Disease Markers
Volume 2018 (2018), Article ID 7565834, 12 pages
https://doi.org/10.1155/2018/7565834

Review Article

A Matrix Metalloproteinase-1 Polymorphism, MMP1–1607 (1G>2G), Is Associated with Increased Cancer Risk: A Meta-Analysis Including 21,327 Patients

Zhonghan Zhou, Xiaocheng Ma, Fangming Wang, Lijiang Sun, and Guiming Zhang

Department of Urology, The Affiliated Hospital of Qingdao University, Qingdao, China

Correspondence should be addressed to Lijiang Sun; slijiang999@126.com and Guiming Zhang; zhangguiming9@126.com

Received 27 June 2018; Revised 23 September 2018; Accepted 26 September 2018; Published 2 December 2018

Academic Editor: Michael Hawkes

Copyright © 2018 Zhonghan Zhou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

Although the matrix metalloproteinase-1 (MMP1) polymorphism MMP1–1607 (1G>2G) has been associated with susceptibility to various cancers, these findings are controversial. Therefore, we conducted this meta-analysis to explore the association between MMP1–1607 (1G>2G) and cancer risk. A systematic search of literature through PubMed, Embase, ISI Web of Knowledge, and Google Scholar yielded 77 articles with 21,327 cancer patients and 23,245 controls. The association between the MMP1–1607 (1G>2G) polymorphism and cancer risks was detected in an allele model (2G vs. 1G, overall risk [OR]: 1.174, 95% confidence interval [CI]: 1.107–1.244), a dominant model (2G2G/1G2G vs. 1G1G OR, OR: 1.192, 95% CI: 1.090–1.303), and a recessive model (2G2G vs. 1G2G/1G1G, OR: 1.231, 95% CI: 1.141–1.329). In subgroup analysis, these associations were detected in both Asians and Caucasians. After stratification by cancer types, associations were found in lung, colorectal, nervous system, renal, bladder, and nasopharyngeal cancers. This meta-analysis revealed that MMP1–1607 (1G>2G) polymorphism was significantly associated with elevated risk of cancers.

1. Introduction

Single-nucleotide polymorphisms (SNP) are variations in single nucleotides that occur at specific positions in the genome and influence protein structure, gene splicing, transcription factor binding, messenger RNA degradation, or sequences of noncoding RNAs [1]. SNPs reportedly contribute to interindividual variability in susceptibility to common diseases such as cancer.

Matrix metalloproteinases (MMPs) are a group of proteolytic enzymes that can degrade extracellular matrix components, thereby affecting various physiological and pathological processes such as embryonic development, wound healing, arthritis, atherosclerosis, and tumor progression [2]. Increasing evidence shows that MMPs play significant roles in cancer development, including cell growth, differentiation, apoptosis, angiogenesis, invasion, and metastasis [3].

MMP1, a member of the MMP family, can degrade interstitial collagen types I, II, and III, clearing a path for cancer cells to invade matrix barriers and migrate through tissue stroma [4]. The MMP1 gene is located at 11q22.3, and MMP1 expression can be regulated by the MMP1 promoter. The gene polymorphism MMP1–1607 (1G>2G) or rs1799750 in the MMP1 promoter has been associated with increased susceptibility for various cancers [5, 6]. However, the results were controversial because of variations in cancer types and patient demographics. Therefore, we conducted this meta-analysis to further explore the association between MMP1–1607 (1G>2G) polymorphism and cancer susceptibility.

2. Materials and Methods

2.1. Identification and Eligibility of Studies

We conducted a systematic search of literature published until December 2017 that investigated the association of MMP1–1607 (1G>2G) polymorphism with cancer risks, through PubMed, Embase, ISI Web of Knowledge, and Google Scholar, using the terms “Matrix metalloproteinase-1 or MMP-1 or rs1799750,” “polymorphism or variation or mutation or SNP,” and “cancer or carcinoma or tumor or neoplasm.” Only case–control studies with sufficient genotype distribution data to calculate odds ratios (ORs) with 95% confidence interval (CIs) in different gene models were included. Letters, case reports, animal studies, and reviews were excluded. When overlapping populations were included in different articles, only the publication with the largest sample size was selected.

2.2. Data Extraction

Two investigators independently reviewed the articles to exclude irrelevant and overlapping studies. The following data were extracted from eligible publications: first author, published year, cancer type, country, ethnicity, control source, genotyping method, and genotype distribution. Any disagreements were resolved by discussion or by consultation with another investigator.

2.3. Statistical Analysis

The meta-analysis was conducted using SATAT (version 13.0). The Hardy–Weinberg equilibrium (HWE) for control groups was checked by the chi-square goodness-of-fit test () The associations between MMP1–1607 (1G>2G) polymorphism and cancer risks were calculated by OR and 95% CI with the following models to avoid assuming only one suboptimal genetic model: an allele model (2G vs. 1G), a dominant model (2G2G/1G2G vs. 1G1G), and a recessive model (2G2G vs. 1G2G/1G1G). Subgroup analyses were performed by cancer type and ethnicity.

The heterogeneity of studies was assessed by test using value and value. A fixed-effects model was adopted when test indicated a lack of heterogeneity (); otherwise, a random-effects model was used. We considered 0–40% of value to indicate low heterogeneity, 30–60% to indicate moderate heterogeneity, 50–90% to indicate substantial heterogeneity, and 75–100% to indicate considerable heterogeneity. Publication bias was measured with funnel plots and Harbord’s and Peter’s tests.

3. Results

3.1. Characteristics of Eligible Studies

The study selection procedure is shown in Figure 1. We included 77 articles with 21,327 cancer patients and 23,245 controls in this meta-analysis (Table 1) [7–83]. Of these, 43 articles were conducted among Asian populations and 34 among Caucasian populations; 67 studies were hospital-based and 10 were population-based. Of the different genotyping methods used in these studies, 45 used polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP), 18 used TaqMan real-time PCR, 8 used sequencing, and 6 used other methods. Sixteen of the 77 articles showed deviations from HWE in control groups.

Figure 1: Flow chart of study selection procedure.

Table 1: The main characteristics of studies included in the meta-analysis.

Author
Year
Cancer type
Country
Ethnicity
Control
Genotype
of case
of control
HWE (P)

Kanamori et al. [7]
1999
Ovarian cancer
Japan
Asian
HB
PCR-RFLP
163
150
0.033

Biondi et al. [8]
2000
Other cancer
Italy
Caucasian
HB
TaqMan
160
164
0.813

Nishioka et al. [9]
2000
Endometrial cancer
Japan
Asian
HB
Sequencing
100
150
0.033

Ye et al. [10]
2001
Cutaneous melanoma
England
Caucasian
HB
TaqMan
139
132
0.849

Zhu et al. [11]
2001
Lung cancer
America
Caucasian
HB
PCR-RFLP
456
451
0.028

Ghilardi et al. [12]
2002
Breast cancer
America
Caucasian
HB
Sequencing
86
110
0.652

Hinoda et al. [13]
2002
Colorectal cancer
Japan
Asian
PB
PCR-RFLP
101
127
0.949

Hirata et al. [14]
2003
Renal cell cancer
Japan
Asian
HB
Sequencing
119
210
0.993

Nishioka et al. [15]
2003
Endometrial cancer
Japan
Asian
HB
Sequencing
109
150
0.033

Wenham et al. [16]
2003
Ovarian cancer
America
Caucasian
PB
TaqMan
311
387
0.536

Hashimoto et al. [17]
2004
Head and neck cancer
Japan
Asian
HB
PCR-RFLP
140
223
0.852

Hirata et al. [18]
2004
Renal cell cancer
Japan
Asian
PB
PCR-RFLP
156
230
0.871

Lin et al. [19]
2004
Oral cancer
Taiwan
Asian
HB
Sequencing
121
147
0.336

Matsumura et al. [20]
2004
Gastric cancer
Japan
Asian
HB
PCR-RFLP
215
166
0.432

Zinzindohoué et al. [21]
2004
Head and neck cancer
France
Caucasian
HB
PCR-RFLP
125
249
0.978

Fang et al. [22]
2005
Lung cancer
China
Asian
HB
PCR-RFLP
243
350
0.000

Jin et al. [23]
2005
Gastric cancer
China
Asian
HB
PCR-RFLP
417
350
0.000

Ju et al. [24]
2005
Cervical cancer
Korea
Asian
HB
TaqMan
232
332
0.695

Lai et al. [25]
2005
Cervical cancer
Taiwan
Asian
HB
Other
197
197
1.000

McCready et al. [26]
2005
Glioblastoma
America
Caucasian
HB
PCR-RFLP
81
57
0.916

Cao and Li [27]
2006
Oral cancer
China
Asian
HB
PCR-RFLP
96
120
0.657

Elander et al. [28]
2006
Colorectal cancer
Sweden
Caucasian
HB
Other
127
208
0.918

Kader et al. [29]
2006
Bladder cancer
America
Caucasian
HB
TaqMan
556
555
0.565

Li et al. [30]
2006
Ovarian cancer
China
Asian
HB
PCR-RFLP
122
151
0.008

Lièvre et al. [31]
2006
Colorectal cancer
France
Caucasian
HB
Other
591
561
0.900

O-charoenrat et al. [32]
2006
Head and neck cancer
Thailand
Asian
HB
PCR-RFLP
300
300
0.988

Su et al. [33]
2006
Lung cancer
America
Caucasian
PB
TaqMan
2014
1323
0.597

Sugimoto et al. [34]
2006
Endometrial cancer
Japan
Asian
HB
PCR-RFLP
107
213
0.768

Xu et al. [35]
2006
Colorectal cancer
China
Asian
HB
Other
126
126
0.938

Albayrak et al. [36]
2007
Prostate cancer
Turkey
Caucasian
HB
PCR-RFLP
55
43
0.000

Ju et al. [37]
2007
Ovarian cancer
Korea
Asian
HB
TaqMan
133
332
0.695

Lei et al. [38]
2007
Breast cancer
Sweden
Caucasian
PB
TaqMan
954
947
0.151

Lu et al. [39]
2007
Other cancer
China
Asian
HB
PCR-RFLP
221
366
0.000

Nasr et al. [40]
2007
Nasopharyngeal cancer
Tunisia
Caucasian
HB
PCR-RFLP
174
171
0.091

Nishizawa et al. [41]
2007
Oral cancer
Japan
Asian
HB
TaqMan
170
164
0.493

Piccoli et al. [42]
2007
Renal cell carcinoma
Brazil
Caucasian
PB
PCR-RFLP
99
118
1.000

Vairaktaris et al. [43]
2007
Oral cancer
Greek
Caucasian
HB
PCR-RFLP
156
141
0.276

Woo et al. [44]
2007
Colorectal cancer
Korea
Asian
HB
PCR-RFLP
185
304
0.488

Zhai et al. [45]
2007
Hepatocellular cancer
China
Asian
HB
Sequencing
431
479
0.559

Zhou et al. [46]
2007
Nasopharyngeal cancer
China
Caucasian
PB
Sequencing
829
759
0.634

Dos Reis et al. [47]
2008
Prostate cancer
Brazil
Caucasian
PB
TaqMan
100
100
0.293

González-Arriaga et al. [48]
2008
Lung cancer
Spain
Caucasian
HB
PCR-RFLP
501
510
0.934

Kouhkan et al. [49]
2008
Colorectal cancer
Iran
Asian
HB
PCR-RFLP
150
100
0.935

Shimizu et al. [50]
2008
Tongue cancer
Japan
Asian
HB
TaqMan
69
91
0.585

Tasci et al. [51]
2008
Bladder cancer
Turkey
Caucasian
HB
PCR-RFLP
102
94
0.740

Bradbury et al. [52]
2009
Esophageal cancer
America
Caucasian
HB
TaqMan
313
450
0.508

de Lima et al. [53]
2009
Colorectal cancer
Brazil
Caucasian
HB
PCR-RFLP
108
108
0.258

dos Reis et al. [54]
2009
Prostate cancer
Brazil
Caucasian
HB
TaqMan
100
100
0.293

Ricketts et al. [55]
2009
Renal cell cancer
Polish
Caucasian
HB
TaqMan
323
314
0.847

Srivastava et al. [56]
2010
Bladder cancer
India
Asian
HB
PCR-RFLP
200
200
0.190

Tsuchiya et al. [57]
2009
Prostate cancer
Japan
Asian
PB
Sequencing
283
251
0.285

Vairaktaris et al. [58]
2009
Oral cancer
Greek
Caucasian
HB
PCR-RFLP
156
141
0.276

Altaş et al. [59]
2010
Other cancer
Turkey
Caucasian
HB
PCR-RFLP
30
30
0.195

Chaudhary et al. [60]
2010
Head and neck cancer
India
Asian
HB
PCR-RFLP
422
426
0.240

Fang et al. [61]
2010
Colorectal cancer
China
Asian
HB
PCR-RFLP
237
252
0.683

Okamoto et al. [62]
2010
Hepatocellular cancer
Japan
Asian
HB
PCR-RFLP
91
82
0.009

Hart et al. [63]
2011
Lung cancer
Norway
Caucasian
PB
TaqMan
436
434
0.218

Liu et al. [64]
2011
Lung cancer
China
Asian
HB
PCR-RFLP
825
825
0.924

Malik et al. [65]
2011
Glioblastoma
India
Asian
HB
PCR-RFLP
110
150
0.433

Wang et al. [66]
2011
Cutaneous melanoma
America
Caucasian
HB
TaqMan
864
849
0.940

Cheung et al. [67]
2012
Esophageal cancer
Canada
Caucasian
HB
TaqMan
309
279
0.974

Enewold et al. [68]
2012
Lung cancer
America
Caucasian
HB
Other
71
147
0.743

Fakhoury et al. [69]
2012
Lung cancer
Lebanon
Caucasian
HB
PCR-RFLP
41
51
0.218

Wieczorek et al. [70]
2013
Bladder cancer
Poland
Caucasian
HB
TaqMan
240
199
0.022

Brzóska et al. [71]
2014
Lung cancer
Poland
Caucasian
HB
PCR-RFLP
53
54
0.264

Dedong et al. [72]
2014
Gastric cancer
China
Asian
HB
Other
422
428
0.546

Devulapalli et al. [73]
2014
Gastric cancer
India
Asian
HB
PCR-RFLP
166
202
0.000

Dey et al. [74]
2014
Gastric cancer
India
Caucasian
HB
PCR-RFLP
145
145
0.850

Guan et al. [75]
2014
Esophageal cancer
China
Asian
HB
PCR-RFLP
132
132
0.989

Kawal et al. [76]
2016
Breast cancer
Taiwan
Asian
HB
PCR-RFLP
1232
1232
0.004

Pei et al. [77]
2016
Other cancer
Taiwan
Asian
HB
PCR-RFLP
266
266
0.258

Su et al. [78]
2016
Breast cancer
Taiwan
Asian
HB
PCR-RFLP
1232
1232
0.004

Sun et al. [79]
2016
Oral cancer
Taiwan
Asian
HB
PCR-RFLP
788
956
0.029

Tsai et al. [80]
2016
Nasopharyngeal cancer
Taiwan
Asian
HB
PCR-RFLP
176
352
0.278

Lai et al. [81]
2017
Hepatocellular cancer
Taiwan
Asian
HB
PCR-RFLP
298
889
0.008

Padala et al. [82]
2017
Breast cancer
India
Asian
HB
PCR-RFLP
300
300
0.015

Yang et al. [83]
2017
Gastric cancer
Taiwan
Asian
HB
PCR-RFLP
121
363
0.131

HWE: Hardy–Weinberg equilibrium.

3.2. Quantitative Analysis

The main results of this meta-analysis are listed in Table 2. The association between the MMP1–1607 (1G>2G) polymorphism and cancer risks was seen in the allele model (2G vs. 1G, OR: 1.174, 95% CI: 1.107–1.244; Figure 2), the dominant model (2G2G/1G2G vs. 1G1G, OR: 1.192, 95% CI: 1.090–1.303; Figure 3), and the recessive model (2G2G vs. 1G2G/1G1G, OR: 1.231, 95% CI: 1.141–1.329; Figure 4).

Table 2: Stratified analyses of MMP1–1607 (1G>2G) polymorphism on cancer risks by random-effects model.

2g vs. 1g
2g2g–1g2g vs. 1g1g
2g2g vs. 1g1g–1g2g

OR
UCI
LCI

OR
UCI
LCI

OR
UCI
LCI

Overall
77
1.174
1.107
1.244
0.000
74.0%
1.192
1.090
1.303
0.000
62.4%
1.231
1.141
1.329
0.000
67.5%

Cancer types

    Lung cancer
9
1.128
1.002
1.269
0.006
63.1%
1.127
1.005
1.264
0.365
8.4%
1.153
0.953
1.395
0.002
68.1%

    Colorectal cancer
8
1.279
1.087
1.505
0.035
53.6%
1.281
1.033
1.588
0.365
8.5%
1.368
1.094
1.712
0.041
52.1%

    Gastric cancer
6
1.106
0.964
1.268
0.165
36.3%
1.221
0.884
1.687
0.061
52.6%
1.121
0.967
1.300
0.448
0.0%

    Oral cancer
6
1.121
0.849
1.481
0.000
81.8%
1.254
0.790
1.991
0.001
75.9%
1.108
0.807
1.521
0.003
72.3%

    Nervous system cancer
5
1.799
1.493
2.168
0.869
0.0%
2.070
1.474
2.906
0.438
0.0%
1.935
1.498
2.501
0.475
0.0%

    Ovarian cancer
4
1.022
0.888
1.176
0.845
0.0%
1.090
0.769
1.545
0.174
39.7%
1.013
0.823
1.247
0.417
0.0%

    Breast cancer
4
1.194
0.904
1.576
0.000
89.6%
1.352
0.906
2.017
0.000
84.9%
1.149
0.809
1.632
0.000
84.7%

    Renal cancer
4
1.351
1.149
1.590
0.328
12.8%
1.179
0.898
1.547
0.829
0.0%
1.674
1.351
2.073
0.580
0.0%

    Bladder cancer
4
1.437
0.960
2.152
0.000
89.2%
1.349
0.771
2.360
0.001
83.1%
1.739
1.074
2.816
0.001
81.7%

    Prostate cancer
4
0.932
0.485
1.791
0.000
90.3%
1.136
0.493
2.616
0.001
82.5%
0.780
0.375
1.623
0.001
82.3%

    Head and neck cancer
4
0.958
0.595
1.543
0.000
92.6%
0.678
0.388
1.186
0.001
81.1%
1.071
0.539
2.219
0.000
92.4%

    Endometrial cancer
3
1.147
0.756
1.741
0.020
74.4%
1.312
0.492
3.497
0.005
81.0%
1.091
0.807
1.476
0.320
12.3%

    Nasopharyngeal cancer
3
1.212
1.067
1.377
0.340
7.4%
1.299
0.996
1.696
0.319
12.5%
1.265
1.074
1.488
0.535
0.0%

    Hepatocellular cancer
3
0.995
0.875
1.131
0.890
0.0%
0.816
0.631
1.055
0.333
9.0%
1.118
0.932
1.341
0.428
0.0%

    Esophageal cancer
3
1.189
0.899
1.572
0.039
69.1%
1.321
0.908
1.922
0.138
49.5%
1.260
0.866
1.835
0.080
60.4%

    Other cancers
7
1.172
1.010
1.360
0.043
53.8%
1.128
0.903
1.410
0.167
34.2%
1.278
1.038
1.573
0.073
48.0%

Ethnicity

    Asian
43
1.228
1.130
1.334
0.009
75.2%
1.256
1.084
1.456
0.000
68.9%
1.297
1.176
1.431
0.000
66.4%

    Caucasian
34
1.109
1.023
1.202
0.009
71.2%
1.126
1.015
1.249
0.000
50.1%
1.431
1.013
1.289
0.000
67.2%

n: number of comparison; P: value of test for heterogeneity test; UCI: upper limit of the 95% confidence interval; LCI: lower limit of the 95% confidence interval.

Figure 2: Forest plot of MMP1–1607 (1G>2G) polymorphism and cancer risks in the allele model (2G vs. 1G).

Figure 3: Forest plot of MMP1–1607 (1G>2G) polymorphism and cancer risks in the dominate model (2G2G/1G2G vs. 1G1G).

Figure 4: Forest plot of MMP1–1607 (1G>2G) polymorphism and cancer risks in the recessive model (2G2G vs. 1G2G/1G1G).

3.3. Risk by Cancer Type

When we considered different cancer types, elevated risk was found in lung cancer in the allele model (2G vs. 1G, OR: 1.128, 95% CI: 1.002–1.268) and the dominant model (2G2G/1G2G vs. 1G1G, OR: 1.127, 95% CI: 1.005–1.264).

Significant association was also found in colorectal cancer in the allele model (2G vs. 1G, OR: 1.279, 95% CI: 1.087–1.505), the dominant model (2G2G/1G2G vs. 1G1G, OR: 1.281, 95% CI: 1.033–1.588), and the recessive model (2G2G vs. 1G2G/1G1G, OR: 1.368, 95% CI: 1.094–1.712).

Five articles addressed the MMP1–1607 polymorphism in nervous system cancers, including astrocytoma, glioblastoma, hypophyseal adenoma, and malignant gliomas. Significantly elevated risks were observed in all the three different models (2G vs. 1G, OR: 1.799, 95% CI: 1.493–2.168; 2G2G/1G2G vs. 1G1G, OR: 2.070, 95% CI: 1.474–2.906; and 2G2G vs. 1G2G/1G1G, OR: 1.935, 95% CI: 1.498–2.501).

In renal cancer, the association was found in the allele model (2G vs. 1G: OR: 1.351, 95% CI: 1.149–1.590) and the recessive model (2G2G vs. 1G2G/1G1G OR: 1.674, 95% CI: 1.351–2.073). In bladder cancer, only in the recessive model was significant association detected (2G2G vs. 1G2G/1G1G, OR: 1.739, 95% CI: 1.074–2.816).

Increased risk was also found in nasopharyngeal cancer in the allele model (2G vs. 1G, OR: 1.212, 95% CI: 1.067–1.377) and the recessive model (2G2G vs. 1G2G/1G1G, OR: 1.267, 95% CI: 1.074–1.488).

No relationship was observed in gastric cancer, oral cancer, ovarian cancer, breast cancer, prostate cancer, head and neck cancer, endometrial cancer, hepatocellular cancer, or esophageal cancer (Table 2).

3.4. Risk by Ethnicity

In the Asian population, the association between the variation and cancer risks was detected in the allele model (2G vs. 1G, OR: 1.228, 95% CI: 1.130–1.334), the dominant model (2G2G/1G2G vs. 1G1G, OR: 1.256, 95% CI: 1.084–1.456), and the recessive model (2G2G vs. 1G2G/1G1G, OR: 1.297, 95% CI: 1.176–1.431). In the Caucasian population, evaluated risk was also found in the allele model (2G vs. 1G, OR: 1.109, 95% CI: 1.023–1.202), the dominant model (2G2G/1G2G vs. 1G1G, OR: 1.126, 95% CI: 1.015–1.249), and the recessive model (2G2G vs. 1G2G/1G1G, OR: 1.431, 95% CI: 1.013–1.289). Although significant differences were observed in both Asian and Caucasian populations, the Asian population showed higher risk than the Caucasian for the allele, dominant model, or homozygous model, but showed a decreasing trend in the recessive model (Table 2).

3.5. Heterogeneity and Sensitivity Analysis

Heterogeneity was observed in overall analyses in all comparison models with and range from 50.2% to 74.0% (indicating moderate or substantial heterogeneity). We therefore used the random-effects model. Sensitivity analysis to assess influence of individual studies showed no individual study to greatly affect the pooled OR.

3.6. Publication Bias

The forest plot seemed to be symmetrical (Figure 5). Harbord’s and Peter’s tests revealed no statistical significance in publication bias (Harbord’s: ; Peter’s: ).

Figure 5: Funnel plot for publication bias test (2G vs. 1G).

4. Discussion

The MMP1–1607 (1G>2G) polymorphism has been associated with increased transcription of MMP1 due to an insert of a guanine base that creates a core-binding site for the EST family of transcription factors, which leads to increased susceptibility for tumor occurrence and progress. The significant association between the variation of MMP1–1607 (1G>2G) with some cancer types has been reported by different meta-analyses [3, 4, 84–86].

In the current meta-analysis of 77 articles with 21,327 cancer patients and 23,245 controls, the MMP1–1607 (1G>2G) polymorphism was a strong risk factor in various cancers. Although both Asian and Caucasian individuals with 2G alleles or 2G2G genotypes may be more susceptible to cancer development, several studies revealed significant associations in Asians, but not Caucasians [5, 6]. These discrepancies might be due to limited sample sizes. Moreover, the Asian population seemed to show increased risk compared with Caucasian populations when the allele or dominant models were adopted, whereas a decreasing trend was observed in a recessive model, which implies different susceptibilities.

The association was found in lung, colorectal, nervous system, renal, bladder, and nasopharyngeal cancers, but not gastric, oral, ovarian, breast, prostate, head-and-neck, endometrial, hepatocellular, or esophageal cancers, which indicates that the variation plays different roles in various cancers, in accordance with pervious meta-analyses [4, 85, 87, 88]. However, these papers only focused on single types of cancer or one specific ethnicity. Our meta-analysis included all the cancers, analyzed the overall pooled OR, and performed subgroup analyses. Our findings imply a complex relationship between cancer susceptibility and gene variation, influenced by cancer sites and ethnicities.

Recently, the functional studies of SNPs have moved fast. For instance, a study reported that a missense variant rs149418249 in the TPP1 gene confers colorectal cancer risk by interrupting TPP1–TIN2 interaction and influencing telomere length [89]. An expression quantitative trait locus-based analysis revealed that a mutation rs27437, residing in the upstream of SLC22A5, can affect colorectal cancer risk by regulating SLC22A5 expression [90]. Another article reported that a TCF7L2 missense variant rs138649767 associates with colorectal cancer risk by interacting with a GWAS-identified regulatory variant rs698326 in the MYC enhancer [91]. However, the biological mechanisms of functional SNPs still remain challenging. Therefore, further studies are required to promulgate the real functions by which the MMP1–1607 (1G>2G) polymorphism may influence cancer susceptibility and progression.

Our study had some limitations. First, moderate or substantial heterogeneity was detected between studies, which was not significantly decreased by subgroup analysis. When all variations were included in the meta-regression analysis, no obvious factors were detected. More subgroup analyses should be performed, based on factors such as tobacco or alcohol consumption. This conclusion should be interpreted with caution. Second, this analysis was performed with candidate gene strategy in which the MMP1–1607 (1G>2G) polymorphism was selected for study based on a priori knowledge of the gene’s biological functional impact on the trait or disease in question [92]. Genome-wide association studies (GWAS) which scan the entire genome for genetic variation include immense amounts of SNPs. Published papers usually reported those SNPs with highly statistical significance (usually ). We have retrieved literature through PubMed in order to search the evidence of association between the MMP1–1607 (1G>2G) polymorphism and cancer risks in GWAS results [92, 93]. However, we did not acquire any positive findings. We speculate that ethnic discrepancy, population stratification, and different standards of statistical significance might lead to negative findings in GWAS. Third, due to the innate shortage of case–control designed studies, the quantity of studies was limited. Third, gene–gene and gene–environment interactions should be considered in analyses of the effects of genes. Fourth, more original papers with large sample sizes were required due to lack of eligible studies in specific cancers in this analysis.

5. Conclusions

In conclusion, an association between the MMP1–1607 (1G>2G) polymorphism and cancer risks was detected in both Asians and Caucasians. After stratification by cancer types, associations were found for lung cancer, colorectal cancer, nervous system cancer, renal cancer, bladder cancer, and nasopharyngeal cancer. More original studies with larger sample size are required for future analysis.

Conflicts of Interest

The authors declare no competing financial interests.

Acknowledgments

This study was partly funded by the National Natural Science Foundation of China (Grant No. NSFC 81502195 and NSFC 81672512) and Medicine and Health Science Technology Development Project of Shandong Province (No. 2016WS0258). We thank Liwen Bianji, Edanz Group China (http://www.liwenbianji.cn/ac), for editing the English text of a draft of this manuscript.

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