| Literature DB >> 25326754 |
Zhong Xu1, Lingling Zhang2, Hui Cao3, Banjun Bai4.
Abstract
BACKGROUND: Evidence has shown that single nucleotide polymorphism located in pre-miRNA or mature microRNA may modify various biological processes and affect the processing of carcinogenesis. Published results about the association between miR-146a rs2910164 G/C polymorphism and human gastric cancer susceptibility are inconclusive. The aim of this study was to acquire a more precise effect of the association between the miR-146a rs2910164 polymorphism and gastric risk by meta-analysis.Entities:
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Year: 2014 PMID: 25326754 PMCID: PMC4411698 DOI: 10.1186/s12881-014-0117-2
Source DB: PubMed Journal: BMC Med Genet ISSN: 1471-2350 Impact factor: 2.103
Figure 1Flow chart of Studies inclusion and exclusion.
Characteristics of studies included in the meta-analysis
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| Okubo | 2010 | Japan | PCR-RFLP | 552 | 697 | 236 | 243 | 73 | 254 | 322 | 121 | 0.2776 |
| Zeng | 2010 | China | PCR-RFLP | 304 | 304 | 89 | 153 | 62 | 119 | 132 | 53 | 0.1223 |
| Hishida | 2011 | Japan | PCR-CTPP | 583 | 1637 | 230 | 271 | 82 | 633 | 775 | 229 | 0.7381 |
| Zhou F | 2012 | China | TaqMan MGB | 1686 | 1895 | 286 | 822 | 578 | 393 | 951 | 551 | 0.6407 |
| Zhou Y | 2012 | China | Locus-specific PCR | 299 | 416 | 107 | 146 | 46 | 127 | 215 | 74 | 0.301 |
| Ahn | 2013 | Korea | PCR-RFLP | 461 | 447 | 159 | 231 | 71 | 164 | 221 | 62 | 0.3618 |
| Dikeakos | 2014 | Greece | PCR-RFLP | 163 | 480 | 105 | 45 | 13 | 307 | 149 | 24 | 0.2892 |
| Kupcinskas | 2014 | Europe | Real Time-PCR | 362 | 347 | 16 | 94 | 252 | 16 | 108 | 223 | 0.5311 |
| Pu | 2014 | China | PCR-RFLP | 197 | 513 | 65 | 96 | 36 | 143 | 274 | 96 | 0.0801 |
Meta-analysis of miR-146a rs2910164 polymorphism with gastric cancer by population
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| Asian | GG vs. CC | 7 | 1.016(0.780, 1.324) | 0.906 | 0.000 | 75.2% |
| CG vs. CC | 0.998(0.847, 1.176) | 0.982 | 0.014 | 62.2% | ||
| CG/GG vs. CC | 1.005(0.830, 1.216) | 0.962 | 0.001 | 75.1% | ||
| GG vs. CG/CC | 1.025(0.861, 1.221) | 0.779 | 0.036 | 55.6% | ||
| G vs. C | 1.006(0.877, 1.153) | 0.935 | 0.000 | 78.7% | ||
| European | GG vs. CC | 2 | 1.331(0.802, 2.210) | 0.269 | 0.512 | 0.0% |
| CG vs. CC | 0.880(0.619, 1.253) | 0.478 | 0.973 | 0.0% | ||
| CG/GG vs. CC | 0.994(0.716, 1.380) | 0.971 | 0.875 | 0.0% | ||
| GG vs. CG/CC | 1.326(0.995, 1.767) | 0.054 | 0.512 | 0.0% | ||
| G vs. C | 1.146(0.937, 1.401) | 0.184 | 0.608 | 0.0% | ||
| Overall | GG vs. CC | 9 | 1.056(0.836, 1.334) | 0.649 | 0.001 | 68.1% |
| CG vs. CC | 0.984(0.853, 1.136) | 0.830 | 0.037 | 51.3% | ||
| CG/GG vs. CC | 1.005(0.852, 1.184) | 0.956 | 0.002 | 66.9% | ||
| GG vs. CG/CC | 1.076(0.925, 1.251) | 0.342 | 0.052 | 48.0% | ||
| G vs. C | 1.028(0.914, 1.155) | 0.648 | 0.000 | 72.5% | ||
Figure 2Meta-analysis of miR-146a rs2910164 polymorphism and gastric cancer susceptibility for recessive model (GG vs. CG + CC).
Meta-analysis of miR-146a rs2910164 polymorphism with gastric cancer in Chinese
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| GG vs. CC | 1.113(0.773, 1.604) | 0.564 | 0.011 | 73.1% |
| CG vs. CC | 1.045(0.783, 1.394) | 0.766 | 0.012 | 72.8% |
| CG/GG vs. CC | 1.065(0.779, 1.457) | 0.692 | 0.002 | 79.4% |
| GG vs. CG/CC | 1.193(1.057, 1.346) | 0.004 | 0.201 | 35.1% |
| G vs. C | 1.055(0.868, 1.283) | 0.590 | 0.004 | 77.8% |
Figure 3Meta-analysis of miR-146a rs2910164 polymorphism and gastric cancer susceptibility in Chinese for recessive model (GG vs. CG + CC).
Meta-analysis of rs2910164 polymorphism with GC by Clinicopathological characters
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| Gender | CC vs. GG/CG | 4 | 0.916 (0.691, 1.215) | 0.542 | 0.201 | 37.7% |
| (Male vs. Female) | CC/CG vs. GG | 4 | 0.939 (0.767, 1.148) | 0.538 | 0.967 | 0.0% |
| Smoking (No vs. Yes) | CC vs. CG/GG | 2 | 0.843 (0.554, 1.282) | 0.425 | 0.315 | 0.9% |
| Site (Cardic vs. Non-Cardic) | CC vs. CG/GG | 4 | 0.844 (0.593, 1.201) | 0.346 | 0.382 | 0.0% |
| CC/CG vs. GG | 4 | 0.897 (0.738, 1.090) | 0.274 | 0.549 | 0.0% | |
| Metastasis of lymph node (Negative vs. Positive) | CC vs. CG/GG | 2 | 1.211(0.842, 1.742) | 0.302 | 0.589 | 0.0% |
| Laurèn’s classification (Intestinal vs. Diffuse) | CC/CG vs. GG | 4 | 0.961(0.807, 1.145) | 0.658 | 0.655 | 0.0% |
Figure 4The influence of each study by omission of individual studies in China for recessive model.
Figure 5Begg’s funnel plot with pseudo 95% confidence limits.
Figure 6Egger’s publication bias plot.
Meta-analyses of rs2910164 polymorphism and cancer risk (information about GC)
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| Wang J [ | 2012 | √ | √ | √ | |||
| Wang F [ | 2012 | √ | √ | √ | |||
| Wu [ | 2013 | √ | √ | √ | |||
| Xu X [ | 2014 | √ | √ | √ | √ | √ | √ |
| Qiu [ | 2011 | √ | √ | √ | |||
| He [ | 2012 | √ | √ | √ | √ | ||
| Xu Y [ | 2013 | √ | √ | √ | √ | ||
| Yin [ | 2013 | √ | √ | √ | √ | ||