| Literature DB >> 25890491 |
Xiaoli Zhang1, Guoyin Jin2, Jianfeng Li3, Linxi Zhang1.
Abstract
BACKGROUND: The role of matrix metalloproteinase 9 (MMP-9) polymorphisms in breast cancer risk remains unclear. The purpose of this study was to evaluate the association between MMP-9 variants and breast cancer susceptibility.Entities:
Mesh:
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Year: 2015 PMID: 25890491 PMCID: PMC4413812 DOI: 10.12659/MSM.893890
Source DB: PubMed Journal: Med Sci Monit ISSN: 1234-1010
Figure 1Process of study selection.
Main characteristics of included studies.
| First author | Year | Country | Ethnicity | Design | Total number | Genotyping method | SNP | |
|---|---|---|---|---|---|---|---|---|
| Cases | Controls | |||||||
| Holliday DL | 2007 | UK | Caucasian | HCC | 13 | 19 | PCR | rs3918242 |
| Lei HX | 2007 | Sweden | Caucasian | PCC | 959 | 952 | TaqMan | rs3918242 |
| Roehe AV | 2007 | Brazil | Caucasian | HCC | 96 | 100 | PCR-RFLP | rs3918242 |
| Sadeghi M | 2009 | Iran | Caucasian | HCC | 90 | 100 | PCR-RFLP | rs3918242 |
| Chahil JK | 2013 | Malaysia | Caucasian | HCC | 80 | 80 | PCR-RFLP | rs17576, rs2250889 |
| Fu FM | 2013 | China | Asian | PCC | 251 | 255 | PCR-RFLP | rs17576, rs2250889, rs3787268 |
| Resler AJ | 2013 | US | Caucasian | PCC | 845 | 807 | PCR-RFLP | rs17576 |
| Slattery ML | 2013 | US, Mexico | Caucasian | HCC | 3553 | 4132 | PCR | rs3787268 |
| Chiranjeevi P | 2014 | India | Caucasian | PCC | 200 | 191 | AS-PCR | rs3918242 |
| Wang XW | 2014 | China | Asian | HCC | 90 | 90 | PCR-RFLP | rs3918242 |
PCC – population-based case-control; HCC – hospital-based case-control; PCR-RFLP – polymerase chain reaction- restriction fragment length polymorphism; AS-PCR – allele-specific polymerase chain reaction; SNP – single nucleotide polymorphism.
Alleles and genotypes distribution for each SNP among included studies.
| First author | Cases | Controls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| rs3918242 | CC | CT | TT | C | T | CC | CT | TT | C | T |
| Holliday DL | 10 | 3 | 0 | 23 | 3 | 15 | 4 | 0 | 34 | 4 |
| Lei HX | 682 | 239 | 25 | 1603 | 289 | 692 | 240 | 14 | 1624 | 268 |
| Roehe AV | 76 | 20 | 0 | 172 | 20 | 83 | 15 | 2 | 181 | 19 |
| Sadeghi M | 57 | 28 | 5 | 142 | 38 | 91 | 9 | 0 | 191 | 9 |
| Chiranjeevi P | 73 | 66 | 61 | 212 | 188 | 86 | 68 | 37 | 240 | 142 |
| Wang XW | 46 | 30 | 14 | 122 | 58 | 38 | 34 | 18 | 110 | 70 |
| rs17576 | GG | GA | AA | G | A | GG | GA | AA | G | A |
| Chahil JK | 50 | 26 | 4 | 126 | 34 | 37 | 29 | 15 | 103 | 59 |
| Fu FM | 139 | 98 | 14 | 376 | 126 | 144 | 93 | 18 | 381 | 129 |
| Resler AJ | 338 | 393 | 106 | 1069 | 605 | 366 | 357 | 78 | 1089 | 513 |
| rs2250889 | CC | CG | GG | C | G | CC | CG | GG | C | G |
| Chahil JK | 1 | 18 | 61 | 20 | 140 | 8 | 27 | 45 | 43 | 117 |
| Fu FM | 154 | 87 | 8 | 395 | 103 | 156 | 82 | 17 | 394 | 116 |
| rs3787268 | GG | GA | AA | G | A | GG | GA | AA | G | A |
| Fu FM | 85 | 120 | 46 | 290 | 212 | 72 | 127 | 56 | 271 | 239 |
| Slattery ML | 2479 | 1074- | 2930 | 1202- | ||||||
Meta-analysis of polymorphisms on MMP9 and breast cancer risk.
| SNP | N | Comparison | OR (95% CI) | P | Ph | I2 | Model |
|---|---|---|---|---|---|---|---|
| rs3918242 | 6 | T | 1.36 (0.91, 2.02) | 0.13 | 0.002 | 79% | R |
| 5 | TT | 1.43 (0.72, 2.86) | 0.30 | 0.05 | 59% | R | |
| 6 | TT+CT | 1.38 (0.88, 2.16) | 0.16 | 0.001 | 75% | R | |
| 5 | TT | 1.55 (1.12, 2.16) | 0.009 | 0.09 | 50% | F | |
| rs17576 | 3 | A | 0.88 (0.58, 1.34) | 0.55 | 0.001 | 85% | R |
| 3 | AA | 0.71 (0.27, 1.89) | 0.49 | 0.003 | 83% | R | |
| 3 | AA+GA | 0.96 (0.64, 1.43) | 0.84 | 0.02 | 73% | R | |
| 3 | AA | 0.72 (0.31, 1.71) | 0.46 | 0.05 | 79% | R | |
| rs2250889 | 2 | G | 0.61 (0.27, 1.36) | 0.23 | 0.01 | 83% | R |
| 2 | GG | 1.96 (0.09, 45.03) | 0.67 | 0.006 | 87% | R | |
| 2 | GG+CG | 2.28 (0.27, 18.95) | 0.45 | 0.04 | 76% | R | |
| 2 | GG | 1.10 (0.21, 5.72) | 0.91 | 0.003 | 89% | R | |
| rs3787268 | 2 | AA+GA | 0.95 (0.71, 1.27) | 0.74 | 0.11 | 61% | R |
N – number of included studies for a certain polymorphism; F – fixed-effect model; R – random-effect model; P – p-value of included studies; Ph – heterogeneity among included studies.
Figure 2Forest plot of MMP-9 rs3918242 in breast cancer risk under all genetic models. (A) allele model (T vs. C); (B) homozygous model (TT vs. CC); (C) dominant model (TT+CT vs. CC); (D) recessive model (TT vs. CT+CC).
Figure 3Forest plot of MMP-9 rs17576 in breast cancer risk. (A) allele model (A vs. G); (B) homozygous model (AA vs. GG); (C) dominant model (AA+GA vs. GG); (D) recessive model (AA vs. GA+GG).
Figure 4Association between MMP-9 rs2250889 and breast cancer risk in dominant model (A: GG+CG vs. CC) and recessive model (B: GG vs. CG+CC).
Figure 5Forest plot of MMP-9 rs3787268 in breast cancer risk under dominant model (AA+GA vs. GG).
Figure 6Funnel plot of MMP-9 rs3918242 in breast cancer risk under recessive model.