FASL rs763110 Polymorphism Contributes to Cancer Risk: An Updated Meta-Analysis Involving 43,295 Subjects

Abstract

Background

Published studies investigating the association between genetic polymorphism -884C/T (rs763110) of the FAS ligand (FASL) promoter and cancer risk reported inconclusive results. To derive a more precise estimation of the relationship, we performed an updated meta-analysis of all eligible studies.

Methodology/Principal Findings

We carried out a meta-analysis, including 47 studies with 19,810 cases and 23,485 controls, to confirm a more conclusive association between the FASL rs763110 polymorphism and cancer susceptibility. Overall, significantly reduced cancer risk was associated with the variant -884T when all studies were pooled (TC vs. CC: OR = 0.83, 95%CI = 0.75–0.92; Pheterogeneity<0.001; TT+TC vs. CC: OR = 0.85, 95%CI = 0.77–0.94; Pheterogeneity<0.001). Stratified analysis revealed that there was a statistically reduced cancer risk in Asians (TC vs. CC: OR = 0.76, 95%CI = 0.67–0.87; Pheterogeneity<0.001; TT+TC vs. CC: OR = 0.79, 95%CI = 0.70–0.90; Pheterogeneity<0.001) and in patients with cancers of head and neck (TC vs. CC: OR = 0.87, 95%CI = 0.77–0.99; Pheterogeneity = 0.118; TT+TC vs. CC: OR = 0.88, 95%CI = 0.78–0.99; Pheterogeneity = 0.168) and ovarian cancer (TC vs. CC: OR = 0.67, 95%CI = 0.49–0.90; Pheterogeneity = 0.187; TT+TC vs. CC: OR = 0.64, 95%CI = 0.48–0.86; Pheterogeneity = 0.199). Meta-regression showed that ethnicity (p = 0.029) and genotyping method (p = 0.043) but not cancer types (p = 0.772), sample size (p = 0.518), or source of controls (p = 0.826) were the source of heterogeneity in heterozygote comparison.

Conclusion

Our results suggest that the FASL polymorphism rs763110 is associated with a significantly reduced risk of cancer, especially in Asian populations.

About the Authors:

Lei Xu

Contributed equally to this work with: Lei Xu, Xin Zhou

Affiliations The Fourth Clinical College of Nanjing Medical University, Nanjing, China, Department of Thoracic Surgery, Nanjing Medical University Affiliated Cancer Hospital Cancer Institute of Jiangsu Province, Nanjing, China

Xin Zhou

Contributed equally to this work with: Lei Xu, Xin Zhou

Affiliation: Department of Oncology, First Clinical College of Nanjing Medical University, Nanjing, China

Feng Jiang

Affiliation: Department of Thoracic Surgery, Nanjing Medical University Affiliated Cancer Hospital Cancer Institute of Jiangsu Province, Nanjing, China

Man-Tang Qiu

Affiliations The Fourth Clinical College of Nanjing Medical University, Nanjing, China, Department of Thoracic Surgery, Nanjing Medical University Affiliated Cancer Hospital Cancer Institute of Jiangsu Province, Nanjing, China

Zhi Zhang

Affiliation: Department of Thoracic Surgery, Nanjing Medical University Affiliated Cancer Hospital Cancer Institute of Jiangsu Province, Nanjing, China

Rong Yin

Affiliation: Department of Thoracic Surgery, Nanjing Medical University Affiliated Cancer Hospital Cancer Institute of Jiangsu Province, Nanjing, China

Lin Xu

* E-mail: [email protected]

Affiliation: Department of Thoracic Surgery, Nanjing Medical University Affiliated Cancer Hospital Cancer Institute of Jiangsu Province, Nanjing, China

Introduction

Cancer is a major public health burden all around the world which counts for one in 4 deaths in the United States [1]. The global burden of cancer continues to increase largely because of the aging and growth of the world population as well as an increasing adoption of cancer-related lifestyle, such as smoking, physical inactivity and ‘‘westernized’’ diets [2]. It was reported that there was about 12.7 million new cancer cases and 7.6 million cancer deaths throughout the world in 2008 [3]. However, the mechanism of carcinogenesis is complicated and remains largely unknown. Many studies identified that genetics play a vital role in determining cancer risk and various genetic variations have been identified to elevate cancer risk [4]. Single nucleotide polymorphisms (SNPs) are the most common form of human genetic variation and may contribute to individual’s cancer risk through interaction with environmental factors [5].

Apoptosis plays an important role in various physiological functions and pathological processes, including immune diseases and carcinogenesis [6], [7], and defects in apoptotic pathways are suggested to be associated with a number of human diseases, ranging from neurodegenerative disorders to various cancers [8]. FASL is a transmembrane protein belonging to the tumor necrosis factor (TNF) superfamily which can trigger apoptotic cell death by ligation to its receptor, Fas (CD95/APO-1). Numerous evidence suggested that FASL could mediate immune privilege in human tumors by inducing FAS-mediated apoptosis in tumor-specific lymphocytes [9].

Recently many common low-penetrance genes have been considered as potential markers of cancer susceptibility. FASL gene is an important one of them, which situated on chromosome 1q23 with four exons. Though it is highly polymorphic, but the polymorphism C to T substitution at position -844(FASL-844C/T, rs763110) in the promoter region has been studied extensively [10]. It is located in a binding motif for another transcription factor, CAAT/enhancer-binding proteinβand a higher basal expression of FASL is significantly associated with the FASL -844C allele compared with the - 844T allele [11].

In the past decade, numerous studies have suggested that the FASL -844C/T polymorphism is associated with many types of cancers [12]–[55], but the results are conflicting rather than conclusive. Although two meta-analyses have discussed the rs763110 polymorphism and susceptibility to cancers [56], [57], but they did not include all of the eligible studies, especially the case-control studies published in the past five years. Therefore, we performed this updated meta-analysis of 47 association studies of the FASL rs763110 polymorphism and cancer risk (including a total of 43,295 participates, approximately twice as many subjects as in previous such meta-analysis).

Methods

Publication search

PubMed and China National Knowledge Infrastructure (CNKI) were searched comprehensively using the terms relating to the FASL gene (e.g. “FASL”, “FAS ligand” or “CD95L”) in combination with words related to cancer (e.g. “cancer”, “carcinoma”, “tumor” or “neoplasm”) and polymorphism or variation. Last search was updated on May 29, 2013.

In order to minimize potential publication bias, there were no language and other restrictions. Furthermore, citations in the retrieved articles were manually examined to identify additional relevant studies. Only the most recent or complete study was used if more than one of the same patient populations was applied in several publications.

Inclusion and exclusion criteria

The major inclusion criteria were: (1) case-control or nested case-control studies; (2) investigating the association between the FASL rs763110 polymorphism and cancer risk; (3) cancers diagnosed by histopathology; (4) sufficient data for calculating an odds ratio (OR) with 95% confidence intervals (CIs). Accordingly, case-only studies, reviews and repeated papers were excluded.

Data extraction

Data were independently extracted by two reviewers (Xu and Zhou) and checked by the other authors. The following information was abstracted: name of the first author, year of publication, country where the study was conducted, genotyping method, ethnicity, cancer types, source of controls, age, gender, number of cases and controls, genotype frequency in cases and controls and Hardy-Winberg equilibrium (HWE). Different ethnicities were classified as Caucasian, Asian, and African. All eligible studies were defined as hospital-based (HB) or population-based (PB) according to the source of controls. In case of discrepancies, a consensus on each item was reached among the authors.

Statistical analysis

For the controls of each study, Hardy-Winberg equilibrium (HWE) was evaluated using the goodness-of-fit chi-square test and a p<0.05 was considered with a significant selective bias [58]. Crude ORs with 95% CIs were used to assess the strength of association between the FALS rs763110 polymorphism and cancer susceptibility and a 95% CI without 1 for OR indicating a significantly increased or reduced cancer risk. The pooled ORs were performed for homozygote comparison (TT versus CC), heterozygote comparison (TC versus CC), dominant (TC+TT versus CC) and recessive (TT versus TC+CC) modes, respectively. Subgroup analyses were also performed to investigate the effects of confounding factors: cancer types, ethnicities, sample size (studies with more than 1000 subjects were sorted as “large”, and studies with less than 1000 subjects were sorted as “small”) and source of controls. Sensitivity analyses were conducted to identify individual study’ effect on pooled results and test the reliability of results. Heterogeneity assumption was checked by the chi-square based Q test, and the heterogeneity was considered significant when p<0.10 [59]. The random-effects model (based on DerSimonian-Laird method) was used when heterogeneity existed among studies; otherwise the fixed-effects model (based on Mantel-Haenszel method) was applied [60]. Stratification and meta-regression analyses were used to detect the potential heterogeneity among studies. The presence of publication bias was examined by Begg’s funnel plot and the Egger’ linear regression test, and a p<0.05 was considered significant [61]. All statistical analyses were performed with STATA software (version 12.0; StataCorp, College Station, Texas USA). And all P values were two-side.

Results

Characteristics of eligible studies

After careful retrieve and selection, 44 articles (listed in Table 1) were identified according to inclusion and exclusion criteria. The flow chart of selection was shown in Figure 1. Qureshi’s and Chatterjee’s studies sorted the data into three types of cancers and two ethnicities respectively. Each group in these studies was considered separately. Thus, a total of 47 case-control studies, including 19,810 cases and 23,485 controls were analyzed in this meta-analysis.

[Figure omitted. See PDF.]

Figure 1. PRISMA Flow Chart.

*a total of 44 articles were identified and five separate studies were reported in two articles, thus 47 studies were eligible

https://doi.org/10.1371/journal.pone.0074543.g001

[Figure omitted. See PDF.]

Table 1. Main characteristics of eligible studies.

https://doi.org/10.1371/journal.pone.0074543.t001

Of the 47 studies, 42 were published in English and 5 in Chinese, 14 of them were studies of Caucasians, 30 studies of Asian and 3 studies of African (details shown in Table 1). All cases were histopathologically confirmed. Controls were mainly matched for age and/or gender, of which 21 were population-based (PB) and 26 were hospital-based (HB). One of the studies did not show source of controls, we considered it to be population-based [15]. All studies showed that the distribution of genotypes in the control group was in agreement with the Hardy-Winberg equilibrium (HWE) except for two studies (Cao [23], p = 0.004 and Sun [48], p = 0.002).

Main results

Table 2 showed the main results of this meta-analysis. Overall, significantly reduced cancer risk was associated with the FASL -844T allele when all studies were pooled (TC vs. CC: OR = 0.83, 95%CI = 0.75–0.92; Pheterogeneity<0.001, Figure 2; dominant model: OR = 0.85, 95%CI = 0.77–0.94; Pheterogeneity<0.001, Figure 3). No significant association was found in homozygote comparison (TT vs. CC: OR = 0.89, 95%CI =  0.79–1.01; Pheterogeneity = 0.074) or recessive model (TT vs. TC+CC: OR =  0.97, 95%CI =  0.86–1.09; Pheterogeneity<0.001).

[Figure omitted. See PDF.]

Figure 2. Subgroup analysis by ethnicity of ORs with a random-effects model for associations between the FASL rs763110 polymorphism and cancer risk under dominant model (TC+TT vs. CC).

https://doi.org/10.1371/journal.pone.0074543.g002

[Figure omitted. See PDF.]

Figure 3. Subgroup analysis by cancer type of ORs with a random-effects model for associations between the FASL rs763110 polymorphism and cancer risk under heterozygote comparison (TC vs. CC).

BC: breast cancer; LC: lung cancer; EC: esophageal cancer; CC: cervical cancer GC: gastric cancer & cardiac cancer AL: acute lymphoblastic leukemia & acute myeloid leukemia OC: ovarian cancer CHN: cancers of head and neck

https://doi.org/10.1371/journal.pone.0074543.g003

[Figure omitted. See PDF.]

Table 2. Meta-analysis results.

https://doi.org/10.1371/journal.pone.0074543.t002

In the sub-group analyses by ethnicity, significant reduced risks were found for T carriers among Asians (TC vs. CC: OR = 0.76, 95%CI = 0.67–0.87; Pheterogeneity<0.001, Figure 2; dominant model: OR = 0.79, 95%CI = 0.70–0.90; Pheterogeneity<0.001). In Caucasians and Africans, however, no significant association was found in each comparison.

When we performed sub-group analyses by cancer types, reduced cancer risk was found in the heterozygote and dominant model comparison for cancers of head and neck ( TC vs. CC: OR = 0.87, 95%CI = 0.77–0.99; Pheterogeneity = 0.118; dominant model: OR = 0.88, 95%CI = 0.78–0.99; Pheterogeneity = 0.168, Figure 3) and ovarian cancer (TC vs. CC: OR = 0.67, 95%CI = 0.49–0.90; Pheterogeneity = 0.187; TT+TC vs. CC: OR = 0.64, 95%CI = 0.48–0.86; Pheterogeneity = 0.199, Figure 3), respectively.

The results of the sub-group analyses by sample size, source of control and genotyping method were shown in supplemental information (Figure S1 – S3 in File S1). We found a statistically significant link between the FASL rs763110 polymorphism and cancer risk in studies utilizing genotyping method with polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) assay in heterozygote and dominant model comparison (TC vs. CC: OR = 0.79, 95%CI = 0.70–0.87; Pheterogeneity<0.001; dominant model: OR = 0.80, 95%CI = 0.72–0.90; Pheterogeneity<0.001), but not for studies using Taqman assay.

Heterogeneity

Heterogeneity among studies was identified in overall comparisons and also in sub-group analyses (shown in Table 2). To examine the potential source of heterogeneity, meta-regression was performed using variables as cancer type, source of control, ethnicity, genotyping method and sample size in heterozygote comparison (TC vs. CC). The results revealed that ethnicity (p = 0.029) and genotyping method (p = 0.043) but not cancer types (p = 0.772), sample size (p = 0.518), or source of controls (p = 0.826) contributed to the source of heterogeneity.

Sensitivity analysis

Leave-one-out sensitivity analysis was performed to explore individual study’s influence on the pooled results. The results revealed that no individual study affected the pooled OR significantly since no substantial change was found (figure not shown).

Publication bias

Begg’s funnel plot and Egger’s test were performed to assess the publication bias. The figure of the funnel plot did not show any evidence of obvious asymmetry (p = 0.430 for TC vs. CC) (Figure 4). Then, the Egger’s test was used to statistical test and publication bias was not detected either (p = 0.572 for TT vs. CC, p = 0.714 for TC vs. CC, p =  0.967 for dominant model, and p = 0.388 for recessive model, respectively).

[Figure omitted. See PDF.]

Figure 4. Funnel plot of heterozygote comparison (TC vs. CC).

Funnel plot of all 47 eligible studies p =  0.430, Egger’s test p = 0.02; the circles represent the weight of individual study.

https://doi.org/10.1371/journal.pone.0074543.g004

Discussion

A total of 47 eligible studies, including 19,810 cases and 23,485 controls were identified and analyzed in this meta-analysis. We demonstrated that the FASL -844T allele was associated with a statistically reduced risk of cancer. This significant association was found in Asians but not for Caucasians or Africans. With all published data, this finding might be plausible.

All controls in the studies involved were mainly cancer-free. The distribution of genotype in the control group was consistent with the Hardy-Weinberg equilibrium in all studies except for two studies (shown in Table 1). When excluding these two studies, the pooled OR and heterogeneity were not significantly changed indicating that the control group could represent the base population.

The FAS/FASL signaling system plays an important role in the cell apoptosis pathway, including regulation of immune system, maintaining immune-privileged ability and performing other regulatory functions [62], [63]. FAS is a cell surface receptor which expresses in various tissues [64], and FASL is the natural ligand to FAS [65]. The FAS combination with the FASL may trigger the death signal cascade, and subsequently leads cells to die. It has been reported that the aberration of expression of FAS and/or FASL results in cancer cells resisting the killing of T lymphocytes and is related to many human tumors [66], [67]. SNPs in a gene may influence its transcription or translation and eventually alter the biological function. The most popular polymorphism for FASL is a C to T changes at nucleotide position -844(rs763110) in the promoter region which may influence FASL expression, apoptosis signaling pathway, and ultimately contribute to the susceptibility to cancer. Numerous researches have proved that the FASL rs763110 polymorphism was associated with cancer risk (studies listed in Table 1). However, the results were controversial. Although meta-analyses of this polymorphism have been performed by the former scholars, in our present study, much more data were included and may get more comprehensive information.

With newly added studies, we carried out sub-group analyses. When stratified by ethnics, we found a significant association in Asians, but not for Caucasians or Africans, which may suggest that ethnic variation of genetic background would be modified by environmental factors [68], such as age, sex, diet, lifestyle, smoking, BMI, and so on. In our study, the frequency of the FASL-844T alleles was 28.4%, 36.9%, and 62.8% in Asians, Caucasians, and Africans respectively, and the -844 C to T mutant rate among them maybe different, which may count for the results stratified by ethnics. Furthermore, it was reported that studies with small size may have insufficient statistical power to investigate a slight effect on the pooled results or may produce a fluctuated risk estimate, which may cause the ethnic differences [69]. We could find that the participants of the three ethnicities differ greatly from each other in Table 2.

In the sub-group analysis by cancer type, no significant association was found except for heterozygote and dominant model comparison of ovarian cancer and cancers of head and neck (shown in Table 2). In our study, the cancers of head and neck subgroup consisted of cancers of oral cavity, pharynx and larynx, thyroid, and nasopharynx, and most of them were squamous cell carcinoma. Gastman and colleagues reported that the FAS/FASL pathway may participate in the immunosuppression process in head and neck cancer [70]. Based on our findings, we speculate that the -844C/T rs763110 polymorphism of FASL may be a potential genetic biomarker for risk of head and neck cancer. As for the significance of ovarian cancer, the relatively small sample size may weaken the statistical power and lead to the results considerable, so many case-control studies with more participants investigating ovarian cancer and the -844C/T rs763110 polymorphism are needed to prove the result. Different cancer risks were also found in the studies using different genotyping methods. We discovered that the association was significant among studies utilizing PCR-RFLP assay, but not for studies with Taqman genotyping assay. This may be explained that most studies for Asians utilizing PCR-RFLP, nevertheless Taqman was the main genotyping method for Caucasian and African studies.

Attention should be paid to the relatively large heterogeneity in our results. Meta-regression was performed for heterozygote model according to ethnicity, cancer type, source of control, genotyping method and sample size. We found the sources of heterogeneity were mainly from ethnicity (p = 0.029) and genotyping method (p = 0.043). In addition, through sub-group analysis by ethnicity, we found that I-squared for the Asian, Caucasian and African studies was 80.9%, 0.0% and 19.3%, respectively (Figure 2). Then we demonstrated that the heterogeneity might mainly come from the Asian studies. In fact, many other factors may also be the potential source of heterogeneity. Due to lack of detailed data, we had to give up performing a meta-regression utilizing these variables.

Some limitation should be noted in this meta-analysis. Firstly, the controls were not uniformly defined and some studies included inpatient with benign disease which may contribute to the FASL gene mutation and development of various cancers. Secondly, due to limited individual data, we did not conduct a more precise analysis on other covariates such as age, gender, and environmental factors. Thirdly, the heterogeneity is difficult to exclude, in that it is influenced by complicated factors, such as age, sex, genetic diversities, different lifestyle, and clinical characteristics.

Despite these limitations, our meta-analysis had significantly higher statistical power than the previous study that analyzed the association between the FASL -844C/T rs763110 polymorphism and cancer risk, since the cancer patients involved in our meta-analysis were twice as many as the previous one. We also analyzed the rs763110 polymorphism in various populations, including Africans. Cancer types in our study were more multifarious and a significant association was also found in cancers of head and neck and ovarian cancer though the sample size was relatively small.

In conclusion, this meta-analysis suggests that the FASL-844C/T rs763110 polymorphism is associated with a significantly reduced risk of cancer, especially in Asian populations. To verify these results, large scale case-control studies with detailed individual information are needed.

Supporting Information

[Figure omitted. See PDF.]

Checklist S1.

PRISMA checklist.

https://doi.org/10.1371/journal.pone.0074543.s001

(DOC)

File S1.

Subgroup analysis by sample size, source of control and genotyping method of ORs with a random-effects model for associations between FASL rs763110 polymorphism and cancer risk under heterozygote comparison (TC vs. CC).

https://doi.org/10.1371/journal.pone.0074543.s002

(DOCX)

Author Contributions

Conceived and designed the experiments: Lei Xu XZ Lin Xu. Performed the experiments: Lei Xu XZ FJ MTQ ZZ RY LX Lin Xu. Analyzed the data: Lei Xu XZ. Contributed reagents/materials/analysis tools: Lei Xu XZ FJ MTQ. Wrote the paper: Lei Xu XZ FJ.

Citation: Xu L, Zhou X, Jiang F, Qiu M-T, Zhang Z, Yin R, et al. (2013) FASL rs763110 Polymorphism Contributes to Cancer Risk: An Updated Meta-Analysis Involving 43,295 Subjects. PLoS ONE 8(9): e74543. https://doi.org/10.1371/journal.pone.0074543

References

1. Siegel R, Naishadham D, Jemal A (2012) Cancer statistics, 2012. CA Cancer J Clin 62: 10–29.

2. Jemal A, Bray F (2011) Center MM, Ferlay J, Ward E, et al (2011) Global cancer statistics. CA Cancer J Clin 61: 69–90.

3. Ferlay J, Shin HR, Bray F, Forman D, Mathers C, et al. (2010) Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008. Int J Cancer 127: 2893–2917.

4. Risch N, Merikangas K (1996) The future of genetic studies of complex human diseases. Science 273: 1516–1517.

5. Lichtenstein P, Holm NV, Verkasalo PK, Iliadou A, Kaprio J, et al. (2000) Environmental and heritable factors in the causation of cancer—analyses of cohorts of twins from Sweden, Denmark, and Finland. N Engl J Med 343: 78–85.

6. Lowe SW, Lin AW (2000) Apoptosis in cancer. Carcinogenesis 21: 485–495.

7. Evan GI, Vousden KH (2001) Proliferation, cell cycle and apoptosis in cancer. Nature 411: 342–348.

8. Thompson CB (1995) Apoptosis in the pathogenesis and treatment of disease. Science 267: 1456–1462.

9. Randhawa SR, Chahine BG, Lowery-Nordberg M, Cotelingam JD, Casillas AM (2010) Underexpression and overexpression of Fas and Fas ligand: a double-edged sword. Ann Allergy Asthma Immunol 104: 286–292.

10. Takahashi T, Tanaka M, Inazawa J, Abe T, Suda T, et al. (1994) Human Fas ligand: gene structure, chromosomal location and species specificity. Int Immunol 6: 1567–1574.

11. Wu J, Metz C, Xu X, Abe R, Gibson AW, et al. (2003) A novel polymorphic CAAT/enhancer-binding protein beta element in the FasL gene promoter alters Fas ligand expression: a candidate background gene in African American systemic lupus erythematosus patients. J Immunol 170: 132–138.

12. Wang J, Gao J, Li Y, Zhao X, Gao W, et al. (2013) Functional polymorphisms in FAS and FASL contribute to risk of squamous cell carcinoma of the larynx and hypopharynx in a Chinese population. Gene 524: 193–196.

13. Hashemi M, Fazaeli A, Ghavami S, Eskandari-Nasab E, Arbabi F, et al. (2013) Functional polymorphisms of FAS and FASL gene and risk of breast cancer - pilot study of 134 cases. PLoS One 8: e53075.

14. Li Y, Hao YL, Kang S, Zhou RM, Wang N, et al. (2013) Genetic polymorphisms in the Fas and FasL genes are associated with epithelial ovarian cancer risk and clinical outcomes. Gynecol Oncol 128: 584–589.

15. Karimi MY, Kapoor V, Sharma SC, Das SN (2013) Genetic polymorphisms in FAS (CD95) and FAS ligand (CD178) promoters and risk of tobacco-related oral carcinoma: gene-gene interactions in high-risk Indians. Cancer Invest 31: 1–6.

16. Wang W, Zheng Z, Yu W, Lin H, Cui B, et al. (2012) Polymorphisms of the FAS and FASL genes and risk of breast cancer. Oncol Lett 3: 625–628.

17. Mahfoudh W, Bouaouina N, Gabbouj S, Chouchane L (2012) FASL-844 T/C polymorphism: a biomarker of good prognosis of breast cancer in the Tunisian population. Hum Immunol 73: 932–938.

18. Tong N, Zhang L, Sheng X, Wang M, Zhang Z, et al. (2012) Functional polymorphisms in FAS, FASL and CASP8 genes and risk of childhood acute lymphoblastic leukemia: a case-control study. Leuk Lymphoma 53: 1360–1366.

19. Zhang W, Li C, Wang J, He C (2012) Functional polymorphisms in FAS/FASL system contribute to the risk of occurrence but not progression of gastric cardiac adenocarcinoma. Hepatogastroenterology 59: 141–146.

20. Kupcinskas J, Wex T, Bornschein J, Selgrad M, Leja M, et al. (2011) Lack of association between gene polymorphisms of Angiotensin converting enzyme, Nod-like receptor 1, Toll-like receptor 4, FAS/FASL and the presence of Helicobacter pylori-induced premalignant gastric lesions and gastric cancer in Caucasians. BMC Med Genet 12: 112.

21. Shao P, Ding Q, Qin C, Wang M, Tang J, et al. (2011) Functional polymorphisms in cell death pathway genes FAS and FAS ligand and risk of prostate cancer in a Chinese population. Prostate 71: 1122–1130.

22. Liu L, Wu C, Wang Y, Zhong R, Wang F, et al. (2011) Association of candidate genetic variations with gastric cardia adenocarcinoma in Chinese population: a multiple interaction analysis. Carcinogenesis 32: 336–342.

23. Cao Y, Miao XP, Huang MY, Deng L, Lin DX, et al. (2010) Polymorphisms of death pathway genes FAS and FASL and risk of nasopharyngeal carcinoma. Mol Carcinog 49: 944–950.

24. Zhu J, Qin C, Wang M, Yan F, Ju X, et al. (2010) Functional polymorphisms in cell death pathway genes and risk of renal cell carcinoma. Mol Carcinog 49: 810–817.

25. Kim HJ, Jin XM, Kim HN, Lee IK, Park KS, et al. (2010) Fas and FasL polymorphisms are not associated with acute myeloid leukemia risk in Koreans. DNA Cell Biol 29: 619–624.

26. Zhai R, Chen F, Liu G, Su L, Kulke MH, et al. (2010) Interactions among genetic variants in apoptosis pathway genes, reflux symptoms, body mass index, and smoking indicate two distinct etiologic patterns of esophageal adenocarcinoma. J Clin Oncol 28: 2445–2451.

27. Wang LH, Ting SC, Chen CH, Tsai CC, Lung O, et al. (2010) Polymorphisms in the apoptosis-associated genes FAS and FASL and risk of oral cancer and malignant potential of oral premalignant lesions in a Taiwanese population. J Oral Pathol Med 39: 155–161.

28. Qureshi A, Nan H, Dyer M, Han J (2010) Polymorphisms of FAS and FAS ligand genes and risk of skin cancer. J Dermatol Sci 58: 78–80.

29. Zhou RM, Wang N, Chen ZF, Duan YN, Sun DL, et al. (2010) Polymorphisms in promoter region of FAS and FASL gene and risk of cardia gastric adenocarcinoma. J Gastroenterol Hepatol 25: 555–561.

30. Chen XB, Chen GL, Liu JN, Yang JZ, Yu DK, et al. (2009) [Genetic polymorphisms in STK15 and MMP-2 associated susceptibility to esophageal cancer in Mongolian population]. Zhonghua Yu Fang Yi Xue Za Zhi 43: 559–564.

31. Chatterjee K, Engelmark M, Gyllensten U, Dandara C, van der Merwe L, et al. (2009) Fas and FasL gene polymorphisms are not associated with cervical cancer but differ among Black and Mixed-ancestry South Africans. BMC Res Notes 2: 238.

32. Wang M, Wu D, Tan M, Gong W, Xue H, et al. (2009) FAS and FAS ligand polymorphisms in the promoter regions and risk of gastric cancer in Southern China. Biochem Genet 47: 559–568.

33. Ter-Minassian M, Zhai R, Asomaning K, Su L, Zhou W, et al. (2008) Apoptosis gene polymorphisms, age, smoking and the risk of non-small cell lung cancer. Carcinogenesis 29: 2147–2152.

34. Yang M, Sun T, Wang L, Yu D, Zhang X, et al. (2008) Functional variants in cell death pathway genes and risk of pancreatic cancer. Clin Cancer Res 14: 3230–3236.

35. Hsu PI, Lu PJ, Wang EM, Ger LP, Lo GH, et al. (2008) Polymorphisms of death pathway genes FAS and FASL and risk of premalignant gastric lesions. Anticancer Res 28: 97–103.

36. Kang S, Dong SM, Seo SS, Kim JW, Park SY (2008) FAS -1377 G/A polymorphism and the risk of lymph node metastasis in cervical cancer. Cancer Genet Cytogenet 180: 1–5.

37. Crew KD, Gammon MD, Terry MB, Zhang FF, Agrawal M, et al. (2007) Genetic polymorphisms in the apoptosis-associated genes FAS and FASL and breast cancer risk. Carcinogenesis 28: 2548–2551.

38. Zhang H, Sun XF, Synnerstad I, Rosdahl I (2007) Importance of FAS-1377, FAS-670, and FASL-844 polymorphisms in tumor onset, progression, and pigment phenotypes of Swedish patients with melanoma: a case-control analysis. Cancer J 13: 233–237.

39. Ivansson EL, Gustavsson IM, Magnusson JJ, Steiner LL, Magnusson PK, et al. (2007) Variants of chemokine receptor 2 and interleukin 4 receptor, but not interleukin 10 or Fas ligand, increase risk of cervical cancer. Int J Cancer 121: 2451–2457.

40. Erdogan M, Karadeniz M, Berdeli A, Tamsel S, Ertan Y, et al. (2007) Fas/Fas ligand gene polymorphism in patients with papillary thyroid cancer in the Turkish population. J Endocrinol Invest 30: 411–416.

41. Gormus U, Ergen A, Yilmaz H, Dalan B, Berkman S, et al. (2007) Fas-1377A/G and FasL-844 T/C gene polymorphisms and epithelial ovarian cancer. Anticancer Res 27: 991–994.

42. Zhang B, Sun T, Xue L, Han X, Zhang B, et al. (2007) Functional polymorphisms in FAS and FASL contribute to increased apoptosis of tumor infiltration lymphocytes and risk of breast cancer. Carcinogenesis 28: 1067–1073.

43. Park SH, Choi JE, Kim EJ, Jang JS, Lee WK, et al. (2006) Polymorphisms in the FAS and FASL genes and risk of lung cancer in a Korean population. Lung Cancer 54: 303–308.

44. Zhang Z, Wang LE, Sturgis EM, El-Naggar AK, Hong WK, et al. (2006) Polymorphisms of FAS and FAS ligand genes involved in the death pathway and risk and progression of squamous cell carcinoma of the head and neck. Clin Cancer Res 12: 5596–5602.

45. Li C, Larson D, Zhang Z, Liu Z, Strom SS, et al. (2006) Polymorphisms of the FAS and FAS ligand genes associated with risk of cutaneous malignant melanoma. Pharmacogenet Genomics 16: 253–263.

46. Li C, Wu W, Liu J, Qian L, Li A, et al. (2006) Functional polymorphisms in the promoter regions of the FAS and FAS ligand genes and risk of bladder cancer in south China: a case-control analysis. Pharmacogenet Genomics 16: 245–251.

47. Yang S, Miao XP, Zhang XM, Sun T, Qu SN, et al. (2005) [Genetic polymorphisms of apoptosis-associated genes FAS and FASL and risk of colorectal cancer]. Zhonghua Yi Xue Za Zhi 85: 2132–2135.

48. Sun T, Zhou Y, Li H, Han X, Shi Y, et al. (2005) FASL -844C polymorphism is associated with increased activation-induced T cell death and risk of cervical cancer. J Exp Med 202: 967–974.

49. Lai HC, Lin WY, Lin YW, Chang CC, Yu MH, et al. (2005) Genetic polymorphisms of FAS and FASL (CD95/CD95L) genes in cervical carcinogenesis: An analysis of haplotype and gene-gene interaction. Gynecol Oncol 99: 113–118.

50. Zhang X, Miao X, Sun T, Tan W, Qu S, et al. (2005) Functional polymorphisms in cell death pathway genes FAS and FASL contribute to risk of lung cancer. J Med Genet 42: 479–484.

51. Krippl P, Langsenlehner U, Renner W, Koppel H, Samonigg H (2004) Re: Polymorphisms of death pathway genes FAS and FASL in esophageal squamous-cell carcinoma. J Natl Cancer Inst 96: 1478–1479; author reply 1479.

52. Sun T, Miao X, Zhang X, Tan W, Xiong P, et al. (2004) Polymorphisms of death pathway genes FAS and FASL in esophageal squamous-cell carcinoma. J Natl Cancer Inst 96: 1030–1036.

53. Zhang M, Ma L, Xu C, Wang Y (2011) The relationship between FAS-670 and FASL -884 gene polymorphism and the susceptibility to gastric cancer. Journal of Shangdong University (Health Sciences) 49: 107–109.

54. Zhang M, Xu C, Yang H, Li K (2010) Correlation study between FASL -844 gene polymorphism and esophageal carcinoma susceptibility. Medical Laboratory and Clinics 21: 1–3,6.

55. Liu J, Li S, Shi W, Cheng X (2012) Genetic polymorphism FAS/FASL system and their association with the risk of gastic cancer Chinese Journal of Health & Heredity. 20: 28–30.

56. Liu Y, Wen QJ, Yin Y, Lu XT, Pu SH, et al. (2009) FASLG polymorphism is associated with cancer risk. Eur J Cancer 45: 2574–2578.

57. Zhang Z, Qiu L, Wang M, Tong N, Li J, et al. (2009) The FAS ligand promoter polymorphism, rs763110 (-844C>T), contributes to cancer susceptibility: evidence from 19 case-control studies. Eur J Hum Genet 17: 1294–1303.

58. Guo SW, Thompson EA (1992) Performing the exact test of Hardy-Weinberg proportion for multiple alleles. Biometrics 48: 361–372.

59. Lau J, Ioannidis JP, Schmid CH (1997) Quantitative synthesis in systematic reviews. Ann Intern Med 127: 820–826.

60. DerSimonian R, Laird N (1986) Meta-analysis in clinical trials. Control Clin Trials 7: 177–188.

61. Egger M, Davey Smith G, Schneider M, Minder C (1997) Bias in meta-analysis detected by a simple, graphical test. Bmj 315: 629–634.

62. Los M, Wesselborg S, Schulze-Osthoff K (1999) The role of caspases in development, immunity, and apoptotic signal transduction: lessons from knockout mice. Immunity 10: 629–639.

63. Rashedi I, Panigrahi S, Ezzati P, Ghavami S, Los M (2007) Autoimmunity and apoptosis–therapeutic implications. Curr Med Chem 14: 3139–3151.

64. Los M, Van de Craen M, Penning LC, Schenk H, Westendorp M, et al. (1995) Requirement of an ICE/CED-3 protease for Fas/APO-1-mediated apoptosis. Nature 375: 81–83.

65. Villa-Morales M, Fernandez-Piqueras J (2012) Targeting the Fas/FasL signaling pathway in cancer therapy. Expert Opin Ther Targets 16: 85–101.

66. Lin Y, Liu L, Zhang T, Liu J (2013) Functional investigation of Fas ligand expressions in human non-small cell lung cancer cells and its clinical implications. Ann Thorac Surg 95: 412–418.

67. Wu SF, Zhang JW, Qian WY, Yang YB, Liu Y, et al. (2012) Altered expression of survivin, Fas and FasL contributed to cervical cancer development and metastasis. Eur Rev Med Pharmacol Sci 16: 2044–2050.

68. Hirschhorn JN, Lohmueller K, Byrne E, Hirschhorn K (2002) A comprehensive review of genetic association studies. Genet Med 4: 45–61.

69. Wacholder S, Chanock S, Garcia-Closas M, El Ghormli L, Rothman N (2004) Assessing the probability that a positive report is false: an approach for molecular epidemiology studies. J Natl Cancer Inst 96: 434–442.

70. Gastman BR, Atarshi Y, Reichert TE, Saito T, Balkir L, et al. (1999) Fas ligand is expressed on human squamous cell carcinomas of the head and neck, and it promotes apoptosis of T lymphocytes. Cancer Res 59: 5356–5364.