Introduction
Cancer remains one of the leading causes of
mortality worldwide, with an estimated 20 million new cases and 9.7
million cancer-related mortalities globally in 2022 (1,2). The
incidence and mortality rates vary across different types of
cancer, among which breast, lung, colorectal, prostate and gastric
cancer are the most common malignancies (3–5). Lung
cancer continues to be the primary cause of cancer-related
mortality, accounting for ~18.7% of all cancer-associated
mortalities globally (1). Genetic
factors play a key role in the occurrence and progression of
cancer. In recent years, numerous genetic polymorphisms have been
shown to be associated with susceptibility to various types of
cancer (6,7).
Matrix metalloproteinases (MMPs) are a family of
zinc-dependent proteases that can degrade the extracellular matrix
(ECM), playing notable roles in tumor growth, invasion and
metastasis (8,9). MMPs, particularly MMP-1, MMP-3 and
MMP-8, are closely involved in several key processes of
tumorigenesis, including apoptosis, angiogenesis and immune evasion
(10–12). MMP-1 is widely expressed,
participates in the degradation of type I, II and III collagen, and
promotes tumor cell proliferation and metastasis by remodeling the
tumor microenvironment (13). As an
MMP, MMP-3 can hydrolyze various ECM components, such as laminin
and collagen, thereby facilitating tumor cell migration and
invasion (14–17). MMP-8, functioning as a collagenase,
may affect the carcinogenic process by altering the balance of the
tumor microenvironment (18,19).
Although numerous studies have investigated the
association between MMP polymorphisms and cancer susceptibility,
the results have shown considerable heterogeneity. For example, the
MMP-1-1607 polymorphism (rs1799750), a 1G/2G insertion/deletion
variant, has been associated with an increased risk of bladder,
colorectal and esophageal cancer, but with a reduced risk of
prostate cancer in certain populations (20–22).
Similarly, inconsistent findings have also been reported for MMP-3
and MMP-8 polymorphisms. A systematic review summarized that the
MMP-8 rs11225395 polymorphism was associated with reduced risks of
breast and bladder cancer, but increased risks of melanoma and
ovarian cancer, while rs2155052 was associated with decreased lung
cancer risk (18). By contrast, an
updated meta-analysis found no significant association between the
MMP-3-1171 5A/6A polymorphism and lung cancer risk (23). These differences in study outcomes
may be attributed to variations in study design, population
characteristics and the genetic models employed.
Given these inconsistencies, the present study
conducted a systematic review and meta-analysis to evaluate the
relationships between MMP-1, MMP-3 and MMP-8 polymorphisms and
cancer risk. The primary aim of the present study was to clarify
whether these polymorphisms are universally associated with cancer
susceptibility across different populations and cancer types, and
to assess the functional significance of these genetic variants by
utilizing publicly available genomic data, thereby providing a
comprehensive understanding of their roles in cancer risk.
Materials and methods
Study selection
A systematic review and meta-analysis were conducted
following the guidelines of the Human Genome Epidemiology Network
for systematic reviews of genetic association studies and the
Preferred Reporting Items for Systematic Reviews and Meta-Analyses
statement (24–26). Studies were eligible for inclusion
if they met the following criteria: i) Case-control or cohort
studies investigating the association between MMP-1, MMP-3 and
MMP-8 polymorphisms and cancer risk; ii) provision of the necessary
genotype data or a method to calculate the odds ratio (OR) and 95%
confidence interval (CI); and iii) publication in English. Studies
were excluded if they: i) Lacked sufficient data; ii) were review
articles, meta-analyses, editorials, case reports or treatment
guidelines; and iii) were conducted on animal models or cell
lines.
Search strategy
PubMed (https://pubmed.ncbi.nlm.nih.gov), Medline (via the
National Library of Medicine; http://www.nlm.nih.gov/medline) and Web of Science
(https://webofscience.clarivate.com)
were used to conduct the literature search from database inception
up to November 1, 2024. The following key words were used: ‘Tumor
or neoplasm or cancer or tumor or malignancy’, ‘polymorphism or
genotype or mutation or variants or SNP or single nucleotide
polymorphism’ and ‘metalloproteinases or collagenase or matrix
metalloproteinase or MMP or gelatinase or matrilysin’. Although a
broad MMP-family search was used to minimize missed studies
reporting multiple MMP SNPs, only data on MMP-1/3/8 polymorphisms
were eligible for inclusion and were extracted for analysis.
Additionally, reference lists from the included studies were also
examined for potentially relevant studies.
Data extraction
In total, two independent reviewers performed the
data extraction from the eligible studies. The extracted data
included the first author, year of publication, study design,
cancer type, MMP polymorphisms, sample size and genotype
distribution. Any discrepancies between the reviewers were resolved
by discussion.
Statistical analysis
Meta-analysis was performed using STATA 12.0
software (StataCorp LP). A total of three genetic models (allelic,
dominant and recessive) were applied in the analysis.
Ethnicity-based subgroup analyses were performed when applicable.
The heterogeneity among studies was assessed using the
I2 statistic and Cochran's Q test. Heterogeneity was
categorized as low (I2≤25%), moderate
(25%<I2<50%) or high (I2≥50%). A
random-effects model was used to pool effect estimates across
studies, as heterogeneity was expected a priori.
Heterogeneity was evaluated using Cochran's Q test and the
I2 statistic. Sensitivity analysis was performed to
evaluate the robustness of the results by excluding studies one at
a time and evaluating deviations from the Hardy-Weinberg
equilibrium (HWE) in the control group. Publication bias was
assessed using Egger's test and Begg's test. To account for the
limited statistical power, a P<0.10 threshold was adopted as
evidence of suggestive bias, following previous genetic association
studies (21,27,28).
Cumulative evidence assessment
Epidemiological credibility of significant
associations was assessed using the Venice criteria (29–31).
The evidence for each polymorphism was graded based on three
criteria: i) Replication of the association; ii) protection from
bias; and iii) the amount of evidence. Each criteria was graded A,
B or C. A strong association was defined by all A grades, moderate
by a mix of A and B grades and weak by any C grade.
The false-positive report probability (FPRP) was
calculated to assess the likelihood of a false-positive finding.
The FPRP values were classified as strong (FPRP <0.05), moderate
(0.05≤ FPRP ≤0.20) or weak (FPRP >0.20) (30).
Functional annotation
Functional annotation was performed to assess the
potential regulatory effects of the polymorphisms on gene
expression. The present study used the ENCODE project database tool
HaploReg v4.1 (https://pubs.broadinstitute.org/mammals/haploreg/haploreg.php)
and the UCSC Genome browser (10-kb view; http://genome.ucsc.edu/) to evaluate the regions of
promoter or enhancer activity, DNase I hypersensitivity, histone
modification patterns and transcription factor binding sites
(32,33). Additionally, the present study
explored genome-wide cis-eQTL data from the Genotype-Tissue
Expression project (GTEx Portal Release V10; dbGaP accession
phs000424.v10.p2; http://gtexportal.org/home) and the Multiple Tissue
Human Expression Resource project (MuTHER; ArrayExpress accession
E-TABM-1140; http://www.muther.ac.uk/Data.html) to assess whether
the identified single nucleotide polymorphisms (SNPs) influence
gene expression across various tissues (34,35).
Data from Phase 3 of the 1000 Genomes Project (https://www.internationalgenome.org/)
(36) were accessed and analyzed
using the LDlink web-based application (https://ldlink.nih.gov/) (37) to examine the genetic structure of
the significant SNPs in different populations.
Results
Characteristics of eligible
studies
As shown in Fig. 1,
Web of Science, Medline and PubMed were used to identify associated
studies, retrieving 3,465 articles. After removing 1,089 duplicates
and excluding 2,233 articles based on titles and abstracts, 143
full-text articles were assessed for eligibility, of which 33 were
excluded after full-text review. Additionally, 5 articles were
added from examining references. Ultimately, 115 studies, with
36,368 cases and 40,246 controls, on the associations of MMP-1,
MMP-3 and MMP-8 polymorphisms with cancer risk were included. The
basic characteristics of these studies, including author,
publication year, ethnicity, cancer type, gene type, genotyping
data and sample size, are detailed in Table SI. These studies examined 43
variants and risk in 23 cancer types.
Main meta-analyses
This meta-analysis investigated the associations of
MMP-1, MMP-3 and MMP-8 polymorphisms with tumor susceptibility.
Table SII summarizes the results.
Significant associations were found for three variants: Two in
MMP-3 (rs35068180 and rs3025058) and one in MMP-1 (rs1799750).
Specifically, as shown in Table I,
MMP-1 rs1799750 showed significant associations with seven types of
cancer; it was associated with bladder cancer risk in the overall
population under the recessive model (OR, 1.739; 95% CI,
1.074–2.816; P=0.024) and colorectal cancer risk under all models
in the overall population, and significant associations with
colorectal cancer risk were observed under the allelic and dominant
models in Asian populations and under the allelic and recessive
models in Caucasian populations. This SNP was also associated with
esophageal cancer risk under the dominant model (OR, 1.367; 95% CI,
1.043–1.67; P=0.024) and with gastric cancer risk in Asian
individuals (allelic model: OR, 1.121; 95% CI, 1.005–1.251;
P=0.041). Additionally, significant associations were found with
glioblastoma in the overall population (allelic model: OR, 1.756;
95% CI, 1.439–2.144; P<0.001; dominant model: OR, 1.877;95% CI,
1.279–2.755; P=0.001; recessive model: OR, 2.036; 95% CI,
1.444–2.869; P<0.001) and nasopharyngeal cancer (allelic model:
OR, 1.382; 95% CI, 1.097–1.741; P=0.006) in the overall
populations, as well as in Asian individuals under the allelic and
recessive models. MMP-1 rs1799750 was also linked to renal cancer
risk in the overall population under the allelic (OR, 1.351; 95%
CI, 1.149–1.590; P<0.001) and recessive (OR, 1.674; 95% CI,
1.351–2.073; P<0.001) models.
 | Table I.Significant associations between
variants in MMP-1, MMP-3 and MMP-8 with cancer risk. |
Table I.
Significant associations between
variants in MMP-1, MMP-3 and MMP-8 with cancer risk.
|
|
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| Number
evaluation |
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|---|
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|
|
|
|
| Random-effects
model meta-analysis for risk |
|
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|---|
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|
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|
|
|
| Sample size
(cases/controls) |
| Venice
criteriac | FPRPd | Credibility of
evidencee |
|---|
| MMP | rs number | Alleles | Cancer type | Ethnicity | MAFa | N | Genetic models | OR (95% CI) | P-value | I2 |
PQb |
|---|
| MMP-1 | rs1799750 | 2Gvs1G | Bladder | Overall | 0.5215 | 4 | 2,146 | Recessive | 1.739 | 0.024 | 81.7 | 0.001 | BCC | 0.629 | Weak |
|
|
|
|
|
|
|
| (1,098/1,048) |
| (1.074–2.816) |
|
|
|
|
|
|
| MMP-1 | rs1799750 | 2Gvs1G | Colorectal | Overall | 0.5882 | 9 | 3,628 | Allelic | 1.319 | 0.001 | 58.8 | 0.013 | ACA | 0.024 | Moderate |
|
|
|
|
|
|
|
| (1,678/1,950) |
| (1.116–1.560) |
|
|
|
|
|
|
| MMP-1 | rs1799750 | 2Gvs1G | Colorectal | Overall | 0.5882 | 9 | 3,628 | Dominant | 1.400 | 0.009 | 28.0 | 0.196 | ABC | 0.198 | Weak |
|
|
|
|
|
|
|
| (1,678/1,950) |
| (1.088–1.803) |
|
|
|
|
|
|
| MMP-1 | rs1799750 | 2Gvs1G | Colorectal | Overall | 0.5882 | 9 | 3,628 | Recessive | 1.429 | 0.002 | 54.6 | 0.024 | ACA | 0.051 | Weak |
|
|
|
|
|
|
|
| (1,678/1,950) |
| (1.141–1.790) |
|
|
|
|
|
|
| MMP-1 | rs1799750 | 2Gvs1G | Colorectal | Asian | 0.7024 | 5 | 1,698 | Allelic | 1.398 | 0.026 | 71.2 | 0.008 | ACA | 0.419 | Weak |
|
|
|
|
|
|
|
| (789/909) |
| (1.041–1.877) |
|
|
|
|
|
|
| MMP-1 | rs1799750 | 2Gvs1G | Colorectal | Asian | 0.7024 | 5 | 1,698 | Dominant | 1.684 | 0.004 | 0.0 | 0.447 | AAA | 0.243 | Moderate |
|
|
|
|
|
|
|
| (789/909) |
| (1.176–2.412) |
|
|
|
|
|
|
| MMP-1 | rs1799750 | 2Gvs1G | Colorectal | Caucasian | 0.4780 | 3 | 1,714 | Allelic | 1.227 | 0.049 | 38.3 | 0.198 | ABA | 0.488 | Weak |
|
|
|
|
|
|
|
| (781/933) |
| (1.001–1.504) |
|
|
|
|
|
|
| MMP-1 | rs1799750 | 2Gvs1G | Colorectal | Caucasian | 0.4780 | 3 | 1,714 | Recessive | 1.403 | 0.009 | 13.4 | 0.315 | BAA | 0.192 | Moderate |
|
|
|
|
|
|
|
| (781/933) |
| (1.090–1.807) |
|
|
|
|
|
|
| MMP-3 | rs35068180 | 6Avs5A | Colorectal | Asian | 0.7977 | 4 | 1,123 | Dominant | 0.421 | 0.001 | 0.0 | 0.843 | AAA | 0.269 | Moderate |
|
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|
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|
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|
| (525/598) |
| (0.255–0.694) |
|
|
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| MMP-1 | rs1799750 | 2Gvs1G | Esophageal | Overall | 0.55 | 4 | 2,197 | Dominant | 1.367 | 0.024 | 30.1 | 0.232 | ABC | 0.049 | Moderate |
|
|
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|
|
| (986/1,121) |
| (1.043–1.67) |
|
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| MMP-3 | rs3025058 | 5Avs6A | Esophageal | Overall | 0.4538 | 3 | 1,620 | Allelic | 1.379 | 0.006 | 57.7 | 0.094 | ACC | 0.122 | Weak |
|
|
|
|
|
|
|
| (754/866) |
| (1.098–1.731) |
|
|
|
|
|
|
| MMP-3 | rs3025058 | 5Avs6A | Esophageal | Overall | 0.4538 | 3 | 1,620 | Dominant | 1.563 | 0.003 | 34.6 | 0.217 | ABA | 0.048 | Strong |
|
|
|
|
|
|
|
| (754/866) |
| (1.165–2.04) |
|
|
|
|
|
|
| MMP-3 | rs3025058 | 5Avs6A | Esophageal | Overall | 0.4538 | 3 | 1,620 | Recessive | 1.566 | 0.037 | 57.4 | 0.096 | BCC | 0.626 | Weak |
|
|
|
|
|
|
|
| (754/866) |
| (1.027–2.387) |
|
|
|
|
|
|
| MMP-1 | rs1799750 | 2Gvs1G | Gastric | Asian | 0.6534 | 8 | 3,997 | Allelic | 1.121 | 0.041 | 18.8 | 0.281 | AAC | 0.440 | Weak |
|
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|
|
|
|
| (1,741/2,256) |
| (1.005–1.251) |
|
|
|
|
|
|
| MMP-1 | rs1799750 | 2Gvs1G | Glioblastoma | Overall | 0.6143 | 3 | 985 | Allelic | 1.756 | <0.001 | 0.0 | 0.941 | AAC | <0.001 | Moderate |
|
|
|
|
|
|
|
| (412/573) |
| (1.439–2.144) |
|
|
|
|
|
|
| MMP-1 | rs1799750 | 2Gvs1G | Glioblastoma | Overall | 0.6143 | 3 | 985 | Dominant | 1.877 | 0.001 | 4.9 | 0.349 | BAC | 0.164 | Weak |
| MMP-1 | rs1799750 | 2Gvs1G | Glioblastoma | Overall | 0.6143 | 3 | 985 |
| 2.036 | <0.001 | 28.3 | 0.248 | BBA | 0.022 | Strong |
|
|
|
|
|
|
|
| (412/573) | Recessive | (1.444–2.869) |
|
|
|
|
|
|
| MMP-1 | rs1799750 | 2Gvs1G | Nasopharyn- | Overall | 0.61 | 4 | 2,626 | Allelic | 1.382 | 0.006 | 64.9 | 0.036 | ACC | 0.132 | Weak |
|
|
|
| geal |
|
|
| (1,262/1,364) |
| (1.097–1.741) |
|
|
|
|
|
|
| MMP-1 | rs1799750 | 2Gvs1G | Nasopharyn- | Overall | 0.61 | 4 | 2,626 | Dominant | 1.387 | 0.022 | 20.4 | 0.287 | AAC | 0.371 | Weak |
|
|
|
| geal |
|
|
| (1,262/1,364) |
| (1.049–1.835) |
|
|
|
|
|
|
| MMP-1 | rs1799750 | 2Gvs1G | Nasopharyn- | Overall | 0.61 | 4 | 2,626 | Recessive | 1.505 | 0.011 | 64.3 | 0.038 | ACC | 0.307 | Weak |
|
|
|
| geal |
|
|
| (1,262/1,364) |
| (1.097–2.066) |
|
|
|
|
|
|
| MMP-1 | rs1799750 | 2Gvs1G | Nasopharyn- | Asian | 0.6014 | 3 | 2,281 | Allelic | 1.390 | 0.033 | 74.4 | 0.02 | ACA | 0.475 | Weak |
|
|
|
| geal |
|
|
| (1,088/1,193) |
| (1.027–1.881) |
|
|
|
|
|
|
| MMP-1 | rs1799750 | 2Gvs1G | Nasopharyn- | Asian | 0.6014 | 3 | 2,281 | Recessive | 1.611 | 0.037 | 76.1 | 0.015 | BCA | 0.645 | Weak |
|
|
|
| geal |
|
|
| (1,088/1,193) |
| (1.030–2.521) |
|
|
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|
|
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| MMP-1 | rs1799750 | 2Gvs1G | Renal | Overall | 0.5803 | 4 | 1,569 | Allelic | 1.351 | <0.001 | 12.8 | 0.328 | AAA | 0.006 | Strong |
|
|
|
|
|
|
|
| (697/872) |
| (1.149–1.590) |
|
|
|
|
|
|
| MMP-1 | rs1799750 | 2Gvs1G | Renal | Overall | 0.5803 | 4 | 1,569 | Recessive | 1.674 | <0.001 | 0.0 | 0.58 | BAA | <0.001 | Strong |
|
|
|
|
|
|
|
| (697/872) |
| (1.351–2.073) |
|
|
|
|
|
|
MMP-3 rs35068180 was significantly associated with
colorectal cancer risk in Asian individuals under the dominant
model (OR, 0.421; 95% CI, 0.255–0.694; P=0.001). MMP-3 rs3025058
showed significant associations with esophageal cancer risk in the
overall population under the allelic model (OR, 1.379; 95% CI,
1.098–1.731; P=0.006), as well as under the dominant (OR, 1.563;
95% CI, 1.165–2.04; P=0.003) and recessive (OR, 1.566; 95% CI,
1.027–2.387; P=0.037) models.
However, MMP-1 rs1799750 showed no significant
associations with breast cancer, head and neck squamous cell
carcinoma, hepatocellular carcinoma, oral cancer, lung cancer,
prostate cancer or ovarian cancer. The MMP-8 rs11225395 variant was
not significantly associated with bladder cancer risk in any
population and MMP-3 rs35068180 had no significant associations
with six other types of cancer.
Cumulative evidence of
association
Cumulative epidemiological evidence was used to
grade variations associated with cancer susceptibility, as shown in
Table I. According to the Venice
criteria, the evidence was evaluated across three categories:
Amount of evidence (17 grade A, 7 grade B and 0 grade C),
replication of association (9 grade A, 5 grade B and 10 grade C)
and protection from bias (13 grade A, 0 grade B and 11 grade C). By
integrating these three dimensions, the overall credibility was
determined: Three associations (specifically SNP-cancer pairs)
reached a ‘strong’ level of evidence, while five and 16
associations were classified as ‘moderate’ and ‘weak’,
respectively.
The FPRP values were used to assess the likelihood
of true associations. A total of seven associations had FPRP values
<0.05 (colorectal cancer and MMP-1 rs1799750 under the allelic
model in the overall population; esophageal cancer and MMP-1
rs1799750 under the dominant model in the overall population;
esophageal cancer and MMP-3 rs3025058 under the dominant model in
the overall population; glioblastoma and MMP-1 rs1799750 in the
overall population under recessive and allelic models; renal cancer
and MMP-1 rs1799750 under recessive and allelic models in the
overall population), six had FPRP values between 0.05 and 0.20, and
11 had values >0.20. Combining the Venice criteria and FPRP
results, the present study identified four strong associations:
MMP-3 rs3025058 was associated with esophageal cancer risk in the
overall population under the dominant model (Fig. 2), MMP-1 rs1799750 was associated
with glioblastoma risk in the overall population under the
recessive model (Fig. 3), and MMP-1
rs1799750 was associated with renal cancer risk in the overall
population under the allelic and recessive models (Figs. 4 and 5). In addition, six associations were
graded as moderate and 14 were graded as weak.
Heterogeneity, bias and sensitivity
analyses
Heterogeneity was systematically evaluated for all
associations included in the current study, with the corresponding
I2 values provided for every entry in Table SII. Among the 24 significant
associations identified (comprising 2 SNPs across 7 types of
cancer), low heterogeneity (I2≤25%) was observed for 9
associations (2 variants and 5 types of cancer), moderate
heterogeneity (25%<I2<50%) for 5 associations (2
variants and 3 types of cancer) and high heterogeneity
(I2≥50%) for 10 associations (2 variants and 4 types of
cancer). No publication bias was found for the majority of
gene-cancer associations (P>0.10), except for MMP-1 rs1799750 in
gastric cancer in Asian individuals and nasopharyngeal cancer in
the overall population under the allelic model (P<0.10).
Sensitivity analysis showed that removing any individual study or
those deviating from HWE did not significantly alter the summary
ORs for the majority of associations. However, significance was
lost for specific associations, including esophageal cancer and
MMP-3 rs3025058 (recessive/allelic model), glioblastoma and MMP-1
rs1799750 (dominant model), and nasopharyngeal cancer and MMP-1
rs1799750 (dominant model), when studies deviating from HWE were
excluded.
Functional annotation for variants
with strong evidence
As shown in Table
II, the present study used HaploReg v4.1 to assess the
functional roles of variants with strong evidence (two SNPs
associated with three types of cancer). rs1799750 is located in
intronic regions and may be in a region with enhancer activity,
DNase I hypersensitivity and regulatory motif alterations. Figs. S1 and S2 show linkage disequilibrium (LD) plots
indicating distinct genetic structures across ancestries.
Specifically, the density and boundaries of LD blocks, as well as
the magnitude of allelic correlation (measured by r2), exhibited
population-specific patterns among individuals of European
(Northern and Western European ancestry), Asian (Han Chinese from
Beijing and Japanese from Tokyo) and African (Yoruba from Nigeria)
descent. The Genotype-Tissue Expression Project data indicate that
rs1799750 is an eQTL for MMP-1, MMP-10 and WTAP pseudogene 1
(WTAPP1). Specifically, rs1799750 is associated with decreased
MMP-1 expression in adipose, artery, breast, esophagus, heart,
lung, tibial nerve and thyroid tissues; decreased MMP-10 expression
in lung tissue; and decreased WTAPP1 expression in testis tissue
(Table SIII).
| Table II.Summary of functional annotations for
two single nucleotide polymorphisms in three cancerous diseases
with strong epidemiological credibility. |
Table II.
Summary of functional annotations for
two single nucleotide polymorphisms in three cancerous diseases
with strong epidemiological credibility.
| Variant | Gene |
Positiona | Annotation | Enhancer histone
marksb | DNAsec | Proteins
boundd | Motifs
changede |
|---|
| rs3025058 | MMP-3 | 102845217 | - | GI and skin | THYM | - | CIZ and Gfi1b |
| rs1799750 | MMP-1 | 102799765 | Intronic | 5 tissues | 5 tissues | CFOS and GATA2 | 21 altered
motifs |
Discussion
MMP-1, MMP-3 and MMP-8 polymorphisms have been
associated with tumor susceptibility, but results have been
inconsistent. The present meta-analysis, based on data from 36,368
patients and 40,246 controls, assessed the associations between
these polymorphisms and tumor susceptibility. A total of three
polymorphisms were significantly associated with cancer risk: MMP-3
rs35068180 and rs3025058, and MMP-1 rs1799750. Using the Venice
criteria and FPRP test, the cumulative evidence showed four strong
associations (MMP-3 rs3025058 with esophageal cancer in the overall
population under the dominant model, MMP-1 rs1799750 with
glioblastoma in the overall population under the recessive model,
and MMP-1 rs1799750 with renal cancer in the overall population
under both the recessive and allelic models), six moderate
associations and 14 weak associations.
MMP-1 (NCBI Gene ID: 4312; http://www.ncbi.nlm.nih.gov/gene/) is expressed in
tumor cells and predicts poor survival (for example, in colorectal,
gastric, pancreatic, lung, breast, prostate, bladder and
oral/head-and-neck cancer) (38).
The MMP-1 rs1799750 (1G>2G) variant, located in the promoter
region, enhances MMP-1 transcription by creating an Ets-binding
site. The 2G allele is associated with active matrix degradation
and tumor progression (39).
Increasing evidence has linked MMP-1 rs1799750 (1G>2G) to
several types of cancer, including bladder, colorectal and gastric
cancer (40–42). The meta-analysis performed in the
present study found that this SNP significantly increases
glioblastoma risk by 2.036-fold in the overall population under the
recessive model (985 samples), with the association upgraded to
strong (FPRP<0.05). Studies suggest that members of the E26
transformation-specific family of transcription factors in glioma
cells bind to the 2G promoter, possibly contributing to
glioblastoma (43,44). Additionally, MMP-1 rs1799750 was
shown to be associated with increased renal cell carcinoma risk
under both allelic and recessive models (1,569 samples), with a
1.351-fold increased risk under the allelic model and a 1.674-fold
increased risk under the recessive model. However, no subgroup
analysis by ethnicity was possible, likely due to the small sample
size. Further studies, particularly in different ethnicity groups,
are warranted.
MMP-3 (Gene ID: 4314) located within the MMP cluster
on chromosome 11q22.3, is expressed by epithelial cells,
fibroblasts and macrophages, contributing to ECM remodeling.
Through broad ECM-degrading activity and activation of other
pro-MMPs, MMP-3 promotes tumor invasion and progression (10,45,46).
The MMP-3 promoter contains a 5A/6A polymorphism at −1612, with the
5A allele showing twice the promoter activity of the 6A allele in
in vitro assays, suggesting that transcription inhibitors
may bind to the 6A allele (47).
The present study found a strong association between MMP-3
rs3025058 and esophageal cancer risk under the dominant model
(1,620 samples), with the 5A allele increasing risk by 1.563-fold
(OR, 1.563; 95% CI, 1.165–2.04). This association was observed in
European populations but is limited by the lack of data on Asian
populations, which warrants further investigation.
In the present study, six associations with cancer
risk were rated as moderate evidence. A total of three associations
(colorectal cancer with MMP-1 rs1799750, esophageal cancer with
MMP-1 rs1799750 and glioblastoma with MMP-1 rs1799750) were
upgraded to strong evidence, graded ‘ACA’, ‘ABC’ and ‘AAC’,
respectively, by the Venice criteria due to replication and
small-study effects. With FPRP<0.05, these were considered
medium-strength associations with tumor susceptibility. In total,
two associations in Asian individuals (colorectal cancer with MMP-1
rs1799750 and MMP-3 rs35068180) were downgraded from strong to
moderate (FPRP>0.2). The remaining associations (colorectal
cancer with MMP-1 rs1799750) were not upgraded or downgraded (0.05≤
FPRP ≤0.20), with the relation rated ‘BAA’ by the Venice criteria
due to the amount of evidence. Further analysis revealed that these
moderate-evidence associations exhibited considerable complexity
across different populations, cancer types and genetic models. A
possible mechanism is that polymorphisms in genes such as MMP-1 and
MMP-3 may influence transcription factor binding, promoter activity
or linked functional variants, thereby affecting MMP expression and
activation in a tissue- and environment-dependent manner and
contributing to cancer susceptibility (48,49).
Tumor heterogeneity, diverse genetic backgrounds among populations,
environmental factors, as well as differences in sample size and
methodological design, may all contribute to the observed
variability (50,51). Therefore, the differential effects
of the same SNP in various populations and cancer types warrant
further systematic investigation in future studies.
In the present study, 14 associations were
classified as weak regarding tumor susceptibility. MMP-1 rs1799750
was significantly associated with several types of cancer
(colorectal cancer, gastric cancer, nasopharyngeal carcinoma,
bladder cancer, esophageal cancer and glioblastoma), although the
association with colorectal cancer in Caucasian individuals under
the allelic model was rated as weak (FPRP=0.488), despite moderate
heterogeneity (‘ABA’ by Venice criteria). Sample size,
environmental factors and methodology may explain variations across
ethnicities. A total of two associations (gastric cancer with MMP-1
rs1799750 and glioblastoma with MMP-1 rs1799750) were also rated as
weak, mainly due to low ORs and HWE deviations; thus, further
studies are needed. MMP-3 rs3025058 (recessive/allelic models)
showed a weak association with esophageal cancer, with Venice
criteria ratings ‘ACC’ and ‘BCC’ due to limited study populations.
No significant association was found between MMP-8 polymorphism and
cancer risk, warranting larger sample sizes for confirmation.
Although the present study identified several
statistically significant associations, the majority of effect
sizes were modest, indicating that individual MMP variants
(including rs1799750) are unlikely to provide sufficient predictive
value as stand-alone clinical markers. For example, even for the
strong association between MMP-1 rs1799750 and glioblastoma, the
pooled effect (OR=2.036 under the recessive model) reflects
relative risk rather than absolute risk and thus does not directly
translate into high positive predictive value (PPV) in the general
population. Instead, the most plausible translational pathway is
risk stratification in high-risk settings, where germline variants
may help refine risk when combined with other determinants. In
future work, rs1799750 and other credible MMP loci could be
incorporated into multi-locus genetic scores or polygenic risk
models and further integrated with established
environmental/clinical factors (for example, smoking, chronic
inflammation, hormonal status or occupational exposures) to improve
discrimination and calibration. Prospective, multi-ethnic cohort
studies that include baseline incidence and exposure data will be
required to estimate absolute risk, PPV and model performance (for
example, area under the curve) and to determine whether adding MMP
variants meaningfully enhances existing prevention or screening
frameworks (52–56).
Overall, the present findings are broadly consistent
with previous meta-analyses, although the strength and direction of
some associations appear to vary across cancer types and
populations (57–60). The present study adds notable value
by incorporating a larger sample size, applying more rigorous
Venice criteria and FPRP evaluation, and conducting more detailed
subgroup analyses by ethnicity and tumor subtype. Notably, the
present study identified context-dependent and ethnicity-specific
associations that were previously unrecognized or only partially
addressed. Previous studies have shown that MMP-1 rs1799750, MMP-3
rs3025058 and other MMP variants may influence cancer
susceptibility, albeit with moderate effect sizes and considerable
heterogeneity across populations and tumor types (48,61).
The present study further refines these associations by applying
rigorous Venice criteria and FPRP testing, supporting strong
epidemiological credibility for only a small subset of associations
(notably, MMP-1 rs1799750 with glioblastoma and renal cancer, as
well as MMP-3 rs3025058 with esophageal cancer). Mechanistically,
MMP-1 and MMP-3 variants may modulate gene expression by altering
promoter or enhancer regions, thus affecting the degradation of ECM
components, tumor cell invasion, angiogenesis and immune cell
infiltration in the tumor microenvironment (45). Functional annotation from ENCODE and
eQTL data performed in the present study suggests that rs1799750
and rs3025058 could influence transcription factor binding and gene
expression across multiple tissues, further supporting their
biological plausibility in tumorigenesis. However, the overall
moderate or weak evidence for the majority of associations in the
present meta-analysis also suggests that MMP variants alone are
unlikely to be major determinants of cancer risk and may interact
with environmental or lifestyle factors such as smoking,
inflammation or hormonal status.
In the present study, associations varied across
ethnicities, genetic models and tumor types. While the majority of
studies focused on Asian populations, research on non-Asian
populations is key. Different genetic models showed varying
associations with tumors, and gaps in research on certain tumor
types highlight the need for larger, more focused studies.
The present study revealed that four SNPs in three
MMPs were not associated with seven types of cancer in any genetic
model and/or ethnicity (Table
III). Among these, two SNPs in two MMPs showed no association
with the risk of two types of cancer in meta-analyses, including a
minimum of 2,000 cases and 2,000 controls, which offers >85%
power to detect an OR of 1.15 under the additive model for a
variant with minor allelic frequency 0.20, Type 1 error 0.05.
Therefore, further research on these SNPs with a similar sample
size may not yield positive results.
| Table III.MMP variants show no relation to
cancer risk in meta-analyses, with at least 2,000 cases and 2,000
controls in the additive model. |
Table III.
MMP variants show no relation to
cancer risk in meta-analyses, with at least 2,000 cases and 2,000
controls in the additive model.
|
|
|
|
|
|
| Number
evaluation |
|
|
|
|
|
|
|
|
|---|
|
|
|
|
|
|
|
| Random-effects
model meta-analysis for risk |
| Power (%) if the
MAF is 0.2 | Power (%) if the
MAF is 0.1 |
|---|
|
|
|
|
|
|
|
| Sample size
(cases/controls) |
|
|
|---|
| MMP | rs number | Alleles | Cancer type | Ethnicity | MAF | N | Genetic Models | OR (95% CI) | P-value | I2 | PQ | Power (%) |
|---|
| MMP-1 | rs1799750 | 2Gvs1G | Breast | Overall | 0.5564 | 6 | 6,127 | Allelic | 1.161 | 0.173 | 82.9 | <0.001 | 96.5 | 87.5 | 65.2 |
|
|
|
|
|
|
|
| (2,979/ |
| (0.937- |
|
|
|
|
|
|
|
|
|
|
|
|
|
| 3,148) |
| 1.439) |
|
|
|
|
|
|
| MMP-1 | rs1799750 | 2Gvs1G | Breast | Asian | 0.5631 | 3 | 5,528 | Allelic | 1.235 | 0.186 | 93.1 | <0.001 | 95.2 | 85.0 | 62.0 |
|
|
|
|
|
|
|
| (2,764/ |
| (0.904- |
|
|
|
|
|
|
|
|
|
|
|
|
|
| 2,764) |
| 1.687) |
|
|
|
|
|
|
| MMP-1 | rs1799750 | 2Gvs1G | Lung | Overall | 0.5316 | 11 | 13,060 | Allelic | 1.087 | 0.075 | 60.6 | 0.005 | 100.0 | 99.7 | 94.2 |
|
|
|
|
|
|
|
| (6,726/ |
| (0.992- |
|
|
|
|
|
|
|
|
|
|
|
|
|
| 6,334) |
| 1.191) |
|
|
|
|
|
|
| MMP-1 | rs1799750 | 2Gvs1G | Lung | Caucasian | 0.4878 | 7 | 9,651 | Allelic | 1.073 | 0.201 | 61.2 | 0.017 | 99.9 | 98.5 | 87.8 |
|
|
|
|
|
|
|
| (5,259/ |
| (0.963- |
|
|
|
|
|
|
|
|
|
|
|
|
|
| 4,392) |
| 1.195) |
|
|
|
|
|
|
| MMP-3 | rs35068180 | 6Avs5A | Lung | Overall | 0.5092 | 4 | 4,872 | Allelic | 0.898 | 0.291 | 63.7 | 0.041 | 95.8 | 85.5 | 62.6 |
|
|
|
|
|
|
|
| (2,800/ |
| (0.735- |
|
|
|
|
|
|
|
|
|
|
|
|
|
| 2,072) |
| 1.097) |
|
|
|
|
|
|
| MMP-3 | rs35068180 | 6Avs5A | Lung | Caucasian | 0.5126 | 3 | 4,780 | Allelic | 0.985 | 0.726 | 0.0 | 0.639 | 95.5 | 85.0 | 61.9 |
|
|
|
|
|
|
|
| (2,759/ |
| (0.907- |
|
|
|
|
|
|
|
|
|
|
|
|
|
| 2,021) |
| 1.070) |
|
|
|
|
|
|
Several limitations of the present study should be
acknowledged. First, the meta-analysis included studies only
published in English, potentially introducing language and
publication bias. Second, the present analysis was primarily
restricted to case-control studies, limiting causal inference. In
addition, the selection of hospital-vs. population-based controls
varied across studies, potentially introducing selection bias, and
survival bias may exist if genotype affects both cancer incidence
and prognosis. Third, subgroup analyses could not account for all
possible confounders, such as age, sex, environmental exposures or
comorbidities, due to limited available data. Furthermore, the
observed heterogeneity (I2≤81.7%) may reflect
differences in population genetic structure, analytical models,
clinical characteristics of included cases and genotyping platforms
across studies. These factors are well-recognized sources of
variability in genetic epidemiology and likely contributed to the
inconsistent effect estimates. Another limitation is that some
ethnicity-stratified analyses were based on a limited number of
studies (for example, the association between MMP-1 rs1799750 and
renal cancer), which restricts the robustness of
population-specific conclusions and warrants caution when
generalizing these findings across diverse ancestral groups.
Additionally, the broad ethnicity classifications (such as Asian
and Caucasian) of the present study may inadequately capture
population substructure and admixture patterns within these groups,
as different subpopulations may exhibit distinct allele frequencies
and LD structures. Fourth, only a subset of functionally annotated
SNPs was investigated and gene-gene or gene-environment
interactions were not systematically evaluated. Moreover, since
individual-level baseline risk and exposure data were unavailable
in the included studies, the present study could not estimate
absolute risk measures or predictive metrics such as PPV, negative
PV or area under the curve for any single SNP. It is also possible
that the analyzed SNPs may represent LD proxies rather than causal
variants themselves and population-specific differences in LD
patterns could contribute to observed heterogeneity. Technical
heterogeneity across studies, including different genotyping
platforms and quality control thresholds, represents a potential
source of systematic bias. To address this and minimize bias, a
strict sensitivity analysis based on HWE was performed. Since
deviations from HWE often indicate genotyping errors, this analysis
acted as a rigorous quality filter. The results demonstrated that
the primary findings, specifically the strong associations between
MMP-1 rs1799750 and glioblastoma and renal cancer, remained robust
after this strict exclusion. This resilience suggests that the core
conclusions of the present study are not driven by technical
variations or platform-specific biases, whereas less robust
associations (which lost significance) should be interpreted with
caution. Additionally, the exclusive focus of the present study on
common variants overlooks potentially important contributions from
rare variants, which may exhibit larger effect sizes and account
for additional heritability in cancer susceptibility. Fifth, the
present study focused only on cancer susceptibility, without
addressing prognosis, response to therapy or functional effects at
the cellular level. Similarly, the present study treated broad
cancer types as homogeneous entities without considering
histological subtypes, molecular classifications or disease stages
that may have distinct genetic architectures. Finally, temporal
changes in diagnostic criteria and treatment protocols across the
present study period spanning multiple decades may affect case
definition consistency.
Future research should include larger, multi-ethnic
cohorts, more comprehensive genotyping, and integrated analyses of
environmental risk factors, lifestyle and somatic mutations.
Experimental studies investigating the functional impact of MMP
variants on protein expression, enzyme activity and tumor biology
are also warranted. Furthermore, emerging genomic and
transcriptomic data from single-cell and spatial profiling
technologies may shed light on the context-dependent effects of MMP
polymorphisms in different tumor microenvironments (62). The present study identified four
strong associations between gene mutations and cancer risk (MMP-3
rs3025058 in esophageal cancer under the dominant model, MMP-1
rs1799750 in glioblastoma under the recessive model, and in renal
cancer under both allelic and recessive models across the overall
population) as well as six moderate associations and 14 weak
associations. These findings may help to identify populations at
increased risk of cancer and provide a comprehensive summary of
current evidence regarding MMP polymorphisms and cancer risk,
thereby offering valuable insights for future studies. Overall, the
present comprehensive meta-analysis highlights the complex and
context-dependent associations between MMP-1, MMP-3 and MMP-8 gene
variants and cancer risk, emphasizing the importance of
multi-dimensional approaches in genetic epidemiology and laying the
groundwork for further exploration of the interplay between ECM
remodeling, genetic susceptibility and cancer development.
Supplementary Material
Supporting Data
Supporting Data
Acknowledgements
Not applicable.
Funding
Funding: No funding was received.
Availability of data and materials
The data generated in the present study may be
requested from the corresponding author.
Authors' contributions
SX and XL conducted the literature search, data
extraction and quality assessment. SX and CH designed the study,
performed the statistical analyses and wrote the manuscript. XL
contributed to the interpretation of the data and critically
revised the manuscript for important intellectual content. SX and
CH confirm the authenticity of all the raw data. All authors have
read and approved the final version of the manuscript.
Ethics approval and consent to
participate
Not applicable.
Patient consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing
interests.
Glossary
Abbreviations
Abbreviations:
|
ECM
|
extracellular matrix
|
|
FPRP
|
false-positive report probability
|
|
HWE
|
Hardy-Weinberg equilibrium
|
|
MMP
|
matrix metalloproteinase
|
|
SNP
|
single nucleotide polymorphism
|
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