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. 2014 Feb 18;9(2):e88748. doi: 10.1371/journal.pone.0088748

Association of Fas -1377 G/A Polymorphism with Susceptibility to Cancer

Peiliang Geng 1,#, Jianjun Li 1,#, Juanjuan Ou 1, Ganfeng Xie 1, Ning Wang 1, Lisha Xiang 1, Rina Sa 1, Chen Liu 1, Hongtao Li 1, Houjie Liang 1,*
Editor: William B Coleman2
PMCID: PMC3928286  PMID: 24558420

Abstract

Background

The relationship between Fas -1377 G/A polymorphism and cancer susceptibility has been implicated in accumulating data. However, the data presented inconsistent results. This study was devised to investigate the association of Fas -1377 G/A polymorphism and cancer susceptibility in a large number of participants.

Methods

The databases of PubMed, Embase, and Web of Science were searched and a total of 27 case-control studies including 13,355 cases and 16,078 controls were included in this meta-analysis. Pooled odds ratios (ORs) with 95% confidence intervals (CIs) were calculated using the fixed-effects model. Statistical analyses were performed by using Stata software.

Results

The results suggested that Fas -1377 G/A polymorphism was overall associated with cancer susceptibility (additive model: OR, 1.16, 95%CI = 1.06–1.27, P heterogeneity  = 0.381; recessive model: OR, 1.19, 95%CI = 1.10–1.29, P heterogeneity  = 0.137). In the subgroup analysis by cancer type, significantly increased risk was observed in breast cancer (additive model: OR, 1.24, 95%CI = 1.04–1.58, P heterogeneity  = 0.614; recessive model: OR, 1.24, 95%CI = 1.02–1.51, P heterogeneity  = 0.349) and lung cancer (recessive model: OR, 1.25, 95%CI = 1.04–1.49, P heterogeneity  = 0.090). Similarly, elevated cancer risk associated with Fas -1377 G/A polymorphism was revealed in Asians.

Conclusions

The combined results suggest that Fas -1377 G/A polymorphism might modulate cancer susceptibility in an Asian-specific manner.

Introduction

Cancer arises as a result of complex interactions between genetic and environmental factors and has become a major public health problem all over the world [1]–[5]. In recent years, many individual studies have set out to determine whether there is an association between genetic polymorphisms and cancer susceptibility, such as Fas -1377 G/A polymorphism and cancer susceptibility. However, these studies showed conflicting results that failed to provide compelling evidence for cancer susceptibility [6]–[9].

Apoptosis is a process of programmed cell death regulated by genes. Inappropriate regulation of apoptosis could lead to a broad range of human disorders including cancer [10]–[13]. Fas is a member of the tumor necrosis factor receptor superfamily and regulates apoptotic activities in activated lymphocytes [14]. Located on chromosome 10q24.1, Fas is highly polymorphic [15]. A functional polymorphism with a G to A substitution at -1377 position within the Fas gene has been extensively explored in the field of cancer. But there is no decisive conclusion of the role of this polymorphism in cancer development [6], [7]. In addition, several studies have been subsequently published since a previous meta-analysis was reported in 2009 [47]. In view of this, we decided to carry out a meta-analysis including 27 eligible studies published to date to systematically and comprehensively estimate the association between Fas -1377 G/A polymorphism and susceptibility to cancer.

Materials and Methods

Literature Search Strategy

The databases of PubMed, Embase, and Web of Science were searched (the last search was updated in May 2013) to identify all relevant publications on the association between Fas -1377 G/A polymorphism and cancer risk. The following search terms and their synonyms were used: “Fas”, “1377 G/A” or “CD95” or “rs2234767”, “polymorphism” or “variation”, and “cancer”. We also manually searched the reference lists of all eligible studies and review articles to obtain additional usable data that can be included in the current meta-analysis.

Inclusion Criteria and Exclusion Criteria

We selected eligible studies according to the following criteria: (1) the study must have a case-control design; (2) the association between Fas -1377 G/A polymorphisms and cancer risk must be examined; (3) adequate genotyping data must be contained such that odds ratios (ORs) with 95% confidence intervals (CIs) could be calculated; (4) the study had to be published in English and use human subjects. Exclusion criteria were: (1) insufficient information on the distribution of Fas -1377 genotypes; (2) case-only studies; (3) duplicated publications. If a study was subsequently updated, we selected the study with the largest sample size. Two investigators independently reviewed all studies to examine whether they fulfilled the inclusion criteria.

Data Extraction

Two independent investigators (Peiliang Geng and Jianjun Li) extracted the original data according to the inclusion criteria and exclusion criteria to ensure the accuracy of the retrieved information. The data extracted from each eligible study included the first author's name, year of publication, cancer type, ethnicity, source of controls, method adopted for genotyping, number of cases and controls and genotype frequencies. Disputes were settled by consulting the third person (Houjie Liang).

Statistical Analysis

Crude ORs with 95% CIs were calculated to evaluate the strength of the association between Fas -1377 G/A polymorphism and cancer risk. The pooled ORs were performed for additive model, dominant model and recessive model. Subgroup analysis by cancer type, ethnicity and source of control were also conducted to further assess if the Fas -1377 polymorphism was associated with cancer susceptibility in each subgroup. Heterogeneity assumption was evaluated by the chi-square based Q-test and I2 statistics [16], [17], P>0.05 for the Q test or I2<50% suggested a lack of heterogeneity. In this situation, the OR of each study was calculated by the fixed-effects model (the Mantel-Haenszel method) [18]. If P<0.05 or I2>50%, the random-effects model (the DerSimonian and Laird method) was used [19]. Sensitivity analysis was performed by removing one study at a time to ensure that our findings were not driven by any single study. The evaluation of potential publication bias was performed using the Begg's funnel plots and Egger's test [20]. Hardy-Weinberg equilibrium (HWE) of the control groups was tested by the χ2 test for goodness of fitness. All statistical analyses were performed by STATA version 12.0 (Stata Corporation, College Station, TX, USA). A level of P<0.05 was accepted as statistically significant.

Results

Study Characteristics

We initially identified 147 potentially relevant studies, of which 27 met the pre-described inclusion criteria and were included in the meta-analysis of the association between Fas -1377G/A polymorphism and cancer risk (Figure 1). Characteristics of all eligible case-control studies for the relationship of Fas -1377G/A polymorphism with cancer risk are summarized in Table 1. Of the twenty-seven studies included, an array of cancers including AML [6], [7], breast cancer [21]–[25], cervical cancer [26]–[28], lung cancer [8], [9], [29], [30], gastric cancer [31], [32], melanoma [33], [34], oral cancer [35], [36], and several other cancers [37]–[43] were involved. The subgroup analysis was carried out by cancer type, ethnicity and source of control, respectively. Genotype frequencies were available in all of the 27 studies.

Figure 1. Flow diagram of study identification.

Figure 1

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Table 1. Main characteristics of the 27 eligible studies.

Authors Year Source of control Ethnicity Cancer type Genotyping method Case Control HWE
Sample size GG GA AA G A Sample size GG GA AA G A
Sibley 2003 Population European AML PCR–RFLP 471 319 136 16 774 168 931 726 186 19 1638 224 0.087
Sun 2004 Population Asian Esophageal PCR–RFLP 588 250 234 104 734 442 648 273 306 69 852 444 0.218
Kripple 2004 Population European Breast TaqMan 499 371 120 8 862 136 497 401 92 4 894 100 0.610
Lai 2005 Hospital Asian Cervical TaqMan 318 127 138 53 392 244 318 99 165 54 3633 273 0.293
Sun 2005 Population Asian Cervical PCR–RFLP 314 144 144 26 432 196 615 282 277 56 841 389 0.304
Zhang 2005 Population Asian Lung PCR–RFLP 1000 413 433 154 1259 741 1270 539 601 130 1679 861 0.046
Li 2006 Hospital Asian Bladder PCR–RFLP 216 66 104 46 236 196 252 81 124 47 286 218 0.970
Park 2006 Hospital Asian Lung PCR–RFLP 582 187 300 95 674 490 582 172 313 97 657 507 0.024
Li 2006 Hospital European Melanoma PCR–RFLP 602 486 107 9 1079 125 603 459 134 10 1052 154 0.951
Zhang 2006 Hospital European SCCHN PCR–RFLP 721 562 142 17 1266 176 1234 957 264 13 2178 290 0.268
Gormas 2007 Population European Lung PCR 94 21 73 0 115 73 50 13 37 0 63 37 >0.05
Zhang 2007 Population Asian Breast PCR–RFLP 840 293 418 129 1004 676 839 345 382 112 1072 606 0.700
Crew 2007 Population European Breast TaqMan 1057 809 225 23 1843 271 1106 847 234 25 1928 284 0.069
Koshkina 2007 Hospital European Osteosarcoma PCR–RFLP 123 99 22 2 220 26 510 400 100 10 900 120 0.210
Zhang 2007 Population European Melanoma PCR–RFLP 229 183 41 5 407 51 351 269 70 12 608 94 0.009
Ter-Minassi 2008 Hospital European Lung TaqMan 2174 1645 492 37 3782 566 1497 1138 336 23 2612 382 0.751
Kang 2008 Population Asian Cervical PCR–RFLP 154 54 69 31 177 131 168 56 82 20 194 142 0.998
Yang 2008 Population Asian Pancreatic PCR–RFLP 397 186 169 42 541 253 907 420 376 111 1216 598 0.062
Zhou 2009 Population Asian Gastric PCR–RFLP 262 124 117 21 365 159 524 225 251 48 701 347 0.062
Cao 2010 Population Asian Nasopharyngeal PCR–RFLP 576 141 264 171 546 606 608 172 303 133 647 569 0.984
Kim 2010 Population Asian AML PCR 592 195 303 94 693 491 858 286 427 145 999 717 0.501
Wang 2010 Population Asian Oral PCR–RFLP 431 146 208 77 500 362 333 115 165 53 395 271 0.628
Zhu 2010 Hospital Asian Renal PCR–RFLP 353 124 173 56 421 285 365 161 161 43 483 247 0.777
Kupcinskas 2011 Hospital European Gastric TaqMan 114 95 18 1 208 20 238 197 40 1 434 42 0.492
Wang 2012 Hospital Asian Breast PCR–RFLP 375 138 171 66 447 303 496 197 246 53 640 352 0.064
Hashemi 2013 Population Asian Breast PCR 134 20 106 8 146 122 152 26 115 11 167 137 >0.05
Karimi 2013 Population Asian Oral PCR–RFLP 139 88 42 9 218 60 126 84 30 12 198 54 0.001
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PCR: polymerase chain reaction; PCR-RFLP: PCR-restriction fragment length polymorphism; TaqMan: TaqManSNP; AML: acute myeloid leukemia; SCCHN: squamous cell carcinoma of the head and neck; HWE: Hardy-Weinberg equilibrium.

Meta-analysis

Major results of the meta-analysis are presented in Table 2. No significant between-study heterogeneity was detected across studies and thus we selected the fix-effects model to summarize the ORs. Overall, we found a significant association between Fas -1377G/A polymorphism and cancer risk under the additive model (OR, 1.16, 95%CI = 1.06–1.27, P heterogeneity  = 0.381), but the association was more pronounced under the recessive model (OR, 1.19, 95%CI = 1.10–1.29, P heterogeneity  = 0.137) (Figure 2, 3). In the subgroup analysis by cancer type, significantly increased risk was observed in breast cancer (additive model: OR, 1.24, 95%CI = 1.04–1.58, P heterogeneity  = 0.614; recessive model: OR, 1.24, 95%CI = 1.02–1.51, P heterogeneity  = 0.349) and lung cancer (recessive model: OR, 1.25, 95%CI = 1.04–1.49, P heterogeneity  = 0.090).

Table 2. Main results of the pooled data in the meta-analysis.

Additive model Dominant model Recessive model
Subtypes OR (95% CI) Heterogeneity OR (95% CI) Heterogeneity OR (95% CI) Heterogeneity
Ph I2 (%) Ph I2 (%) Ph I2 (%)
Cancer type
AML 1.07 (0.82, 1.14) 0.080 67.4 1.14 (0.99, 1.30) 0.011 84.6 1.02 (0.79, 1.32) 0.125 57.6
Breast 1.28 (1.04, 1.58) 0.614 0 1.08 (0.99, 1.19) 0.602 0 1.24 (1.02, 1.51) 0.349 10.0
Cervical 0.96 (0.72, 1.29) 0.432 0 0.96 (0.83, 1.12) 0.590 0 1.08 (0.82, 1.42) 0.245 28.9
Lung 1.19 (0.98, 1.43) 0.163 45.0 1.01 (0.92, 1.10) 0.960 0 1.25 (1.04, 1.49) 0.090 58.4
Melanoma 0.74 (0.37, 1.47) 0.659 0 0.82 (0.66, 1.03) 0.795 0 0.77 (0.39, 1.53) 0.627 0
Gastric 0.85 (0.50, 1.46) 0.526 0 0.93 (0.74, 1.17) 0.885 0 0.90 (0.53, 1.52) 0.547 0
Oral 1.03 (0.71, 1.48) 0.444 0 1.03 (0.84, 1.26) 0.749 0 1.04 (0.74, 1.47) 0.313 1.9
Other 1.26 (1.08, 1.47) 0.301 16.9 1.02 (0.94, 1.11) 0.940 0 1.31 (1.13, 1.52) 0.139 38.0
Ethnicity
European 1.23 (0.94, 1.60) 0.419 1.8 1.04 (0.96, 1.13) 0.069 43.4 1.22 (0.93, 1.58) 0.519 0
Asian 1.15 (1.05, 1.26) 0.318 11.6 1.02 (0.97, 1.07) 0.994 0 1.19 (1.09, 1.30) 0.060 37.4
Source of control
Population 1.16 (1.05, 1.29) 0.383 6.1 1.05 (0.99, 1.10) 0.587 0 1.19 (1.08, 1.32) 0.073 36.4
Hospital 1.15 (0.97, 1.35) 0.311 14.3 0.99 (0.92, 1.06) 0.783 0 1.19 (1.02, 1.39) 0.419 2.2
Total 1.16 (1.06, 1.27) 0.381 5.7 1.02 (0.98, 1.07) 0.722 0 1.19 (1.10, 1.29) 0.137 23.7
Total* 1.16 (1.05, 1.28) 0.484 0 1.03 (0.98, 1.08) 0.583 0 1.19 (1.08, 1.30) 0.249 15.8
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AML: acute myeloid leukemia; Ph: p-value of heterogeneity test; CI: confidence interval; OR: odds ratio;

*meta-analysis results after removing the studies deviating from Hardy-Weinberg equilibrium (HWE).

Figure 2. Meta-analysis for the association between Fas -1377 G/A polymorphism and cancer risk by fixed-effects model (additive model; stratified by ethnicity).

Figure 2

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Figure 3. Meta-analysis for the association between Fas -1377 G/A polymorphism and cancer risk by fixed-effects model (recessive model; stratified by ethnicity).

Figure 3

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Subgroup analysis by ethnicity also provided evidence for an association in Asian populations (additive model: OR, 1.15, 95%CI = 1.05–1.26, P heterogeneity  = 0.318; recessive model: OR, 1.19, 95%CI = 1.09–1.30, P heterogeneity  = 0.060), but not in European populations. In the succeeding analysis by source of control, an elevated cancer risk was observed in both population-based and hospital-based studies (Table 2).

Sensitivity Analysis

We performed a leave-one-out sensitivity analysis by omitting one study at a time to assess the stability of the combined results. The results suggested that our findings were not substantially affected by any single study (data not shown).

Publication Bias

Begg's funnel plot and Egger's test were performed to detect publication bias. No statistically significant evidence of publication bias was revealed (Begg's test: P = 0.826; Egger's test: P = 0.721, additive model) (Figure 4).

Figure 4. Publication bias test for all included studies (additive model).

Figure 4

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Discussion

The human Fas gene mapped on chromosome 10q24.1 consists of nine exons and eight introns [15]. -1377 G/A polymorphism, located in the promoter region of the Fas gene, has been investigated in a variety of previous studies looking at cancer risk [8], [21], [22], [26]. However, these findings remain controversial rather than conclusive. This might be attributed to the different ethnicities, distinct study design, and sample inadequacy in each of the published studies. But meta-analysis could avoid the shortcomings and convincingly estimate the genetic association through including all relevant studies.

In our meta-analysis, we observed Fas -1377G/A polymorphism was overall associated with cancer susceptibility under the additive model and the recessive model. Several published meta-analyses observed the same finding that Fas -1377 G/A polymorphism was associated with cancer risk as well as some common diseases, such as autoimmune rheumatic diseases, systemic lupus erythematosus [44]–[47]. The detection power of the four meta-analyses, however, may be limited largely because of sample insufficiency: 4 publications (996 cases and 1,160 controls) were included by Lu et al. [44], 5 (615 cases and 622 controls) by Lee et al. [45], 3 (444 cases and 442 controls) by Xiang et al. [46] and 17 (10,564 cases and 12,075 controls) by Qiu et al [47]. Our meta-analysis nevertheless summarized data from 27 studies composed of 13,355 cases and 16,078 controls. It should be noted that study size is obviously important to know the proportion of false positive findings of meta-analysis. Therefore, the relatively larger sample may assure the statistical power of our study. Deviation from HWE was observed in several studies, which may result from misclassification of genotypes, because multiple genotyping methods were used across studies. When we reanalyzed the studies without departure form HWE, the general results were not significantly altered, suggesting our findings are robust and convincing.

Apart from the comparison among all subjects, we also performed stratification analysis by cancer type. We found that Fas -1377 G/A polymorphism increased the risk of some cancers, such as breast cancer and lung cancer. Our findings were consistent with those revealed in the previous studies [6], [9], [21], [26], but contradictory discoveries that there was no association between Fas -1377 G/A polymorphism and lung cancer were also suggested in two studies [7], [8]. The underlying etiology mechanisms differ substantially across cancers, and the role of Fas -1377 G/A polymorphism in various caners requires to be identified by future larger studies.

In addition, in the subgroup analysis by ethnicity, Fas -1377 G/A polymorphism was found to increase cancer risk in Asian populations under several genetic models, such as the recessive model and the additive model. However, this association was obtained in European populations. There is obvious disparity in genotype frequencies between the two ethnic groups (GA: 21.3% vs 47.7%; AA: 1.5% vs 13.2%). It is known that different genetic background donates a series of differences between ethnic groups, for instance, frequency of exposure to cancer-causing agents and diverse lifestyles, which are important components in the process of cancer progression.

In the final subgroup analysis by control source, we observed significant association in both population-based and hospital-based studies. However, investigators demonstrated a different discovery of significantly increased cancer risk associated with Fas -1377 AA genotype among studies based on population-based controls, but not among studies of hospital-based controls [47]. Control subjects in some hospital-based studies may be poorly-defined reference populations and failed to well represent the general population, leading to some biases in the analysis, but the relatively small sample may be responsible for a large part of the inconsistency.

Some limitations in our meta-analysis need to be addressed. To begin with, in the subgroup analysis by cancer type, significant association was not observed in several cancers, such as gastric cancer, melanoma cancer and oral cancer. Fas -1377 G/A polymorphism and these cancers may be positively correlated, which may be masked due to the small sample size in this study. Furthermore, there existed heterogeneity between studies. The reason might be attributable to the different genetic backgrounds of the subjects and study design in each of the included studies. Finally, this meta-analysis was carried out among Asian and European populations, thus the results can not be applicable in other ethnicities.

In summary, the meta-analysis provided evidence that Fas -1377 G/A polymorphism might be associated with an increased cancer risk. Significant association was also found in subgroup analyses by cancer type, ethnicity and source of control. In future, studies with a larger sample size and multiple ethnic groups are required to further validate the relationship between Fas -1377 G/A polymorphism and cancer susceptibility.

Funding Statement

This work was supported in part by grant number 30973430 from the National Natural Science Foundation of China (to HJ.L). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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