Abstract
We conducted a case-control study in a Chinese population, and investigated the association between four SNPs (rs3791679, rs1346786, rs1344733 and rs727878) in EFEMP1 and development of glioma. A case-control study was taken in the present study. The rs3791679, rs1346786, rs1344733 and rs727878 gene polymorphisms were analyzed using a polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) assay. A total of 159 patients with glioma and 364 controls were collected between July 2012 and June 2014. By unconditional logistic regression analysis, we found that individuals carrying the AA genotype and GA+AA genotype were associated with development of glioma when compared with the GG genotype, and the adjusted ORs (95% CI) were 2.13 (1.15-3.90) and 1.55 (1.04-2.32), respectively. However, we did not find that rs1346786, rs1344733 and rs727878 were significantly associated with development of glioma. Moreover, we found that the GA+AA genotype of rs3791679 was associations with a heavy increased risk of glioma in patients who have family history of cancers, and the OR (95% CI) was 6.81 (1.17-48.06). The results of our study suggested an association between the rs3791679 polymorphism and an elevated risk of glioma, especially in those with family history of glioma.
Keywords: EFEMP1, single nucleotide polymorphisms, glioma
Introduction
Gliomas are central nervous system neoplasms derived from glial cells, and glioma is the most frequently type of brain tumors worldwide. Gliomas account for more than 70% of all malignant brain tumors [1]. It is reported that about 80% of patients with gliomas die within one year after initial diagnosis [2]. Many environmental and lifestyle factors including several occupations, Ionizing radiation, cellular phones, smoking, and diet have been considered to be associated with an increased glioma risk [3,4]. However, the mechanisms underlying glioma tumorigenesis remain poorly understood. Not all individuals who are exposed to high doses of ionizing radiation and other risk factors of gliomas developed gliomas [5], which suggest that genetic factors may contribute to the development of glioma. Increasing evidences have reported that inherited risks may play an important role in the susceptibility to glioma, such as XRCC1, LIG4, XRCC4, PTGS2, ERCC1, ERCC2 and TGF-β1 [6-10].
EFEMP1 is located in chromosome 2 and encodes a member of the fibulin family of extracellular matrix glycoproteins. Like all members of this family, the encoded protein contains tandemly repeated epidermal growth factor-like repeats followed by a C-terminus fibulin-type domain. Different members of the fibulin family showed different functions, either tumor-suppressive or oncogenic activity. The EFEMP1 may play a role in the nature of many malignant tumors and interacts with its partners and modulates their functions [11,12]. So far, only one study reported the association between EFEMP1 variations and risk of glioma [13]. Therefore, we conducted a case-control study in a Chinese population, and investigated the association between four SNPs (rs3791679, rs1346786, rs1344733 and rs727878) in EFEMP1 and development of glioma.
Material and methods
Patients
A case-control study was taken in the present study. A total of 159 patients who were histopathologically diagnosed to be glioma were collected at Nanfang Hospital between July 2012 and June 2014. The tumors were graded according to the World Health Organization (WHO) classification.
A group of 364 control subjects was randomly selected from the trauma outpatients and the annual check-up visitors in our hospital during July 2012 and June 2014. All the control subjects were free of glioma. The controls with a self-reported history of cancer or central nervous system-related diseases and previously receiving radiotherapy and chemotherapy for certain diseases were excluded from this hospital.
At recruitment, all participants were interviewed by trained nurses to collect detailed demographic information, such as smoking, drinking and family history of cancer. The clinical characteristics of patients with glioma were collected from medical records, such as histology types and tumor grade. Smoking status was based on self-reported smoking, and the subjects who have never smoked less than 100 cigarettes in their lives were classified as non-smokers. Drinking status was defined as non-drinker and drinker (drinks per day).
Blood samples and signed informed consent forms were obtained from enrolled individuals prior to their participation in the study. The study protocol was approved by the Clinical Research Ethics Committee of the Jiujiang First People’s Hospital.
DNA extraction and genotyping
5 ml blood sample was collected from each patient with glioma and control subject, and the blood samples were collected in ethylene diamine tetra-acetic acid (EDTA)-coated tubes and stored at -20°C until use. The rs3791679, rs1346786, rs1344733 and rs727878 gene polymorphisms were analyzed using a polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) assay. The primers of rs3791679, rs1346786, rs1344733 and rs727878 were designed using the Sequenom Assay Design 3.1 software. The reaction conditions were performed as follows: one cycle of DNA denaturation at 94°C for 5 min, followed by 30 cycles of denaturation at 94°C for 1 min, 55°C annealing step for 1 min with a 72°C extension step for 2 min, with a final extension step of 5 min at 72°C. The PCR products were analyzed by electrophoresis in a 2% agarose gel stained with ethidium bromide and visualized under UV light. For quality control, the genotyping analysis was done blind as regards the subjects.
Statistical analysis
The demographic and clinical characteristics of patients with glioma and control subjects were expressed by mean ± standard deviation or frequency and percentage. The differences between groups were compared by the t-test and chi-square test. The goodness-of-fit χ2-test was taken to analyze the departures from the Hardy-Weinberg equilibrium (HWE) of genotype distributions in rs3791679, rs1346786, rs1344733 and rs727878. Unconditional logistic regression analysis was taken to analyze the association between rs3791679, rs1346786, rs1344733 and rs727878 polymorphisms and development of glioma, and the results were evaluated using the Odd’s ratio (OR) and 95% confidence interval (95% CI). The major homozygous genotypes of the rs3791679, rs1346786, rs1344733 and rs727878 polymorphism were used as references. The interaction between gene polymorphism and environmental factors in the risk of glioma was analyzed using multiple logistic regression analysis. Statistical analysis was conducted using the SPSS 17.0 package (SPSS Inc., Chicago, IL, USA). P < 0.05 was considered to indicate a significant difference.
Results
The demographic and clinical characteristics of patients with glioma and control subjects are summarized in Table 1. The mean ages of patients with glioma and control subjects were 57.32±11.70 and 55.14±12.10, respectively. By a comparison of the demographic characteristics between patients and controls, we found no significant difference between the two groups. Of the 159 patients with glioma, 52 (32.70%) patients were glioblastoma, 107 (67.30%) were astrocytoma; oligodendroglioma and mixed glioma, 66 (41.51%) were at I-II tumor stage and 93 (58.49%) were at III-IV tumor stage.
Table 1.
Characteristics of patients with glioma and control subjects
| Parameters | Patients | % | Controls | % | χ2-test | P value |
|---|---|---|---|---|---|---|
| Age, years | 57.32±11.70 | 55.14±12.10 | ||||
| Gender | ||||||
| Female | 63 | 39.62 | 157 | 43.13 | ||
| Male | 96 | 60.38 | 207 | 56.87 | 0.56 | 0.46 |
| Smoking status | ||||||
| Never | 100 | 62.89 | 238 | 65.38 | ||
| Ever | 59 | 37.11 | 126 | 34.62 | 0.30 | 0.58 |
| Drinking status | ||||||
| Never | 101 | 63.52 | 247 | 67.86 | ||
| Ever | 58 | 36.48 | 117 | 32.14 | 0.93 | 0.33 |
| Family history of cancer | ||||||
| No | 145 | 91.19 | 344 | 94.51 | ||
| Yes | 14 | 8.81 | 20 | 5.49 | 1.20 | 0.16 |
| Histology type | ||||||
| Glioblastoma | 52 | 32.70 | ||||
| Astrocytoma; oligodendroglioma and mixed glioma | 107 | 67.30 | ||||
| WHO | ||||||
| I-II | 66 | 41.51 | ||||
| III-IV | 93 | 58.49 |
The information of the four common SNPs in EFEMP1 was shown in Table 2. By χ2-test, we found a significant difference in the genotype distribution of rs3791679 between patients and controls (χ2=7.37, P=0.03). By the goodness-of-fit χ2-test, we found that the genotype distributions of rs3791679, rs1346786, rs1344733 and rs727878 were in line with Hardy-Weinberg equilibrium, and the P values were 0.17, 0.76, 0.83 and 0.80, respectively (Table 2). Moreover, the minor allele frequencies of rs3791679, rs1346786, rs1344733 and rs727878 were similar with those in dbSNP databases.
Table 2.
Distributions of EFEMP1 polymorphisms and development of glioma
| SNPs | Patients | % | Controls | % | χ2-test | P value | Chromosome position | SNP location | Base change | Minor allele frequency | P for HWE | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
| ||||||||||||
| In dbSNP database | In controls | |||||||||||
| rs3791679 | ||||||||||||
| GG | 58 | 36.48 | 171 | 46.98 | 7.37 | 0.03 | 55950396 | Intron10 | G>A | 0.2919 | 0.3187 | 0.17 |
| GA | 73 | 45.91 | 154 | 42.31 | ||||||||
| AA | 28 | 17.61 | 39 | 10.71 | ||||||||
| rs1346786 | ||||||||||||
| AA | 51 | 32.08 | 129 | 35.44 | 0.54 | 0.76 | 55961837 | Intron5 | A>G | 0.4569 | 0.4148 | 0.76 |
| AG | 76 | 47.80 | 168 | 46.15 | ||||||||
| GG | 32 | 20.12 | 67 | 18.41 | ||||||||
| rs1344733 | ||||||||||||
| AA | 55 | 34.59 | 136 | 37.36 | 0.38 | 0.83 | 55981531 | Intron4 | A>G | 0.4299 | 0.3984 | 0.83 |
| AG | 75 | 47.17 | 166 | 45.61 | ||||||||
| GG | 29 | 18.24 | 62 | 17.03 | ||||||||
| rs727878 | ||||||||||||
| AA | 53 | 33.33 | 132 | 36.26 | 0.44 | 0.8 | 55973161 | Intron4 | A>G | 0.4593 | 0.4121 | 0.80 |
| AG | 74 | 46.54 | 164 | 45.06 | ||||||||
| GG | 32 | 20.13 | 68 | 18.68 | ||||||||
By unconditional logistic regression analysis, we found that individuals carrying the AA genotype and GA+AA genotype were associated with development of glioma when compared with the GG genotype, and the adjusted ORs (95% CI) were 2.13 (1.15-3.90) and 1.55 (1.04-2.32), respectively (Table 3). However, we did not find that rs1346786, rs1344733 and rs727878 were significantly associated with development of glioma.
Table 3.
Association between studies SNPs and development of glioma
| SNP | Patients | % | Controls | % | OR (95% CI) | P value |
|---|---|---|---|---|---|---|
| rs3791679 | ||||||
| GG | 58 | 36.48 | 171 | 46.98 | 1.0 (Ref.) | - |
| GA | 73 | 45.91 | 154 | 42.31 | 1.41 (0.92-2.16) | 0.11 |
| AA | 28 | 17.61 | 39 | 10.71 | 2.13 (1.15-3.90) | 0.01 |
| GA+AA | 101 | 63.52 | 193 | 53.02 | 1.55 (1.04-2.32) | 0.02 |
| rs1346786 | ||||||
| AA | 51 | 32.08 | 129 | 35.44 | 1.0 (Ref.) | - |
| AG | 76 | 47.80 | 168 | 46.15 | 1.14 (0.73-1.78) | 0.56 |
| GG | 32 | 20.12 | 67 | 18.41 | 1.20 (0.68-2.10) | 0.5 |
| AG+GG | 108 | 67.93 | 235 | 64.56 | 1.15 (0.76-1.75) | 0.48 |
| rs1344733 | ||||||
| AA | 55 | 34.59 | 136 | 37.36 | 1.0 (Ref.) | - |
| AG | 75 | 47.17 | 166 | 45.60 | 1.18 (0.72-1.73) | 0.6 |
| GG | 29 | 18.24 | 62 | 17.04 | 1.16 (0.65-2.05) | 0.6 |
| AG+GG | 104 | 65.41 | 228 | 62.63 | 1.12 (0.75-1.70) | 0.54 |
| rs727878 | ||||||
| AA | 53 | 33.33 | 132 | 36.26 | 1.0 (Ref.) | - |
| AG | 74 | 46.54 | 164 | 45.06 | 1.12 (0.72-1.75) | 0.59 |
| GG | 32 | 20.13 | 68 | 18.68 | 1.17 (0.67-2.05) | 0.56 |
| AG+GG | 106 | 66.67 | 232 | 63.73 | 1.14 (0.76-1.72) | 0.52 |
By stratification analysis, we found that the GA+AA genotype of rs3791679 was associations with a heavy increased risk of glioma in patients who have family history of cancers, and the OR (95% CI) was 6.81 (1.17-48.06) (Table 4). However, we did not find significant association of rs3791679 polymorphism with smoking status and drinking status in the risk of glioma.
Table 4.
Stratification analysis of rs3791679 polymorphism in the development of glioma
| Variables | Patients | Controls | OR (95% CI) | P value | ||
|---|---|---|---|---|---|---|
|
|
||||||
| GG | GA+AA | GG | GA+AA | |||
| Smoking status | ||||||
| Never | 38 | 62 | 112 | 126 | 1.45 (0.88-2.41) | 0.13 |
| Ever | 20 | 39 | 59 | 67 | 1.77 (0.89-3.57) | 0.08 |
| Drinking status | ||||||
| Never | 37 | 64 | 114 | 133 | 1.48 (0.90-2.46) | 0.1 |
| Ever | 21 | 37 | 57 | 60 | 1.73 (0.87-3.50) | 0.09 |
| Family history of cancer | ||||||
| No | 55 | 90 | 159 | 185 | 1.41 (0.93-2.14) | 0.09 |
| Yes | 3 | 11 | 12 | 8 | 6.81 (1.17-48.06) | 0.01 |
Discussion
It is well known that individuals may not develop the same type of cancer despite being exposed to similar environmental conditions. Therefore, genetic variations may play an important role in the development of cancers. In the present study, we conducted a case-control study to investigate the association between four common SNPs in EFEMP1 and development of glioma in a Chinese population, and we found that the rs3791679 polymorphism was associated with susceptibility to glioma.
EGF-containing fibulin-like extracellular matrix protein1 is also called EFEMP1. Fibulins are a seven-member family of secreted glycoproteins, and they have a role in repeating epidermal growth-factor-like domains and a unique C-terminal structure [12]. Glycoproteins include a complex network structure, support and connect the organizational structure, regulate tissue and cell physiological activity, and thus contribute the formation and development of cancers [12]. Previous studies have reported that the members of the fibulin family have a role in tumor-suppressive and oncogenic activity [11,12].
Currently, many studies have reported the association between low expression of EFEMP1 and development of cancers, such as prostate cancer, hepatocellular carcinoma and non-small cell lung cancer [14-17]. Almeida et al. reported that EFEMP1 promoter methylation is associated with development of prostate cancer, and this epigenetic alteration of EFEMP1 is associated with prostate carcinogenesis [14]. Nomoto et al. have reported that downregulation of EFEMP1 expression was associated with promoter hypermethylation, and was a marker of worse prognosis in hepatocellular carcinoma [15]. Lang et al. have reported that EFEMP1 has a role in suppressing the lung cancer growth and invasion [17]. However, some studies reported that the high expression of EFEMP1 was associated with development of breast cancer and pancreatic cancers [18,19]. En-lin et al. have reported that over expression was significantly correlated with lymph node metastasis, vascular invasion and poor survival of cervical cancer [18]. Seeliger et al. over-expression of EFEMP1 has protumorigenic effects on pancreatic cancer in vivo and in vitro [19]. These previous studies have suggested that gene expression of EFEMP1 was associated with the development and prognosis of cancers.
For the association between EFEMP1 polymorphisms and susceptibility to glioma, only one previous study reported their association [13]. Zhang et al. conducted a study in a Chinese population, and they found that EFEMP1 rs3791679, rs134678, rs1346787 and rs3791675 polymorphisms contributed to the susceptibility of glioma [13]. In our study, we reported that rs3791679 polymorphism was associated with the development of glioma, but no significant association was found between rs1346786, rs1344733 and rs727878 polymorphisms and susceptibility to glioma. Moreover, our study found that rs3791679 has interaction with family history of cancer in the glioma risk, which suggests that rs3791679 may be an inherited factor for the development of glioma.
Two limitations in our study should be taken into consideration. First, patients with glioma and control subjects were collected from the same hospital, which would cause selection bias in our study. However, the genotype distributions of rs3791679, rs1346786, rs1344733 and rs727878 in EFEMP1 did not deviated from the Hardy-Weinberg equilibrium, which suggests that the study participants could represent the general population. Second, the sample size of the glioma patients is small, which could results in a lack of statistical power. Therefore, further studies with more sample size and more ethnicities are needed to confirm our results.
In conclusion, the results of our study suggested an association between the rs3791679 polymorphism and an elevated risk of glioma; in addition, rs3791679 interacts with the family history of cancer in contributing to the development of glioma. Future studies using larger sample sizes, and employing either similar or different analytical strategies may help in elucidating the impact of EFEMP1 gene polymorphisms on the risk of glioma.
Disclosure of conflict of interest
None.
References
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