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International Journal of Clinical and Experimental Medicine logoLink to International Journal of Clinical and Experimental Medicine
. 2015 Aug 15;8(8):12661–12673.

Peroxisome proliferator-activated receptor gamma (PPARG) rs1801282 C>G polymorphism is associated with cancer susceptibility in asians: an updated meta-analysis

Yafeng Wang 1,*, Yu Chen 2,*, Heping Jiang 3,*, Weifeng Tang 4, Mingqiang Kang 4, Tianyun Liu 5, Zengqing Guo 2, Zhiqiang Ma 6
PMCID: PMC4612865  PMID: 26550180

Abstract

Peroxisome proliferator-activated receptor gamma (PPARG) is related to inflammation and plays an important role in the development of cancer. PPARG rs1801282 C>G polymorphism might influence the risk of cancer by regulating production of PPARG gene. Hence, a comprehensive meta-analysis was conducted to explore the association of PPARG rs1801282 C>G polymorphism with cancer susceptibility. An extensive search of PubMed and Embase databases for all relevant publications was carried out. A total of 38 publications with 16,844 cancer cases and 23,736 controls for PPARG rs1801282 C>G polymorphism were recruited in our study. Our results indicated that PPARG rs1801282 C>G variants were associated with an increased cancer risk in Asian populations and gastric cancer. In summary, the findings suggest that PPARG rs1801282 C>G polymorphism may play a crucial role in malignant transformation and the development of cancer.

Keywords: Cancer, polymorphism, peroxisome proliferator-activated receptor gamma, meta-analysi

Introduction

With the dramatic increase of the incidence of cancer and cancer-relative mortality, cancer has become one of the major public health burdens. For this reason, novel cancer biomarkers are needed urgently for prevention and early detection of malignance. Carcinogenesis is a very complicated process and has not been fully understood. It is believed that the development of cancer is influenced by susceptibility genes and environmental factors.

Peroxisome proliferator-activated receptor gamma (PPARG), a type of nuclear hormone receptor, acts as an important transcriptional regulator of cellular differentiation, carbohydrate and lipid metabolism [1]. PPARG also owns certain anti-inflammatory properties [2,3]. Activation of PPARG reduces the production of multiple cytokines (e.g., interleukin (IL)-6, IL-8, and tumor necrosis factor-alpha) by antagonizing the role of the signal transducer and activator of transcription, nuclear factor kappa-B and transcription factors activator protein 1, which suppresses induction of the inflammatory response [4,5]. Since PPARG has been supported to take part in cell growth and differentiation, it has been hypothesized that the disorder of PPARG contributes to malignant transformation and the development of cancer. The PPARG rs1801282 C>G polymorphism, a SNP in exon 2 of PPARG, encodes a proline→alanine substitution at amino acid residue 12 (Pro12Ala). This mutation reduces the transcription of PPARγ2 [3]. The PPARG rs1801282 C>G polymorphism has been extensively investigated and was found to be correlated with the risk of cardiovascular diseases and type 2 diabetes [6-9]. Furthermore, the evidence is mounting that PPARG rs1801282 C>G polymorphism might affect individual susceptibility to certain types of malignancy (e.g., gastric cancer, pancreatic cancer, breast cancer and colorectal cancer) [10-14].

Recently, the association between this polymorphism in PPARG gene and cancer risk was extensively examined. A meta-analysis supported that PPARG rs1801282 C>G polymorphism was associated with the increased risk of gastric cancer, but this polymorphism was not correlated with overall cancer risk [15]. Up to now, 43 publications focus on the correlation of the PPARG rs1801282 C>G polymorphism with cancer risk, and the observed results remain conflicting. In the present study, we harnessed an updated meta-analysis on the eligible studies to further investigate the association of PPARG rs1801282 C>G polymorphism with the risk of cancer.

Materials and methods

Search strategy

Eligible publications were extracted by exhaustively electronic search of PubMed and Embase databases using the following terms: (Peroxisome proliferator activated receptor gamma or PPARγ or PPARG) and (polymorphism or SNP or mutation or variant) and (cancer or carcinoma or malignance). References of retrieved studies, comments, meta-analyses, reviews and letters were manually searched for additional publications. There was no limitation of language and the last research was performed on July 15, 2014.

Inclusion and exclusion criteria

Inclusion criteria were defined as follows: (a) The publications assessed the association of PPARG rs1801282 C>G polymorphism with cancer risk; (b) The studies designed as a case-control or cohort study; (c) The sufficient data could be extracted to calculate an odds ratio (OR) with its 95% CI; (d) In these articles, the genotype distributions among controls were consistent with Hardy-Weinberg equilibrium (HWE). The major exclusion criteria were: (a) not a case-control or cohort study; (b) overlapping data; (c) comments, letters, reviews, animal studies and editorials; (d) cancer prognosis and treatment. In certain publications, the data were reported on different subgroups; we treated them as separate studies.

Data extraction

From each eligible study, data were extracted independently by three authors (Y. Wang, Y. Chen and H. Jiang). The following terms were collected: the surname of first author, year of publication, country, numbers of subjects and genotype frequencies of cases and controls, cancer type, ethnicity, genotyping method, and evidence of HWE in controls. If there were any discrepancies, they were resolved following a discussion between all reviewers.

Statistical analysis

HWE in controls was tested by a web-based Pearson’s χ2 test (http://ihg.gsf.de/cgi-bin/hw/hwa1.pl). We used crude ORs with corresponding 95% CIs as an assessment of the association between PPARG rs1801282 C>G polymorphism with cancer risk. A P<0.05 was considered significant. Heterogeneities were assessed using Cochran’s Q-statistic and I2 test. When I2>50% or P<0.10, there was significant heterogeneity, then the random-effects model was applied [16], otherwise, the fixed-effects model was used [17]. Subgroup analyses were conducted by ethnicity and cancer type. Sensitivity analysis was performed by nonparametric “trim-and-fill” method. The Begg’s test and Egger’s test were both used to determine the evidence of publication bias [18]. For publication bias test, statistical significance was defined as P<0.1. In our study, all the statistical analyses were conducted with Stata 12.0 software (StataCorp LP, College Station, TX) and P values were two-sided.

Results

Characteristics of studies

As shown in Figure 1, a total of 1101 publications were retrieved. According to the inclusion criteria and exclusion criteria, there were 38 publications (including 51 individual studies) on the PPARG rs1801282 C>G polymorphism [10,11,13,14,19-52]. Among them, fifteen investigated colorectal cancer [13,14,19-28], seven investigated breast cancer [12,29-33,35], five investigated ovarian cancer [36,37], five investigated gastric cancer [10,38-41], four investigated lung cancer [42-45], four investigated prostate cancer [37,46-48], two investigated pancreatic cancer [11,49], two investigated melanoma [50] and two investigated glioblastoma [51]. Other articles investigated skin cancer [52], endometrial cancer [37], bladder cancer [37], cervical cancer [37] and renal cell carcinoma [37]. Among these, 28 were from Caucasians, 12 were from Asians and 11 were from mixed populations. The characteristics are summarized in Table 1. The genotype distributions are listed in Table 2.

Figure 1.

Figure 1

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Flow diagram of included and excluded process.

Table 1.

Characteristics of all included studies in the meta-analysis

study Publication year Ethnicity Country Cancer type Sample size (case/control) Genotype method
Kopp et al. 2013 Caucasians Denmark prostate cancer 370/370 RT-PCR
Martinez-Nava et al. 2013 mixed Mexico breast cancer 208/220 RT-PCR
Canbay et al. 2012 Caucasians Turkey gastric cancer 86/129 PCR-RFLP
Crous-Bou et al. 2012 Caucasians Israel colorectal cancer 1780/1864 Illumina Beadstation and BeadExpress
Petersen et al. 2012 Caucasians Denmark breast cancer 798/798 TaqMan
Abuli et al. 2011 Caucasians Spain colorectal cancer 515/502 MALDI-TOF MS
Tang et al. 2011 mixed USA pancreatic cancer 1070/1175 TaqMan
Lim et al. 2011 Asians Singapore lung cancer 298/718 RT-PCR
Wu et al. 2011 Asians China breast cancer 291/589 RT-PCR
Bazargani et al. 2010 Caucasians Iran gastric cancer 79/152 PCR–RFLP
Pinheiro et al. 2010 Caucasians UK ovarian cancer 233/663 Taqman
Pinheiro et al. 2010 Caucasians UK ovarian cancer 1120/1160 Taqman
Tsilidis et al. 2009 mixed USA colorectal cancer 208/381 Taqman
Fesinmeyer et al. 2009 mixed USA pancreatic cancer 83/166 TaqMan
Wang et al. 2009 mixed USA prostate cancer 258/258 TaqMan
Kury et al. 2008 Caucasians France colorectal cancer 1023/1121 TaqMan
Prasad et al. 2008 Asians India gastric cancer 62/286 PCR-RFLP
Tahara et al. 2008 Asians Japan gastric cancer 215/201 PCR-RFLP
Vogel et al. 2008 Caucasians Denmark lung cancer 403/744 TaqMan
Justenhoven et al. 2008 Caucasians German breast cancer 688/724 MALDI-TOF MS
Gallicchio et al. 2007 Caucasians USA breast cancer 61/933 TaqMan
Wang et al. 2007 Caucasians USA breast cancer 488/488 TaqMan
Mossner et al. 2007 Caucasians German melanoma 335/355 PCR-RFLP
Mossner et al. 2007 Caucasians German melanoma 497/435 PCR-RFLP
Vogel et al. 2007 Caucasians Denmark colorectal cancer 355/753 TaqMan
Zhang et al. 2007 Asians China lung cancer 45/45 DNA sequence
Vogel et al. 2007 Caucasians Denmark skin cancer 304/315 TaqMan
Kuriki et al. 2006 Asians Japan colorectal cancer 128/238 PCR-CTPP, PCR-RFLP
Theodoropoulos et al. 2006 Caucasians Greece colorectal cancer 222/200 PCR-RFLP
Liao et al. 2006 Asians China gastric cancer 104/104 PCR-RFLP
Siezen et al. 2006 Caucasians The netherlands colorectal cancer 204/399 DNA sequence
Siezen et al. 2006 Caucasians The netherlands colorectal cancer 487/750 DNA sequence
Slattery et al. 2005 mixed USA colorectal cancer 2371/2972 TaqMan
McGreavey et al. 2005 Caucasians UK colorectal cancer 478/733 TaqMan
Jiang et al. 2005 Asians India colorectal cancer 59/291 PCR-RFLP
Jiang et al. 2005 Asians India colorectal cancer 242/291 PCR-RFLP
Campa et al. 2004 Caucasians Norway lung cancer 250/214 TaqMan
Landi et al. 2003 Caucasians Spain colorectal cancer 139/326 TaqMan
Landi et al. 2003 Caucasians Spain colorectal cancer 238/326 TaqMan
Paltoo et al. 2003 Caucasians Finland prostate cancer 193/188 MALDI-TOF
Memisoglu et al. 2002 mixed USA breast cancer 725/953 PCR-RFLP
Smith et al. 2001 Caucasians UK Renal cell carcinoma 40/62 DGGE
Smith et al. 2001 Caucasians UK ovarian cancer 31/62 DGGE
Smith et al. 2001 Asians Japan ovarian cancer 28/215 DGGE
Smith et al. 2001 Asians Japan cervical cancer 20/215 DGGE
Smith et al. 2001 Asians Japan bladder cancer 31/215 DGGE
Smith et al. 2001 mixed USA ovarian cancer 26/80 DGGE
Smith et al. 2001 mixed USA endometrial cancer 69/80 DGGE
Smith et al. 2001 mixed USA prostate cancer 38/80 DGGE
Zhou et al. 2000 mixed USA glioblastoma 52/80 PCR
Zhou et al. 2000 Caucasians German glioblastoma 44/60 PCR
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RT-PCR: reverse transcription-polymerase chain reaction. MALDI-TOF MS: Matrix-Assisted Laser Desorption/Ionization Time of Flight Mass Spectrometry. PCR-RFLP: polymerase chain reaction-restriction fragment length polymorphism. PCR-CTPP: polymerase chain reaction with confronting two-pair primers. DGGE: denaturing gradient gel electrophoresis.

Table 2.

Distribution of PPARG rs1801282 C>G polymorphism genotype and allele among cases and controls

Study Publication year Case Control Case Control HWE
CC CG GG CC CG GG G C G C
Kopp et al. 2013 241 90 3 245 87 2 96 572 91 577 0.050905
Martı´nez-Nava et al. 2013 165 43 0 169 49 2 43 373 53 387 0.448105
Canbay et al. 2012 68 14 4 116 12 1 22 150 14 244 0.287345
Crous-Bou et al. 2012 710 102 0 1307 163 9 102 1522 181 2777 0.117069
Petersen et al. 2012 616 167 15 569 209 20 197 1399 249 1347 0.87691
Abuli et al. 2011 426 87 2 419 80 3 91 939 86 918 0.697001
Tang et al. 2011 826 216 10 871 236 23 236 1868 282 1978 0.140851
Lim et al. 2011 274 23 1 653 64 1 25 571 66 1370 0.660099
Wu et al. 2011 260 29 0 546 40 0 29 549 40 1132 0.392337
Bazargani et al. 2010 60 18 1 134 17 1 20 138 19 285 0.573866
Pinheiro et al. 2010 166 56 2 487 144 13 60 388 170 1118 0.540142
Pinheiro et al. 2010 831 228 16 882 241 13 260 1890 267 2005 0.441786
Tsilidis et al. 2009 165 37 1 295 68 6 39 367 80 658 0.370123
Fesinmeyer et al. 2009 60 22 1 139 27 0 24 142 27 305 0.254053
Wang et al. 2009 198 57 0 189 58 7 57 453 72 436 0.327667
Kury et al. 2008 822 194 7 896 212 13 208 1838 238 2004 0.9079
Prasad et al. 2008 39 18 5 214 67 5 28 96 77 495 0.926116
Tahara et al. 2008 194 21 0 193 8 0 21 409 8 394 0.773449
Vogel et al. 2008 301 93 9 544 187 13 111 695 213 1275 0.502205
Justenhoven et al. 2008 452 135 6 462 145 15 147 1039 175 1069 0.372101
Gallicchio et al. 2007 48 7 1 689 188 18 9 103 224 1566 0.223793
Wang et al. 2007 376 87 15 375 98 5 117 839 108 848 0.615475
Mossner et al. 2007 239 84 11 258 86 7 106 562 100 602 0.957311
Mossner et al. 2007 372 115 7 324 102 6 129 859 114 750 0.522918
Vogel et al. 2007 252 96 7 550 190 13 110 600 216 1290 0.460144
Zhang et al. 2007 39 6 0 41 4 0 6 84 4 86 0.755033
Vogel et al. 2007 220 83 1 232 77 6 85 523 89 541 0.894139
Kuriki et al. 2006 120 7 0 221 17 0 7 247 17 459 0.567742
Theodoropoulos et al. 2006 164 48 10 118 70 12 68 376 94 306 0.707193
Liao et al. 2006 84 17 3 95 9 0 23 185 9 199 0.644642
Siezen et al. 2006 160 40 1 325 70 2 42 360 74 720 0.389723
Siezen et al. 2006 387 92 8 596 146 8 108 866 162 1338 0.723797
Slattery et al. 2005 1840 496 35 2283 645 44 566 4176 733 5211 0.839204
McGreavey et al. 2005 366 80 9 403 100 10 98 812 120 906 0.202319
Jiang et al. 2005 46 13 0 230 57 4 13 105 65 517 0.768946
Jiang et al. 2005 194 44 4 230 57 4 52 432 65 517 0.768946
Campa et al. 2004 2 52 192 4 47 161 436 56 369 55 0.792322
Landi et al. 2003 111 15 3 243 61 5 21 237 71 547 0.60618
Landi et al. 2003 200 31 0 243 61 5 31 431 71 547 0.60618
Paltoo et al. 2003 121 64 8 128 54 6 80 306 66 310 0.916738
Memisoglu et al. 2002 563 148 14 752 190 11 176 1274 212 1694 0.795703
Smith et al. 2001 37 3 0 49 11 2 3 77 15 109 0.191855
Smith et al. 2001 27 4 0 49 11 2 4 58 15 109 0.191855
Smith et al. 2001 27 1 0 203 11 1 1 55 13 417 0.061618
Smith et al. 2001 19 1 0 203 11 1 1 39 13 417 0.061618
Smith et al. 2001 29 2 0 203 11 1 2 60 13 417 0.061618
Smith et al. 2001 21 5 0 68 12 0 5 47 12 148 0.468322
Smith et al. 2001 56 13 0 68 12 0 13 125 12 148 0.468322
Smith et al. 2001 34 4 0 68 12 0 4 72 12 148 0.468322
Zhou et al. 2000 37 15 0 68 12 0 15 89 12 148 0.468322
Zhou et al. 2000 35 9 0 46 14 0 9 79 14 106 0.306283
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HWE: Hardy-Weinberg equilibrium.

Quantitative synthesis

In total, 51 studies with 16,844 cancer cases and 23,736 controls focused on the relationship of PPARG rs1801282 C>G polymorphism with cancer risk. Overall, our results did not support any statistical evidence of the association between PPARG rs1801282 C>G polymorphism and cancer. As Caucasians, Asians and mixed populations were involved in our study, we performed subgroup analyses base on different ethnicities. The results showed that PPARG rs1801282 C>G polymorphism was a risk factor for cancer in Asians: GG+CG vs. CC (OR, 1.23; 95% CI, 1.01-1.50; P = 0.039), GG vs. CG+CC (OR, 2.36; 95% CI, 1.15-4.86; P = 0.020), GG vs. CC (OR, 2.43; 95% CI, 1.18-5.01; P = 0.016) and G vs. C (OR, 1.25; 95% CI, 1.04-1.51; P = 0.018) (Table 3; Figure 2). With respect to a subgroup analysis by cancer type, the results of the combined analyses showed that PPARG rs1801282 C>G polymorphism was associated with gastric cancer risk in five genetic models: GG+CG vs. CC (OR, 2.22; 95% CI, 1.61-3.07; P<0.001), GG vs. CG+CC (OR, 4.95; 95% CI, 1.86-13.16; P = 0.001), GG vs. CC (OR, 5.51; 95% CI, 2.06-14.79; P = 0.001), CG vs. CC (OR, 2.01; 95% CI, 1.44-2.82; P<0.001) and G vs. C (OR, 2.26; 95% CI, 1.69-3.02; P<0.001) (Table 4).

Table 3.

Different comparative genetic models results of this meta-analysis in the subgroup analysis by race

Polymorphism Genetic comparison Population OR (95% CI); P Test of heterogeneity
(p-Value, I2) Model
rs1801282 C>G GG+CG vs. CC All 1.00 (0.93-1.07); 0.987 0.007, 35.7% R
Asians 1.23 (1.01-1.50); 0.039 0.272, 17.6% F
Caucasians 0.96 (0.88-1.05); 0.402 0.009, 43.2% R
Mixed 0.98 (0.90-1.07); 0.656 0.305, 14.6% F
GG vs. CG+CC All 0.97 (0.83-1.14); 0.713 0.175, 16.8% F
Asians 2.36 (1.15-4.86); 0.020 0.808, 0.0% F
Caucasians 0.98 (0.80-1.18); 0.800 0.415, 3.3% F
Mixed 0.76 (0.39-1.45); 0.399 0.055, 51.4% R
GG vs. CC All 0.94 (0.79-1.12); 0.511 0.101, 22.5% F
Asians 2.43 (1.18-5.01); 0.016 0.785, 0.0% F
Caucasians 0.94 (0.75-1.16); 0.543 0.302, 11.0% F
Mixed 0.75 (0.39-1.46); 0.399 0.049, 52.6% R
CG vs. CC All 1.00 (0.93-1.07); 0.956 0.047, 26.3% R
Asians 1.20 (0.98-1.47); 0.083 0.439, 0.5% F
Caucasians 0.96 (0.88-1.05); 0.402 0.023, 37.9% R
Mixed 0.99 (0.91-1.09); 0.870 0.488, 0.0% F
G vs. C All 1.00 (0.94-1.07); 0.952 0.001, 42.3% R
Asians 1.25 (1.04-1.51); 0.018 0.145, 30.8% F
Caucasians 0.97 (0.89-1.05); 0.466 0.005, 45.6% R
Mixed 0.97 (0.89-1.05); 0.459 0.158, 30.3% F
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F indicates fixed model; R indicates random model.

Figure 2.

Figure 2

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Meta-analysis with a fixed-effect for the association of cancer risk with the PPARG rs1801282 C>G polymorphism in Asians (allele comparing model).

Table 4.

Different comparative genetic models results of this meta-analysis in the subgroup analysis by cancer type

Polymorphism Genetic comparison Cancer type OR (95% CI); P Test of heterogeneity
(p-Value, I2) Model
rs1801282 C>G GG+CG vs. CC All 1.00 (0.93-1.07); 0.987 0.007, 35.7% R
Prostate cancer 1.02 (0.82-1.27); 0.836 0.482, 0.0% F
Breast cancer 0.93 (0.78-1.10); 0.395 0.076, 47.5% R
Gastric cancer 2.22 (1.61-3.07); <0.001 0.922, 0.0% F
Colorectal cancer 0.94 (0.87-1.02); 0.131 0.125, 30.6% F
Pancreatic cancer 1.27 (0.61-2.65); 0.529 0.024, 80.2% R
Lung cancer 0.95 (0.75-1.19); 0.636 0.623, 0.0% F
Ovarian cancer 1.02 (0.86-1.21); 0.792 0.828, 0.0% F
Melanoma 1.03 (0.83-1.29); 0.764 0.619, 0.0% F
Glioblastoma 1.42 (0.53-3.79); 0.481 0.125, 57.6% R
Other cancers 1.01 (0.74-1.37); 0.968 0.459, 0.0% F
GG vs. CG+CC All 0.97 (0.83-1.14); 0.713 0.175, 16.8% F
Prostate cancer 0.76 (0.16-3.58); 0.726 0.104, 55.8% R
Breast cancer 1.00 (0.51-1.98); 0.991 0.045, 55.9% R
Gastric cancer 4.95 (1.86-13.16); 0.001 0.910, 0.0% F
Colorectal cancer 0.86 (0.65-1.12); 0.258 0.770, 0.0% F
Pancreatic cancer 1.02 (0.10-10.55); 0.985 0.126, 57.4% R
Lung cancer 1.17 (0.80-1.72); 0.420 0.845, 0.0% F
Ovarian cancer 0.98 (0.53-1.80); 0.946 0.497, 0.0% F
Melanoma 1.35 (0.66-2.78); 0.409 0.506, 0.0% F
Glioblastoma NA NA NA
Other cancers 0.40 (0.10-1.51); 0.174 0.319, 14.5% F
GG vs. CC All 0.94 (0.79-1.12); 0.511 0.101, 22.5% F
Prostate cancer 0.77 (0.16-3.81); 0.752 0.094, 57.7% R
Breast cancer 0.97 (0.49-1.93); 0.930 0.039, 57.2% R
Gastric cancer 5.51 (2.06-14.79); 0.001 0.920, 0.0% F
Colorectal cancer 0.83 (0.63-1.09); 0.183 0.729, 0.0% F
Pancreatic cancer 1.12 (0.09-13.61); 0.931 0.107, 61.6% R
Lung cancer 1.49 (0.72-3.09); 0.287 0.756, 0.0% F
Ovarian cancer 0.98 (0.54-1.81); 0.960 0.507, 0.0% F
Melanoma 1.36 (0.66-2.80); 0.402 0.492, 0.0% F
Glioblastoma NA NA NA
Other cancers 0.39 (0.10-1.50); 0.172 0.319, 14.6% F
CG vs. CC All 1.00 (0.93-1.07); 0.956 0.047, 26.3% R
Prostate cancer 1.05 (0.84-1.31); 0.684 0.693, 0.0% F
Breast cancer 0.91 (0.80-1.02); 0.108 0.118, 40.9% F
Gastric cancer 2.01 (1.44-2.82); <0.001 0.820, 0.0% F
Colorectal cancer 0.95 (0.88-1.03); 0.207 0.113, 32.0% F
Pancreatic cancer 1.26 (0.66-2.39); 0.485 0.050, 73.9% R
Lung cancer 0.92 (0.73-1.17); 0.507 0.637, 0.0% F
Ovarian cancer 1.03 (0.87-1.22); 0.747 0.872, 0.0% F
Melanoma 1.01 (0.80-1.27); 0.914 0.763, 0.0% F
Glioblastoma 1.42 (0.53-3.79); 0.481 0.125, 57.6% R
Other cancers 1.08 (0.79-1.47); 0.642 0.578, 0.0% F
G vs. C All 1.00 (0.94-1.07); 0.952 0.001, 42.3% R
Prostate cancer 1.00 (0.82-1.21); 0.981 0.278, 22.1% F
Breast cancer 0.94 (0.80-1.12); 0.515 0.040, 54.5% R
Gastric cancer 2.26 (1.69-3.02); <0.001 0.909, 0.0% F
Colorectal cancer 0.94 (0.88-1.01); 0.091 0.195, 23.4% F
Pancreatic cancer 1.23 (0.59-2.60); 0.580 0.014, 83.4% R
Lung cancer 1.00 (0.83-1.21); 0.993 0.743, 0.0% F
Ovarian cancer 1.01 (0.87-1.18); 0.857 0.739, 0.0% F
Melanoma 1.05 (0.86-1.29); 0.616 0.497, 0.0% F
Glioblastoma 1.37 (0.58-3.23); 0.477 0.150, 51.8% R
Other cancers 0.94 (0.71-1.24); 0.652 0.392, 2.5% F
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F indicates fixed model; R indicates random model.

Tests for publication bias

We used Begg’s Funnel plot and Egger’s test to examine publication bias of included studies. No statistical evidence of publication bias was identified in all genetic models (G vs. C: Begg’s test P = 0.709, Egger’s test P = 0.202; GG vs. CC: Begg’s test P = 0.879, Egger’s test P = 0.935; CG vs. CC: Begg’s test P = 0.372, Egger’s test P = 0.168; GG+CG vs. CC: Begg’s test P = 0.380, Egger’s test P = 0.157; GG vs. CG+CC: Begg’s test P = 1.000, Egger’s test P = 0.676; Figure 3).

Figure 3.

Figure 3

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For PPARG rs1801282 C>G polymorphism, Begg’s funnel plot analysis for publication bias (allele comparing model).

Sensitivity analyses

Influence of the potential publication bias involved in the meta-analysis on the pooled ORs and CIs was assessed by non-parametric “trim-and-fill” method and the filling of any potential studies did not significantly altered the final decision, suggesting that our results were stable and statistically robust: GG+CG vs. CC (adjusted pooled OR, 0.971; 95% CI, 0.897-1.051; P = 0.464), GG vs. CG+CC (adjusted pooled OR, 1.025; 95% CI, 0.865-1.213; P = 0.779), GG vs. CC (adjusted pooled OR, 1.002; 95% CI, 0.835-1.203; P = 0.982), CG vs. CC (adjusted pooled OR, 0.975; 95% CI, 0.905-1.051; P = 0.506) and G vs. C (adjusted pooled OR, 0.982; 95% CI, 0.911-1.059; P = 0.641) (Figure 4).

Figure 4.

Figure 4

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For PPARG rs1801282 C>G polymorphism, Filled funnel plot of meta-analysis (allele comparing model).

Tests for heterogeneity

Heterogeneity was assessed by the χ2-based Q-test in overall genetic models and sub-group analyses. We explored the main source of heterogeneity in sub-group analyses of ethnicity and cancer type. In current study, Caucasians, mixed populations, breast cancer, pancreatic cancer and prostate cancer provided potential sources of heterogeneity.

Discussion

The PPARG rs1801282 C>G polymorphism has been popularly examined on the risk of many cancers; however, the results of such studies are inconsistent. To address the gap, we performed an updated meta-analysis of published studies. The results indicated that PPARG rs1801282 C>G was not associated with the risk of overall cancer. The results from our sub-group analyses suggested that there was an effective modification of the cancer risk among Asians and gastric cancer patients.

Accumulating evidences demonstrated that the PPARG gene is related to malignance, which plays an important role in the pathogenesis of multiple cancers in some clinical studies and animal models. The association between PPARG rs1801282 C>G polymorphism and cancer risk has been widely explored. The prior study reported that PPARG rs1801282 C>G polymorphism was associated with reduced transactivation activity, lower body mass index and improved insulin sensitivity among middle-aged and elderly Caucasians [3]. PPARG gene variants may increase susceptibility of colorectal cancer by interruption of the metabolism of a high fat diet [53]. In the current study, a significantly increased risk of cancer correlated with PPARG rs1801282 C>G polymorphism was overt among Asians and gastric cancer patients. Our results suggest different cancerigenic mechanisms of different cancers and different population. A previous meta-analysis was performed to determine the effect of PPARG polymorphisms on the risk of cancer [15]. Comparing with that, our pooled analyses have some merits. First, this is a larger samples meta-analysis not only to analyze the association between PPARG rs1801282 C>G and cancer susceptibility in different races and different cancer types, but also to support the rs1801282 C>G polymorphism is a risk factor in Asians and gastric cancer. Second, we carried out a more extensively pooled analysis by calculating five different comparison models and performing sub-group analyses.

Since heterogeneity across studies may affect the strengths of results, we conducted sub-group analyses. In our study, relatively high heterogeneity was observed. Then, the random-effect model was utilized when significant heterogeneity was found. Meanwhile, to analyze the major source of heterogeneity, we conducted sub-group analyses by races and cancer types. Results of meta-analysis showed that heterogeneity greatly reduced or vanished in some sub-groups. We also performed non-parametric “trim-and-fill” method to verify the stability of our results. The adjusted ORs and CIs were not materially altered, suggesting that the results of our study were reliable and suggestive. The publication bias across studies for the correlation of PPARG rs1801282 C>G polymorphism with cancer risk was not observed.

Some limitations should be noted in this meta-analysis when interpreting the results. First of all, only published literatures were included in our study, some unpublished investigations that might also be fit for the inclusion criteria were ignored. Secondly, due to limited individual data (e.g., age, sex and other environmental factors) in some studies, we did not perform a more precise analysis, which limited further assessments to a certain extent. Finally, in some subgroups, sample sizes were relatively small, which might have insufficient power to get a reliable result. In the future, studies with larger sample sizes will be needed to validate these associations.

In conclusion, our findings suggest that PPARG rs1801282 C>G polymorphism is a candidate for susceptibility to gastric cancer and Asians. Further studies with larger samples and detailed environmental factors will be needed to confirm our results.

Acknowledgements

Grant support: The project was supported by the Natural Science Foundation of Fujian Province (Grant No. 2014J01298 and 2015J01435), the Medical Innovation Foundation of Fujian Province (Grant No. 2015-CX-9) and the National Clinical Key Specialty Construction Program.

Disclosure of conflict of interest

None.

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