman-3 2008 63 43 17 3 438 251 164 23 103 23 666 210 0.18
Choi-1 2007 463 281 157 25 1233 732 445 56 719 207 1909 557 0.22
Choi-2 2007 27 24 3 0 120 109 11 0 51 3 229 11 0.06
Cebrian 2006 2171 1351 707 113 2262 1362 787 113 3409 933 3511 1013 0.21
Ho 2006 230 209 19 2 240 217 23 0 437 23 457 23 0.05
Lightfoot 2006 909 554 298 57 1437 867 498 72 1406 412 2232 642 0.23
Ahn 2005 1008 614 349 45 1056 679 335 42 1577 439 1693 419 0.22
rs794316
Chang 2012 880 280 448 152 848 272 472 104 1008 752 1016 680 0.43
Wu 2010 122 57 55 10 122 62 54 6 169 75 178 66 0.31
Lee-1 2002 80 35 38 7 108 51 44 13 108 52 146 70 0.33
Lee-2 2002 106 51 42 13 108 51 44 13 144 68 146 70 0.32
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A: the major allele; B: the minor allele; MAF: minor allele frequencies.

The main results of this meta-analysis are listed in Table 3. Thirty-three studies involving 13,754 cases and 42,099 controls were included for rs1001179. As shown in Table 3 and Figure 2, we observed an increased cancer risk associated with the rs1001179 polymorphism under the homozygote and recessive models (TT vs. CC: odds ratio [OR] = 1.19, 95% confidence interval [CI] = 1.04-1.37, P = 0.01; TT vs. CT+CC: OR = 1.19, 95% CI = 1.06- 1.34, P < 0.001.) In the cancer-specific analysis, the results showed significant correlations between the rs1001179 polymorphism and prostate cancer risk in different comparison models (T vs. C: OR = 1.21, 95% CI = 1.04-1.41, P = 0.02; TT vs. CC: OR = 1.57, 95% CI = 1.17-2.10, P = 0.00; TT+CT vs. CC: OR = 1.20, 95% CI = 1.01-1.42, P = 0.04; TT vs. CT+CC: OR = 1.40, 95% CI = 1.18-1.67, P < 0.001). However, no meaningful correlations were observed in analyses stratified by ethnicity or the source of controls.

Table 3. Meta-analysis of the association between CAT polymorphisms and cancer risk.

Comparisons OR 95%CI P value Heterogeneity Effects model
I2 P value
B vs A
rs1001179 1.06 0.99-1.13 0.11 54% 0.00 R
HWE 1.04 0.97-1.11 0.28 39% 0.02 R
Caucasian 1.05 0.96-1.14 0.27 66% 0.00 R
Asian 1.05 0.86-1.29 0.64 0% 0.80 F
Mixed 1.10 0.92-1.32 0.29 54% 0.07 R
PC 1.21 1.04-1.41 0.02 61% 0.02 R
HCC 0.85 0.62-1.17 0.32 25% 0.25 F
BC 1.04 0.93-1.17 0.50 52% 0.05 R
rs794316 1.10 0.98-1.24 0.11 0% 0.88 F
HWE 1.06 0.84- 1.35 0.61 0% 0.76 F
BB vs AA
rs1001179 1.20 1.08-1.34 0.00 20% 0.16 F
HWE 1.12 1.00-1.27 0.05 0% 0.70 F
Caucasian 1.16 0.97-1.38 0.10 41% 0.03 R
Asian 1.37 0.37-5.14 0.64 0% 0.80 F
Mixed 1.29 0.98-1.68 0.07 0% 0.47 F
PC 1.57 1.17- 2.10 0.00 33% 0.20 F
HCC 0.88 0.20- 3.82 0.87 45% 0.12 F
BC 1.03 0.85- 1.25 0.75 0% 0.82 F
rs794316 1.34 1.03-1.74 0.00 0% 0.58 F
HWE 1.09 0.62-1.91 0.76 0% 0.52 F
AB vs AA
rs1001179 1.02 0.94- 1.09 0.68 39% 0.01 R
HWE 1.01 0.93- 1.09 0.82 35% 0.03 R
Caucasian 1.01 0.93- 1.11 0.76 47% 0.01 R
Asian 1.03 0.84- 1.28 0.77 0% 0.77 F
Mixed 1.05 0.80- 1.38 0.72 67% 0.02 R
PC 1.14 0.99- 1.31 0.06 33% 0.19 F
HCC 0.81 0.60- 1.09 0.17 0% 0.73 F
BC 1.07 0.91- 1.25 0.43 60% 0.02 R
rs794316 0.97 0.81- 1.16 0.74 0% 0.76 F
HWE 1.10 0.79- 1.52 0.59 0% 0.81 F
BB+AB vs AA
rs1001179 1.04 0.96- 1.12 0.33 48% 0.00 R
HWE 1.02 0.95- 1.11 0.54 39% 0.02 R
Caucasian 1.03 0.94- 1.14 0.50 59% 0.00 R
Asian 1.04 0.84- 1.29 0.70 0 % 0.79 F
Mixed 1.09 0.86- 1.38 0.49 62% 0.03 R
PC 1.20 1.01- 1.42 0.04 55% 0.05 R
HCC 0.83 0.62- 1.11 0.21 0% 0.56 F
BC 1.06 0.91- 1.23 0.44 59% 0.02 R
rs794316 1.04 0.87-1.23 0.68 0% 0.92 F
HWE 1.10 0.80- 1.49 0.57 0% 0.85 F
BB vs AB+AA
rs1001179 1.19 1.06- 1.34 0.00 10% 0.31 F
HWE 1.12 1.00- 1.27 0.05 0% 0.70 F
Caucasian 1.16 0.99- 1.35 0.06 29% 0.11 F
Asian 1.38 0.37- 5.18 0.63 0 % 0.80 F
Mixed 1.30 0.99- 1.70 0.05 0% 0.50 F
PC 1.40 1.18- 1.67 0.00 0% 0.48 F
HCC 0.95 0.23- 3.99 0.94 43% 0.14 F
BC 1.04 0.86- 1.25 0.70 0% 0.89 F
rs794316 1.39 1.09-1.77 0.01 0% 0.41 F
HWE 1.05 0.61- 1.79 0.87 0% 0.46 F
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A: the major allele; B: the minor allele; F: fixed effects mode; R: random effects model; HCC: hepatocellular carcinoma; BC: breast cancer; PC: Prostate cancer; HWE: meta-analysis excluding the studies departing from HWE.

Figure 2. Forest plot of cancer risk related to rs1001179 polymorphism under TT versus CC genetic model.

Figure 2

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T = the minor allele in rs1001179 polymorphism, C = the major allele in rs1001179 polymorphism, CI = confidence interval, OR = odds ratio.

The association of the rs794316 polymorphism with cancer risk was investigated in 4 studies involving 1,188 cases and 1,186 controls. This polymorphism was associated with an increased cancer risk in the overall population under the two models (TT vs. AA: OR = 1.34, 95% CI = 1.03-1.74, P < 0.001; TT vs. AT+AA: OR = 1.39, 95% CI = 1.09-1.77, P = 0.01; Figure 3).

Figure 3. Forest plot of cancer risk related to rs794316 polymorphism under TT versus AA genetic model.

Figure 3

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T = the minor allele in rs794316 polymorphism, A = the major allele in rs794316 polymorphism, CI = confidence interval, OR = odds ratio.

Heterogeneity analysis and publication bias

In this meta-analysis, Q-statistic test was used to detect between-study heterogeneity that arose from methodological or clinical dissimilarity across studies. When the P value of the heterogeneity test was more than 0.1 (P ≥0.1), a fixed-effects model was performed. Otherwise, the random-effects model was used. To explore the other factors which may influence our results, we performed a meta-regression analysis. As shown in the Table 4, sample size was not the factor which could be involved in cancer susceptibility (P = 0.134). Furthermore, the results revealed that the publication year, ethnicity, genotype method and the source of controls were all not the factors that could impact on our results (P = 0.088, 0.368, 0.676 and 0.300, respectively). We also performed a funnel plot and Egger's test to assess publication bias. As shown in Figure 4, the funnel plots failed to reveal any obvious asymmetries of the 2 polymorphisms in the overall population, and the results of Egger's test revealed no publication bias (P > 0.05). Therefore, the results revealed that publication bias was not significant in this meta-analysis.

Table 4. Meta-regression analyses of potential source of heterogeneity.

Heterogeneity factors Coefficient SE Z P 95% CI
LL UL
Sample size 0.047 0.042 1.12 0.273 −0.039 0.134
Publication year 0.026 0.014 1.77 0.088 −0.004 0.056
Ethnicity 0.146 0.159 0.92 0.368 −0.182 0.473
Genotype method −0.023 0.054 −0.42 0.676 −0.135 0.089
Source of control 0.259 0.244 1.06 0.300 −0.244 0.761
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SE: standard error; 95% CI: 95% confidence interval; LL: lower limit; UL: upper limit.

Figure 4. Begg's funnel plot for publication bias test of CAT polymorphisms: rs1001179 (A), rs794316 (B), under the homozygous model.

Figure 4

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Sensitivity analysis

A single study was deleted one at a time from the meta-analysis to reflect the influence of each individual dataset on the pooled ORs. The analysis results demonstrated that no single study greatly influenced the overall cancer risk estimations with respect to the CAT polymorphisms (Figure 5), which indicates that our results are statistically robust.

Figure 5. Sensitivity analysis of the association between CAT rs1001179 polymorphism and cancer risk under the homozygous model.

Figure 5

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DISCUSSION

Previous case-control studies have investigated the association between the rs1001179 polymorphism and cancer risk. No significant associations were observed between rs1001179 polymorphism and hepatocellular carcinoma or breast cancer risk in studies by Liu et al. [17] and Saadat et al. [23], respectively. However, Geybels et al. [10] and Castaldo et al.[7] reported significant associations between rs1001179 polymorphism and increased prostate and cervical cancer risks, respectively, and Nahon et al. [18] and Su et al. [16] demonstrated that rs1001179 polymorphism was a protective factor with respect to hepatocellular carcinoma susceptibility.

We combined all the case-control studies concerning rs1001179 polymorphism and cancer risk to perform this meta-analysis, and found that individuals harboring the rs1001179 TT and rs794316 TT genotypes had a higher cancer risk than did those with other genotypes. This is likely attributable to the relationship between rs1001179 polymorphism and lower CAT activity, which further hinders the response to oxidative stress and might lead to tumorigenesis [37, 38]. The stratified analysis results indicated that the CAT rs1001179 polymorphism was only associated with prostate cancer, but not other cancers. These results were in accordance with others' findings. Geybels et al. observed that the CAT rs1001179 polymorphism was associated with the risk of stage III/IV prostate cancer, which might be explained by the effect of CAT expression on oxidative stress and the link between increased oxidative stress and prostate cancer.

A previous meta-analysis including 9,777 cancer patients and 12,223 controls showed significant association between rs1001179 polymorphism and cancer risk in the recessive model [39]. Compared with that meta-analysis, our meta-analysis included 11 new independent studies of hepatocellular carcinoma [16, 17, 22, 34], chronic myeloid leukemia [24], laryngeal cancer [25], colorectal cancer [20], and oral cavity cancer [9]. Different from the previous result, we observed an association between the rs1001179 polymorphism and an increased cancer risk in the homozygote model. And it is worth mentioning that we found an association of the rs794316 polymorphism with cancer risk in recessive model and homozygote model, which wasn't detected by anyone before.

Because the control group genotype distributions departed from HWE in 3 studies, we performed a subgroup analysis that excluded those studies. Regarding the rs1001179 polymorphism, the result was remained consistent with the overall analysis; in other words, an association between an increased cancer risk and rs1001179 polymorphism was observed in recessive model and homozygote model. Nevertheless, we observed no significant association between the rs794316 polymorphism and cancer risk with any of the genetic models, although this might be a consequence of the small number of studies.

Several limitations of this meta-analysis should be acknowledged. First, only Asian population was involved in the analysis of rs794316, and most studies of rs1001179 are for Caucasian and Asian population. Accordingly, it would be better to include more studies with various ethnic groups to identify their definite roles in different populations. Second, some detailed information (e.g., sex, age, lifestyle, and environmental factors) was not considered. Third, the overall outcomes were based on individual unadjusted ORs, whereas a more precise evaluation should be adjusted using other potentially suspect factors. Fourth, the genotyping methods used in the eligible studies differed widely, which might have influenced the results. Moreover, although we have summarized all data on rs794316 polymorphism and cancer risk, the number of relative studies still needs further expansion.

In summary, this meta-analysis has shown associations of the CAT rs1001179 and rs794316 polymorphisms with an increased cancer risk. Additional larger-scale multicenter studies with larger sample sizes are needed to further validate the possible roles of these polymorphisms in cancers.

MATERIALS AND METHODS

Search strategy

The PubMed, Web of Science, and Chinese National Knowledge Infrastructure (CNKI) databases were searched for publications from 2002 to January 2016 using the terms “cancer” or “tumor”, “CAT” or “Catalase”, “polymorphism” or “SNP”, “rs1001179” or “C-262T”, and “rs794316” or “A-15 T”. We also used the “Related Articles” option in PubMed to identify additional studies of the same topic. The reference lists of the retrieved articles were also screened. All included studies were selected using the following criteria: (a) studies must have featured a case-control design and focused on CAT polymorphism and cancer risk; (b) published data must have been sufficient to allow OR estimation with a 95% CI; and (c) for multiple publications reporting the same data or overlapping data, the largest or most recent publication was selected.

Data extraction

Initially, 2 investigators (Liu K and Liu XH) independently checked all potentially relevant studies, and disagreements were resolved through discussions with a third researcher. We extracted the following items from each article: first author, year of publication, country of origin, ethnicity, cancer types, control source, genotyping method, total numbers of cases and controls, and numbers of different genotypes among cases and controls. All data were extracted from published articles. All cancers were confirmed by histology or pathology. The non-cancer controls had no evidence of any malignant disease at the time of the study.

Statistical analysis

We used ORs and 95% CIs to evaluate the cancer risks associated with CAT polymorphisms. Heterogeneity between studies was evaluated using the I2 test, with a higher I2 value indicating a higher level of heterogeneity (I2 = 75-100%: extreme heterogeneity; I2 = 50-75%: great heterogeneity; I2 = 25-50%: moderate heterogeneity; I2 < 25%: no heterogeneity). During the heterogeneity evaluation, the fixed-effects model would be used if the P value was ≥0.10; otherwise, the random-effects model was used. Subgroup analyses were performed according to cancer type, control source, and ethnicity. A sensitivity analysis was performed to assess the stability of the final results by sequentially omitting each individual study at a time. Egger's test and Begg's test were adopted to assess publication bias. The meta-analysis assessed the following genetic models: dominant model (AB+BB vs. AA), recessive model (BB vs. AA + AB), homozygote comparison (BB vs. AA), heterozygote comparison (AB vs. AA), and allele comparison (B vs. A). All analyses were performed using the Stata software, version 12.0 (Stata Corp., College Station, TX, USA). A P value < 0.5 was considered statistically significant, and all P values were 2-sided.

Footnotes

CONFLICTS OF INTEREST

The authors have no conflicts of interest to declare.

GRANT SUPPORT

This study was supported by the National Natural Science Foundation, China (No. 81471670; 81274136); the China Postdoctoral Science Foundation (No. 2015T81037); the Fundamental Research Funds for the Central Universities, China (No. 2014qngz-04); and the specialized Research Fund of the Second Affiliated Hospital of Xi'an Jiaotong University, China [RC (GG) 201203].

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