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

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 (P > 0.05) 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 Q test using P value and [I.sup.2] value. A fixed-effects model was adopted when Q test indicated a lack of heterogeneity (P > 0.05); otherwise, a random-effects model was used. We considered 0-40% of [I.sup.2] 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.

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).

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: 1.1301.334), 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 P < 0.05 and [I.sup.2] 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: P = 0.093; Peter's: P = 0.153).

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 P < [10.sup.-6]). 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.

https://doi.org/10.1155/2018/7565834

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

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

Caption: Figure 1: Flow chart of study selection procedure.

Caption: Figure 5: Funnel plot for publication bias test (2G vs. 1G).
Table 1: The main characteristics of studies included in the meta-
analysis.

Author                    Year        Cancer type        Country

Kanamori et al. [7]       1999      Ovarian cancer        Japan
Biondi et al. [8]         2000       Other cancer         Italy
Nishioka et al. [9]       2000    Endometrial cancer      Japan
Ye et al. [10]            2001    Cutaneous melanoma     England
Zhu et al. [11]           2001        Lung cancer        America
Ghilardi et al. [12]      2002       Breast cancer       America
Hinoda et al. [13]        2002     Colorectal cancer      Japan
Hirata et al. [14]        2003     Renal cell cancer      Japan
Nishioka et al. [15]      2003    Endometrial cancer      Japan
Wenham et al. [16]        2003      Ovarian cancer       America
Hashimoto et al. [17]     2004   Head and neck cancer     Japan
Hirata et al. [18]        2004     Renal cell cancer      Japan
Lin et al. [19]           2004        Oral cancer         Taiwan
Matsumura et al. [20]     2004      Gastric cancer        Japan
Zinzindohoue et al. [21]  2004   Head and neck cancer     France
Fang et al. [22]          2005        Lung cancer         China
Jin et al. [23]           2005      Gastric cancer        China
Ju et al. [24]            2005      Cervical cancer       Korea
Lai et al. [25]           2005      Cervical cancer       Taiwan
McCready et al. [26]      2005       Glioblastoma        America
Cao and Li [27]           2006        Oral cancer         China
Elander et al. [28]       2006     Colorectal cancer      Sweden
Kader et al. [29]         2006      Bladder cancer       America
Li et al. [30]            2006      Ovarian cancer        China
Lievre et al. [31]        2006     Colorectal cancer      France
O-charoenrat et al. [32]  2006   Head and neck cancer    Thailand
Su et al. [33]            2006        Lung cancer        America
Sugimoto et al. [34]      2006    Endometrial cancer      Japan
Xu et al. [35]            2006     Colorectal cancer      China
Albayrak et al. [36]      2007      Prostate cancer       Turkey
Ju et al. [37]            2007      Ovarian cancer        Korea
Lei et al. [38]           2007       Breast cancer        Sweden
Lu et al. [39]            2007       Other cancer         China
Nasr et al. [40]          2007      Nasopharyngeal       Tunisia
                                        cancer
Nishizawa et al. [41]     2007        Oral cancer         Japan
Piccoli et al. [42]       2007   Renal cell carcinoma     Brazil
Vairaktaris et al. [43]   2007        Oral cancer         Greek
Woo et al. [44]           2007     Colorectal cancer      Korea
Zhai et al. [45]          2007   Hepatocellular cancer    China
Zhou et al. [46]          2007      Nasopharyngeal        China
                                        cancer
Dos Reis et al. [47]      2008      Prostate cancer       Brazil
Gonzalez-Arriaga et al.   2008        Lung cancer         Spain
[48]
Kouhkan et al. [49]       2008     Colorectal cancer       Iran
Shimizu et al. [50]       2008       Tongue cancer        Japan
Tasci et al. [51]         2008      Bladder cancer        Turkey
Bradbury et al. [52]      2009     Esophageal cancer     America
de Lima et al. [53]       2009     Colorectal cancer      Brazil
dos Reis et al. [54]      2009      Prostate cancer       Brazil
Ricketts et al. [55]      2009     Renal cell cancer      Polish
Srivastava et al. [56]    2010      Bladder cancer        India
Tsuchiya et al. [57]      2009      Prostate cancer       Japan
Vairaktaris et al. [58]   2009        Oral cancer         Greek
Altaf et al. [59]         2010       Other cancer         Turkey
Chaudhary et al. [60]     2010   Head and neck cancer     India
Fang et al. [61]          2010     Colorectal cancer      China
Okamoto et al. [62]       2010   Hepatocellular cancer    Japan
Hart et al. [63]          2011        Lung cancer         Norway
Liu et al. [64]           2011        Lung cancer         China
Malik et al. [65]         2011       Glioblastoma         India
Wang et al. [66]          2011    Cutaneous melanoma     America
Cheung et al. [67]        2012     Esophageal cancer      Canada
Enewold et al. [68]       2012        Lung cancer        America
Fakhoury et al. [69]      2012        Lung cancer        Lebanon
Wieczorek et al. [70]     2013      Bladder cancer        Poland
Brzoska et al. [71]       2014        Lung cancer         Poland
Dedong et al. [72]        2014      Gastric cancer        China
Devulapalli et al. [73]   2014      Gastric cancer        India
Dey et al. [74]           2014      Gastric cancer        India
Guan et al. [75]          2014     Esophageal cancer      China
Kawal et al. [76]         2016       Breast cancer        Taiwan
Pei et al. [77]           2016       Other cancer         Taiwan
Su et al. [78]            2016       Breast cancer        Taiwan
Sun et al. [79]           2016        Oral cancer         Taiwan
Tsai et al. [80]          2016      Nasopharyngeal        Taiwan
                                        cancer
Lai et al. [81]           2017   Hepatocellular cancer    Taiwan
Padala et al. [82]        2017       Breast cancer        India
Yang et al. [83]          2017      Gastric cancer        Taiwan

Author                    Ethnicity   Control    Genotype    N of
                                                             case

Kanamori et al. [7]         Asian       HB       PCR-RFLP    163
Biondi et al. [8]         Caucasian     HB        TaqMan     160
Nishioka et al. [9]         Asian       HB      Sequencing   100
Ye et al. [10]            Caucasian     HB        TaqMan     139
Zhu et al. [11]           Caucasian     HB       PCR-RFLP    456
Ghilardi et al. [12]      Caucasian     HB      Sequencing    86
Hinoda et al. [13]          Asian       PB       PCR-RFLP    101
Hirata et al. [14]          Asian       HB      Sequencing   119
Nishioka et al. [15]        Asian       HB      Sequencing   109
Wenham et al. [16]        Caucasian     PB        TaqMan     311
Hashimoto et al. [17]       Asian       HB       PCR-RFLP    140
Hirata et al. [18]          Asian       PB       PCR-RFLP    156
Lin et al. [19]             Asian       HB      Sequencing   121
Matsumura et al. [20]       Asian       HB       PCR-RFLP    215
Zinzindohoue et al. [21]  Caucasian     HB       PCR-RFLP    125
Fang et al. [22]            Asian       HB       PCR-RFLP    243
Jin et al. [23]             Asian       HB       PCR-RFLP    417
Ju et al. [24]              Asian       HB        TaqMan     232
Lai et al. [25]             Asian       HB        Other      197
McCready et al. [26]      Caucasian     HB       PCR-RFLP     81
Cao and Li [27]             Asian       HB       PCR-RFLP     96
Elander et al. [28]       Caucasian     HB        Other      127
Kader et al. [29]         Caucasian     HB        TaqMan     556
Li et al. [30]              Asian       HB       PCR-RFLP    122
Lievre et al. [31]        Caucasian     HB        Other      591
O-charoenrat et al. [32]    Asian       HB       PCR-RFLP    300
Su et al. [33]            Caucasian     PB        TaqMan     2014
Sugimoto et al. [34]        Asian       HB       PCR-RFLP    107
Xu et al. [35]              Asian       HB        Other      126
Albayrak et al. [36]      Caucasian     HB       PCR-RFLP     55
Ju et al. [37]              Asian       HB        TaqMan     133
Lei et al. [38]           Caucasian     PB       TaqMan      954
Lu et al. [39]              Asian       HB       PCR-RFLP    221
Nasr et al. [40]          Caucasian     HB       PCR-RFLP    174

Nishizawa et al. [41]       Asian       HB        TaqMan     170
Piccoli et al. [42]       Caucasian     PB       PCR-RFLP     99
Vairaktaris et al. [43]   Caucasian     HB      PCR-RFLP     156
Woo et al. [44]             Asian       HB      PCR-RFLP     185
Zhai et al. [45]            Asian       HB      Sequencing   431
Zhou et al. [46]          Caucasian     PB      Sequencing   829

Dos Reis et al. [47]      Caucasian     PB        TaqMan     100
Gonzalez-Arriaga et al.   Caucasian     HB       PCR-RFLP    501
[48]
Kouhkan et al. [49]         Asian       HB       PCR-RFLP    150
Shimizu et al. [50]         Asian       HB        TaqMan      69
Tasci et al. [51]         Caucasian     HB       PCR-RFLP    102
Bradbury et al. [52]      Caucasian     HB        TaqMan     313
de Lima et al. [53]       Caucasian     HB       PCR-RFLP    108
dos Reis et al. [54]      Caucasian     HB        TaqMan     100
Ricketts et al. [55]      Caucasian     HB        TaqMan     323
Srivastava et al. [56]      Asian       HB       PCR-RFLP    200
Tsuchiya et al. [57]        Asian       PB      Sequencing   283
Vairaktaris et al. [58]   Caucasian     HB       PCR-RFLP    156
Altaf et al. [59]         Caucasian     HB       PCR-RFLP     30
Chaudhary et al. [60]       Asian       HB       PCR-RFLP    422
Fang et al. [61]            Asian       HB       PCR-RFLP    237
Okamoto et al. [62]         Asian       HB       PCR-RFLP     91
Hart et al. [63]          Caucasian     PB        TaqMan     436
Liu et al. [64]             Asian       HB       PCR-RFLP    825
Malik et al. [65]           Asian       HB       PCR-RFLP    110
Wang et al. [66]          Caucasian     HB        TaqMan     864
Cheung et al. [67]        Caucasian     HB        TaqMan     309
Enewold et al. [68]       Caucasian     HB        Other       71
Fakhoury et al. [69]      Caucasian     HB       PCR-RFLP     41
Wieczorek et al. [70]     Caucasian     HB        TaqMan     240
Brzoska et al. [71]       Caucasian     HB       PCR-RFLP     53
Dedong et al. [72]          Asian       HB        Other      422
Devulapalli et al. [73]     Asian       HB       PCR-RFLP    166
Dey et al. [74]           Caucasian     HB       PCR-RFLP    145
Guan et al. [75]            Asian       HB       PCR-RFLP    132
Kawal et al. [76]           Asian       HB       PCR-RFLP    1232
Pei et al. [77]             Asian       HB       PCR-RFLP    266
Su et al. [78]              Asian       HB       PCR-RFLP    1232
Sun et al. [79]             Asian       HB       PCR-RFLP    788
Tsai et al. [80]            Asian       HB       PCR-RFLP    176

Lai et al. [81]             Asian       HB       PCR-RFLP    298
Padala et al. [82]          Asian       HB       PCR-RFLP    300
Yang et al. [83]            Asian       HB       PCR-RFLP    121

Author                     N of     HWE (P)
                          control

Kanamori et al. [7]         150      0.033
Biondi et al. [8]           164      0.813
Nishioka et al. [9]         150      0.033
Ye et al. [10]              132      0.849
Zhu et al. [11]             451      0.028
Ghilardi et al. [12]        110      0.652
Hinoda et al. [13]          127      0.949
Hirata et al. [14]          210      0.993
Nishioka et al. [15]        150      0.033
Wenham et al. [16]          387      0.536
Hashimoto et al. [17]       223      0.852
Hirata et al. [18]          230      0.871
Lin et al. [19]             147      0.336
Matsumura et al. [20]       166      0.432
Zinzindohoue et al. [21]    249      0.978
Fang et al. [22]            350      0.000
Jin et al. [23]             350      0.000
Ju et al. [24]              332      0.695
Lai et al. [25]             197      1.000
McCready et al. [26]        57       0.916
Cao and Li [27]             120      0.657
Elander et al. [28]         208      0.918
Kader et al. [29]           555      0.565
Li et al. [30]              151      0.008
Lievre et al. [31]          561      0.900
O-charoenrat et al. [32]    300      0.988
Su et al. [33]             1323      0.597
Sugimoto et al. [34]        213      0.768
Xu et al. [35]              126      0.938
Albayrak et al. [36]        43       0.000
Ju et al. [37]              332      0.695
Lei et al. [38]             947      0.151
Lu et al. [39]              366      0.000
Nasr et al. [40]            171      0.091

Nishizawa et al. [41]       164      0.493
Piccoli et al. [42]         118      1.000
Vairaktaris et al. [43]     141      0.276
Woo et al. [44]             304      0.488
Zhai et al. [45]            479      0.559
Zhou et al. [46]            759      0.634

Dos Reis et al. [47]        100      0.293
Gonzalez-Arriaga et al.     510      0.934
[48]
Kouhkan et al. [49]         100      0.935
Shimizu et al. [50]         91       0.585
Tasci et al. [51]           94       0.740
Bradbury et al. [52]        450      0.508
de Lima et al. [53]         108      0.258
dos Reis et al. [54]        100      0.293
Ricketts et al. [55]        314      0.847
Srivastava et al. [56]      200      0.190
Tsuchiya et al. [57]        251      0.285
Vairaktaris et al. [58]     141      0.276
Altaf et al. [59]           30       0.195
Chaudhary et al. [60]       426      0.240
Fang et al. [61]            252      0.683
Okamoto et al. [62]         82       0.009
Hart et al. [63]            434      0.218
Liu et al. [64]             825      0.924
Malik et al. [65]           150      0.433
Wang et al. [66]            849      0.940
Cheung et al. [67]          279      0.974
Enewold et al. [68]         147      0.743
Fakhoury et al. [69]        51       0.218
Wieczorek et al. [70]       199      0.022
Brzoska et al. [71]         54       0.264
Dedong et al. [72]          428      0.546
Devulapalli et al. [73]     202      0.000
Dey et al. [74]             145      0.850
Guan et al. [75]            132      0.989
Kawal et al. [76]          1232      0.004
Pei et al. [77]             266      0.258
Su et al. [78]             1232      0.004
Sun et al. [79]             956      0.029
Tsai et al. [80]            352      0.278

Lai et al. [81]             889      0.008
Padala et al. [82]          300      0.015
Yang et al. [83]            363      0.131

HWE: Hardy-Weinberg equilibrium.

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

                       n                     2g vs. 1g

                             OR      UCI     LCI      P     [I.sup.2]

Overall                77   1.174   1.107   1.244   0.000     74.0%
Cancer types
  Lung cancer          9    1.128   1.002   1.269   0.006     63.1%
  Colorectal cancer    8    1.279   1.087   1.505   0.035     53.6%
  Gastric cancer       6    1.106   0.964   1.268   0.165     36.3%
  Oral cancer          6    1.121   0.849   1.481   0.000     81.8%
  Nervous system       5    1.799   1.493   2.168   0.869     0.0%
  cancer
  Ovarian cancer       4    1.022   0.888   1.176   0.845     0.0%
  Breast cancer        4    1.194   0.904   1.576   0.000     89.6%
  Renal cancer         4    1.351   1.149   1.590   0.328     12.8%
  Bladder cancer       4    1.437   0.960   2.152   0.000     89.2%
  Prostate cancer      4    0.932   0.485   1.791   0.000     90.3%
  Head and neck        4    0.958   0.595   1.543   0.000     92.6%
  cancer
  Endometrial cancer   3    1.147   0.756   1.741   0.020     74.4%
  Nasopharyngeal       3    1.212   1.067   1.377   0.340     7.4%
  cancer
  Hepatocellular       3    0.995   0.875   1.131   0.890     0.0%
  cancer
  Esophageal cancer    3    1.189   0.899   1.572   0.039     69.1%
  Other cancers        7    1.172   1.010   1.360   0.043     53.8%
Ethnicity
  Asian                43   1.228   1.130   1.334   0.009     75.2%
  Caucasian            34   1.109   1.023   1.202   0.009     71.2%

                                     2g2g-1g2g vs. 1g1g

                        OR      UCI     LCI      P     [I.sup.2]

Overall                1.192   1.090   1.303   0.000     62.4%
Cancer types
  Lung cancer          1.127   1.005   1.264   0.365     8.4%
  Colorectal cancer    1.281   1.033   1.588   0.365     8.5%
  Gastric cancer       1.221   0.884   1.687   0.061     52.6%
  Oral cancer          1.254   0.790   1.991   0.001     75.9%
  Nervous system       2.070   1.474   2.906   0.438     0.0%
  cancer
  Ovarian cancer       1.090   0.769   1.545   0.174     39.7%
  Breast cancer        1.352   0.906   2.017   0.000     84.9%
  Renal cancer         1.179   0.898   1.547   0.829     0.0%
  Bladder cancer       1.349   0.771   2.360   0.001     83.1%
  Prostate cancer      1.136   0.493   2.616   0.001     82.5%
  Head and neck        0.678   0.388   1.186   0.001     81.1%
  cancer
  Endometrial cancer   1.312   0.492   3.497   0.005     81.0%
  Nasopharyngeal       1.299   0.996   1.696   0.319     12.5%
  cancer
  Hepatocellular       0.816   0.631   1.055   0.333     9.0%
  cancer
  Esophageal cancer    1.321   0.908   1.922   0.138     49.5%
  Other cancers        1.128   0.903   1.410   0.167     34.2%
Ethnicity
  Asian                1.256   1.084   1.456   0.000     68.9%
  Caucasian            1.126   1.015   1.249   0.000     50.1%

                                    2g2g vs. 1g1g-1g2g

                        OR      UCI     LCI      P     [I.sup.2]

Overall                1.231   1.141   1.329   0.000     67.5%
Cancer types
  Lung cancer          1.153   0.953   1.395   0.002     68.1%
  Colorectal cancer    1.368   1.094   1.712   0.041     52.1%
  Gastric cancer       1.121   0.967   1.300   0.448     0.0%
  Oral cancer          1.108   0.807   1.521   0.003     72.3%
  Nervous system       1.935   1.498   2.501   0.475     0.0%
  cancer
  Ovarian cancer       1.013   0.823   1.247   0.417     0.0%
  Breast cancer        1.149   0.809   1.632   0.000     84.7%
  Renal cancer         1.674   1.351   2.073   0.580     0.0%
  Bladder cancer       1.739   1.074   2.816   0.001     81.7%
  Prostate cancer      0.780   0.375   1.623   0.001     82.3%
  Head and neck        1.071   0.539   2.219   0.000     92.4%
  cancer
  Endometrial cancer   1.091   0.807   1.476   0.320     12.3%
  Nasopharyngeal       1.265   1.074   1.488   0.535     0.0%
  cancer
  Hepatocellular       1.118   0.932   1.341   0.428     0.0%
  cancer
  Esophageal cancer    1.260   0.866   1.835   0.080     60.4%
  Other cancers        1.278   1.038   1.573   0.073     48.0%
Ethnicity
  Asian                1.297   1.176   1.431   0.000     66.4%
  Caucasian            1.431   1.013   1.289   0.000     67.2%

n: number of comparison; P: P value of Q 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).

Study                                       OR (95% CI)        %
ID                                                           Weight

Padala et al. (2017)                     2.02 (1.58, 2.57)    1.49
Yang et al. (2017)                       0.99 (0.74, 1.33)    1.33
Lai Y et al. (2017)                      0.98 (0.82, 1.19)    1.67
Su C et al. (2016)                       1.01 (0.90, 1.13)    1.87
Pei J et al. (2016)                      1.57 (1.22, 2.02)    1.46
Tsai et al. (2016)                       1.31 (1.01, 1.71)    1.43
Sun et al. (2016)                        1.01 (0.88, 1.15)    1.82
Kawal et al. (2016)                      2.67 (1.26, 5.64)    0.46
Dedong et al. (2014)                     1.32 (1.09, 1.61)    1.63
Guan et al. (2014)                       0.78 (0.51, 1.18)    0.98
Devulapalli et al. (2014)                1.24 (0.92, 1.66)    1.32
Dey et al. (2014)                        1.00 (0.72, 1.40)    1.21
Brzoska et al. (2014)                    1.16 (0.68, 1.99)    0.74
Wieczorek et al. (2013)                  1.11 (0.85, 1.45)    1.42
Enewold et al. (2012)                    1.08 (0.72, 1.61)    1.03
Fakhoury et al. (2012)                   0.85 (0.45, 1.59)    0.60
Cheung et al. (2012)                     1.45 (1.15, 1.82)    1.53
Liu et al. (2011)                        1.31 (1.13, 1.52)    1.78
Malik et al. (2011)                      1.76 (1.24, 2.51)    1.15
Wang et al. (2011)                       1.00 (0.88, 1.15)    1.82
Hart et al. (2011)                       1.13 (0.94, 1.37)    1.66
Srivastava et al. (2010)                 1.91 (1.44, 2.55)    1.35
Chaudhary et al. (2010)                  0.66 (0.54, 0.81)    1.62
Fang et al. (2010)                       0.98 (0.72, 1.34)    1.28
Okamoto et al. (2010)                    1.11 (0.71, 1.74)    0.91
Altas et al. (2010)                      1.71 (0.83, 3.54)    0.49
de Lima et al. (2009)                    1.19 (0.81, 1.75)    1.07
Vairaktaris et al. (2009)                0.73 (0.52, 1.01)    1.23
Tsuchiya et al. (2009)                   0.96 (0.75, 1.24)    1.45
Bradbury et al. (2009)                   1.27 (1.03, 1.56)    1.61
Ricketts et al. (2009)                   1.25 (1.00, 1.56)    1.56
dos Reis et al. (2009)                   0.44 (0.29, 0.66)    0.99
F Kouhkan et al. (2008)                  1.67 (1.16, 2.39)    1.13
Tasci et al. (2008)                      2.32 (1.54, 3.49)    1.01
Gonzalez-Arriaga et al. (2008)           0.94 (0.79, 1.12)    1.71
Shimizu et al. (2008)                    1.24 (0.77, 1.99)    0.86
dos Reis (2008)                          2.28 (1.51, 3.45)    0.99
Albayrak et al. (2007)                   0.76 (0.38, 1.50)    0.53
Woo et al. (2007)                        1.59 (1.15, 2.18)    1.25
Nasr et al. (2007)                       1.44 (1.03, 2.00)    1.22
Vairaktaris et al. (2007)                0.73 (0.52, 1.01)    1.23
Lu et al. (2007)                         1.71 (1.30, 2.26)    1.38
Piccoli et al. (2007)                    1.11 (0.76, 1.62)    1.09
Zhou et al. (2007)                       1.13 (0.98, 1.31)    1.79
Zhai et al. (2007)                       0.99 (0.82, 1.19)    1.66
Nishizawa et al. (2007)                  1.41 (1.03, 1.94)    1.26
Lei et al. (2007)                        0.99 (0.87, 1.12)    1.84
Ju et al. (2007)                         0.91 (0.67, 1.22)    1.32
Lievre et al. (2006)                     1.08 (0.92, 1.27)    1.74
Xu et al. (2006)                         0.98 (0.67, 1.44)    1.08
Elander et al. (2006)                    1.41 (1.03, 1.94)    1.27
Sugimoto et al. (2006)                   0.76 (0.54, 1.08)    1.17
O-charoenrat et al. (2006)               1.60 (1.27, 2.02)    1.52
Cao and Li (2006)                        2.23 (1.48, 3.37)    1.00
Li et al. (2006)                         1.05 (0.73, 1.51)    1.14
Kader et al. (2006)                      0.95 (0.81, 1.13)    1.73
Su et al. (2006)                         1.00 (0.90, 1.10)    1.90
Lai et al. (2005)                        1.06 (0.79, 1.41)    1.34
Jin et al. (2005)                        1.15 (0.92, 1.44)    1.55
Fang et al. (2005)                       1.13 (0.87, 1.46)    1.44
McCready et al. (2005)                   1.89 (1.16, 3.07)    0.84
Ju et al. (2005)                         1.01 (0.78, 1.30)    1.46
Hashimoto et al. (2004)                  1.25 (0.90, 1.73)    1.24
Matsumura et al. (2004)                  0.83 (0.61, 1.13)    1.28
Zinzindohoue et al. (2004)               0.64 (0.47, 0.87)    1.29
Hirata et al. (2004)                     1.50 (1.09, 2.07)    1.25
Lin et al. (2004)                        1.32 (0.92, 1.89)    1.13
Hirata et al. (2003)                     1.69 (1.18, 2.42)    1.14
Nishioka et al. (2003)                   1.33 (0.92, 1.92)    1.12
Wenham et al. (2003)                     1.05 (0.85, 1.29)    1.59
Hinoda et al. (2002)                     1.80 (1.20, 2.70)    1.02
Ghilardi et al. (2002)                   1.03 (0.69, 1.54)    1.03
Zhu et al. (2001)                        1.45 (1.20, 1.75)    1.67
Ye et al. (2001)                         1.48 (1.05, 2.07)    1.20
Nishioka et al. (2000)                   1.51 (1.03, 2.21)    1.08
Biondi et al. (2000)                     1.15 (0.84, 1.56)    1.29
Kanamori et al. (1999)                   1.08 (0.78, 1.50)    1.24
Overall (I-squared = 74.0%, P = 0.000)   1.17 (1.11, 1.24)   100.00

NOTE: weights are from random-effects analysis

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

Study                                       OR (95% CI)         %
ID                                                            Weight

Padala et al. (2017)                     2.97 (1.83, 4.83)     1.46
Yang et al. (2017)                       0.90 (0.55, 1.46)     1.46
Lai Y et al. (2017)                      0.83 (0.61, 1.13)     1.97
Su C et al. (2016)                       0.92 (0.76, 1.12)     2.31
Pei J et al. (2016)                      1.91 (1.18, 3.11)     1.46
Tsai et al. (2016)                       1.27 (0.80, 2.04)     1.50
Sun et al. (2016)                        0.88 (0.70, 1.11)     2.21
Kawal et al. (2016)                      3.76 (1.24, 11.38)    0.52
Dedong et al. (2014)                     1.62 (1.10, 2.38)     1.73
Guan et al. (2014)                       0.48 (0.16, 1.45)     0.52
Devulapalli et al. (2014)                3.26 (1.29, 8.24)     0.68
Dey et al. (2014)                        0.85 (0.44, 1.62)     1.09
Brzoska et al. (2014)                    1.57 (0.66, 3.72)     0.76
Wieczorek et al. (2013)                  0.83 (0.53, 1.31)     1.55
Enewold et al. (2012)                    0.89 (0.48, 1.64)     1.17
Fakhoury et al. (2012)                   1.15 (0.34, 3.92)     0.44
Cheung et al. (2012)                     1.56 (1.09, 2.22)     1.83
Liu et al. (2011)                        1.40 (1.02, 1.92)     1.95
Malik et al. (2011)                      1.41 (0.80, 2.49)     1.26
Wang et al. (2011)                       1.04 (0.84, 1.28)     2.25
Hart et al. (2011)                       1.22 (0.91, 1.64)     2.01
Srivastava et al. (2010)                 2.21 (1.33, 3.68)     1.41
Chaudhary et al. (2010)                  0.35 (0.22, 0.55)     1.57
Fang et al. (2010)                       0.94 (0.43, 2.07)     0.85
Okamoto et al. (2010)                    0.31 (0.08, 1.16)     0.39
Altas et al. (2010)                      2.89 (0.86, 9.74)     0.45
de Lima et al. (2009)                    2.02 (0.82, 4.98)     0.71
Vairaktaris et al. (2009)                0.68 (0.38, 1.22)     1.24
Tsuchiya et al. (2009)                   1.07 (0.64, 1.78)     1.40
Bradbury et al. (2009)                   1.41 (1.02, 1.94)     1.94
Ricketts et al. (2009)                   1.20 (0.82, 1.76)     1.76
dos Reis et al. (2009)                   0.46 (0.21, 1.02)     0.85
F Kouhkan et al. (2008)                  1.63 (0.91, 2.94)     1.22
Tasci et al. (2008)                      2.59 (1.28, 5.22)     1.00
Gonzalez-Arriaga et al. (2008)           0.89 (0.67, 1.18)     2.04
Shimizu et al. (2008)                    0.65 (0.25, 1.69)     0.65
dos Reis (2008)                          3.30 (1.83, 5.97)     1.21
Albayrak et al. (2007)                   0.88 (0.30, 2.53)     0.56
Woo et al. (2007)                        1.51 (0.61, 3.70)     0.71
Nasr et al. (2007)                       2.12 (1.05, 4.30)     0.99
Vairaktaris et al. (2007)                0.68 (0.38, 1.22)     1.24
Lu et al. (2007)                         2.61 (1.42, 4.82)     1.17
Piccoli et al. (2007)                    0.94 (0.52, 1.71)     1.20
Zhou et al. (2007)                       1.17 (0.87, 1.59)     1.99
Zhai et al. (2007)                       0.87 (0.59, 1.27)     1.76
Nishizawa et al. (2007)                  2.39 (1.21, 4.72)     1.04
Lei et al. (2007)                        1.09 (0.89, 1.33)     2.28
Ju et al. (2007)                         0.87 (0.45, 1.66)     1.10
Lievre et al. (2006)                     1.00 (0.77, 1.30)     2.12
Xu et al. (2006)                         1.55 (0.61, 3.94)     0.68
Elander et al. (2006)                    1.63 (0.94, 2.81)     1.31
Sugimoto et al. (2006)                   0.49 (0.24, 1.02)     0.95
O-charoenrat et al. (2006)               1.16 (0.77, 1.75)     1.68
Cao and Li (2006)                        2.89 (1.24, 6.75)     0.78
Li et al. (2006)                         1.01 (0.53, 1.93)     1.10
Kader et al. (2006)                      0.84 (0.63, 1.13)     2.03
Su et al. (2006)                         1.05 (0.89, 1.22)     2.39
Lai et al. (2005)                        0.85 (0.48, 1.49)     1.26
Jin et al. (2005)                        1.31 (0.86, 2.00)     1.63
Fang et al. (2005)                       1.56 (0.93, 2.61)     1.39
McCready et al. (2005)                   1.87 (0.81, 4.31)     0.79
Ju et al. (2005)                         0.87 (0.51, 1.51)     1.32
Hashimoto et al. (2004)                  0.87 (0.45, 1.69)     1.07
Matsumura et al. (2004)                  0.83 (0.43, 1.59)     1.09
Zinzindohoue et al. (2004)               0.61 (0.38, 0.98)     1.49
Hirata et al. (2004)                     1.24 (0.62, 2.46)     1.02
Lin et al. (2004)                        2.17 (0.99, 4.73)     0.87
Hirata et al. (2003)                     1.47 (0.68, 3.18)     0.88
Nishioka et al. (2003)                   1.85 (0.91, 3.73)     0.99
Wenham et al. (2003)                     0.92 (0.66, 1.29)     1.89
Hinoda et al. (2002)                     2.05 (0.86, 4.89)     0.75
Ghilardi et al. (2002)                   1.42 (0.71, 2.85)     1.01
Zhu et al. (2001)                        1.26 (0.92, 1.72)     1.96
Ye et al. (2001)                         1.45 (0.85, 2.48)     1.33
Nishioka et al. (2000)                   2.53 (1.14, 5.59)     0.85
Biondi et al. (2000)                     1.23 (0.74, 2.05)     1.39
Kanamori et al. (1999)                   2.01 (1.07, 3.79)     1.12
Overall (I-squared = 62.4%, P = 0.000)   1.19 (1.09, 1.30)    100.00

NOTE: weights are from random-effects analysis

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

Study                                       OR (95% CI)         %
ID                                                            Weight

Padala et al. (2017)                     2.08 (1.50, 2.88)     1.58
Yang et al. (2017)                       1.08 (0.70, 1.66)     1.30
Lai Y et al. (2017)                      1.11 (0.85, 1.46)     1.73
Su C et al. (2016)                       1.08 (0.92, 1.28)     2.00
Pei J et al. (2016)                      1.66 (1.18, 2.35)     1.52
Tsai et al. (2016)                       1.48 (1.03, 2.15)     1.46
Sun et al. (2016)                        1.11 (0.92, 1.36)     1.93
Kawal et al. (2016)                      2.79 (0.75, 10.33)    0.30
Dedong et al. (2014)                     1.35 (1.02, 1.77)     1.72
Guan et al. (2014)                       0.82 (0.50, 1.36)     1.13
Devulapalli et al. (2014)                1.17 (0.73, 1.86)     1.21
Dey et al. (2014)                        1.09 (0.68, 1.76)     1.19
Brzoska et al. (2014)                    0.94 (0.41, 2.16)     0.61
Wieczorek et al. (2013)                  1.59 (1.01, 2.49)     1.25
Enewold et al. (2012)                    1.47 (0.73, 2.92)     0.79
Fakhoury et al. (2012)                   0.71 (0.31, 1.62)     0.62
Cheung et al. (2012)                     1.71 (1.15, 2.54)     1.39
Liu et al. (2011)                        1.41 (1.16, 1.71)     1.94
Malik et al. (2011)                      2.55 (1.51, 4.30)     1.09
Wang et al. (2011)                       0.97 (0.77, 1.22)     1.85
Hart et al. (2011)                       1.12 (0.83, 1.53)     1.63
Srivastava et al. (2010)                 2.20 (1.46, 3.31)     1.35
Chaudhary et al. (2010)                  0.73 (0.55, 0.95)     1.73
Fang et al. (2010)                       0.99 (0.69, 1.44)     1.46
Okamoto et al. (2010)                    1.62 (0.88, 2.98)     0.92
Altas et al. (2010)                      1.35 (0.46, 3.97)     0.41
de Lima et al. (2009)                    1.09 (0.61, 1.95)     0.98
Vairaktaris et al. (2009)                0.68 (0.42, 1.08)     1.20
Tsuchiya et al. (2009)                   0.90 (0.64, 1.27)     1.54
Bradbury et al. (2009)                   1.31 (0.93, 1.85)     1.53
Ricketts et al. (2009)                   1.47 (1.04, 2.07)     1.53
dos Reis et al. (2009)                   0.30 (0.17, 0.55)     0.95
F Kouhkan et al. (2008)                  2.17 (1.23, 3.84)     0.99
Tasci et al. (2008)                      3.03 (1.64, 5.60)     0.91
Gonzalez-Arriaga et al. (2008)           0.95 (0.72, 1.26)     1.70
Shimizu et al. (2008)                    1.77 (0.94, 3.33)     0.88
dos Reis (2008)                          2.15 (0.98, 4.74)     0.66
Albayrak et al. (2007)                   0.68 (0.27, 1.68)     0.54
Woo et al. (2007)                        1.81 (1.23, 2.64)     1.43
Nasr et al. (2007)                       1.37 (0.89, 2.09)     1.32
Vairaktaris et al. (2007)                0.68 (0.42, 1.08)     1.20
Lu et al. (2007)                         1.63 (1.15, 2.29)     1.53
Piccoli et al. (2007)                    1.40 (0.76, 2.57)     0.92
Zhou et al. (2007)                       1.19 (0.97, 1.45)     1.92
Zhai et al. (2007)                       1.04 (0.80, 1.36)     1.74
Nishizawa et al. (2007)                  1.29 (0.84, 2.00)     1.29
Lei et al. (2007)                        0.89 (0.72, 1.10)     1.89
Ju et al. (2007)                         0.88 (0.59, 1.33)     1.36
Lievre et al. (2006)                     1.23 (0.94, 1.62)     1.73
Xu et al. (2006)                         0.85 (0.52, 1.40)     1.15
Elander et al. (2006)                    1.54 (0.95, 2.49)     1.18
Sugimoto et al. (2006)                   0.82 (0.51, 1.31)     1.21
O-charoenrat et al. (2006)               2.34 (1.67, 3.27)     1.55
Cao and Li (2006)                        2.58 (1.49, 4.49)     1.03
Li et al. (2006)                         1.09 (0.68, 1.76)     1.19
Kader et al. (2006)                      1.02 (0.79, 1.33)     1.76
Su et al. (2006)                         0.95 (0.80, 1.11)     2.01
Lai et al. (2005)                        1.21 (0.81, 1.80)     1.38
Jin et al. (2005)                        1.11 (0.83, 1.48)     1.69
Fang et al. (2005)                       1.01 (0.72, 1.40)     1.57
McCready et al. (2005)                   2.71 (1.25, 5.89)     0.68
Ju et al. (2005)                         1.07 (0.76, 1.49)     1.55
Hashimoto et al. (2004)                  1.55 (1.02, 2.38)     1.31
Matsumura et al. (2004)                  0.79 (0.52, 1.18)     1.36
Zinzindohoue et al. (2004)               0.47 (0.26, 0.83)     0.99
Hirata et al. (2004)                     1.84 (1.22, 2.77)     1.35
Lin et al. (2004)                        1.19 (0.73, 1.93)     1.17
Hirata et al. (2003)                     2.08 (1.32, 3.29)     1.23
Nishioka et al. (2003)                   1.23 (0.75, 2.01)     1.15
Wenham et al. (2003)                     1.25 (0.87, 1.77)     1.50
Hinoda et al. (2002)                     2.08 (1.22, 3.53)     1.07
Ghilardi et al. (2002)                   0.80 (0.43, 1.51)     0.88
Zhu et al. (2001)                        1.82 (1.39, 2.38)     1.73
Ye et al. (2001)                         1.98 (1.11, 3.54)     0.98
Nishioka et al. (2000)                   1.34 (0.81, 2.23)     1.12
Biondi et al. (2000)                     1.19 (0.71, 1.98)     1.11
Kanamori et al. (1999)                   0.80 (0.51, 1.26)     1.25
Overall (I-squared = 67.5%, P = 0.000)   1.23 (1.14, 1.33)    100.00

NOTE: weights are from random-effects analysis
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Author:Zhou, Zhonghan; Ma, Xiaocheng; Wang, Fangming; Sun, Lijiang; Zhang, Guiming
Publication:Disease Markers
Date:Jan 1, 2018
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