临床荟萃 ›› 2023, Vol. 38 ›› Issue (9): 779-787.doi: 10.3969/j.issn.1004-583X.2023.09.002
收稿日期:
2022-09-24
出版日期:
2023-09-20
发布日期:
2023-11-21
通讯作者:
周利明,Email:基金资助:
Received:
2022-09-24
Online:
2023-09-20
Published:
2023-11-21
Contact:
Zhou Liming,Email: 摘要:
目的 分析肿瘤坏死因子-α(TNF-α)启动子308位点多态性与胃癌易感性的关系。方法 检索Pubmed、Embase、Web of Science、Cochrane library、万方数据和中国知网数据库中有关TNF-α-308基因多态性与胃癌发病风险的病例对照研究,时间截至2022年6月。由2位研究者独立进行文献筛选、提取数据及评价偏倚风险,通过Stata16.0软件进行Meta分析。结果 纳入41项病例-对照研究,共7528例胃癌患者和10924例对照。Meta分析结果显示:等位,杂合和显性模型TNF-α-308位点多态性与增加胃癌患病风险相关[A vs G:
中图分类号:
吕畅, 周利明. TNF-α-308基因多态性与胃癌易感相关性的meta分析[J]. 临床荟萃, 2023, 38(9): 779-787.
Lyu Chang, Zhou Liming. Correlation between the TNF-α-308 gene polymorphism and gastric cancer susceptibility: A meta-analysis[J]. Clinical Focus, 2023, 38(9): 779-787.
纳入研究 | 国家 | 地区 | 检测方式 | 病例组 | 对照组 | HWE 检验 | NOS 评分 | |||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
GG | GA | AA | GG | GA | AA | |||||||
Jang 2001[ | 朝鲜 | 亚洲 | RFLP | 46 | 4 | 2 | 85 | 7 | 0 | 0.704 | 8 | |
Wu 2002[ | 中国 | 亚洲 | 直接分型 | 114 | 27 | 9 | 180 | 27 | 13 | <0.01 | 6 | |
El-Omar 2003[ | 美国 | 美洲 | Taqman | 201 | 87 | 26 | 152 | 52 | 6 | 0.548 | 8 | |
Machado 2003[ | 葡萄牙 | 欧洲 | PCR/SSCP | 179 | 105 | 3 | 231 | 69 | 4 | 0.650 | 8 | |
Wu 2003[ | 中国 | 亚洲 | 直接分型 | 176 | 31 | 13 | 185 | 29 | 16 | <0.01 | 8 | |
Glas 2004[ | 德国 | 欧洲 | RFLP | 66 | 19 | 3 | 105 | 36 | 4 | 0.669 | 8 | |
Lee 2004[ | 朝鲜 | 亚洲 | 直接分型 | 297 | 43 | 1 | 218 | 42 | 1 | 0.493 | 8 | |
Wu 2004[ | 中国 | 亚洲 | RFLP | 163 | 29 | 12 | 171 | 26 | 13 | <0.01 | 7 | |
Garza-González 2005[ | 墨西哥 | 美洲 | 直接分型 | 0 | 8 | 55 | 1 | 35 | 179 | 0.608 | 6 | |
Lee 2005[ | 朝鲜 | 亚洲 | RFLP | 112 | 10 | 0 | 103 | 17 | 0 | 0.404 | 8 | |
Li 2005[ | 中国 | 亚洲 | RFLP | 55 | 4 | 0 | 228 | 34 | 2 | 0.560 | 8 | |
Lu 2005[ | 中国 | 亚洲 | DNPLC | 214 | 36 | 0 | 274 | 24 | 2 | 0.081 | 8 | |
Perri 2005[ | 意大利 | 欧洲 | RFLP | 152 | 30 | 2 | 290 | 65 | 7 | 0.146 | 8 | |
Zambon 2005[ | 意大利 | 欧洲 | Taqman | 95 | 31 | 3 | 496 | 138 | 10 | 0.910 | 7 | |
Kamanrar 2006[ | 芬兰 | 欧洲 | Taqman | 86 | 23 | 3 | 154 | 52 | 2 | 0.292 | 6 | |
Kim 2006[ | 朝鲜 | 亚洲 | RFLP | 199 | 34 | 4 | 400 | 59 | 2 | 0.911 | 8 | |
Morgan 2006[ | 洪都拉斯 | 美洲 | Taqman | 151 | 17 | 0 | 149 | 12 | 0 | 0.623 | 8 | |
邢培祥2006[ | 中国 | 亚洲 | 芯片检测 | 36 | 27 | 2 | 50 | 20 | 1 | 0.523 | 8 | |
Hou 2007[ | 波兰 | 欧洲 | Taqman | 186 | 98 | 21 | 304 | 109 | 15 | 0.187 | 8 | |
Sugimoto 2007[ | 日本 | 亚洲 | RFLP | 101 | 4 | 0 | 169 | 3 | 0 | 0.908 | 7 | |
曾庆东2007[ | 中国 | 亚洲 | 芯片检测 | 72 | 54 | 4 | 100 | 40 | 2 | 0.367 | 8 | |
Crusius 2008[ | 欧洲 | 欧洲 | Real-time PCR | 170 | 64 | 2 | 820 | 274 | 31 | 0.165 | 8 | |
Barbosa 2009[ | 巴西 | 美洲 | RFLP | 24 | 5 | 1 | 86 | 13 | 1 | 0.528 | 7 | |
贾皑2009[ | 中国 | 亚洲 | PCR | 96 | 2 | 8 | 91 | 3 | 14 | <0.01 | 7 | |
Burada 2012[ | 罗马 | 欧洲 | Taqman | 78 | 26 | 1 | 196 | 44 | 2 | 0.784 | 8 | |
王美丽2012[ | 中国 | 亚洲 | RFLP | 16 | 75 | 21 | 14 | 64 | 21 | 0.003 | 7 | |
尹东2011[ | 中国 | 亚洲 | PCR | 265 | 43 | 3 | 398 | 79 | 8 | 0.085 | 7 | |
Bhayal 2013[ | 印度 | 亚洲 | ARMSPCR | 32 | 76 | 6 | 76 | 128 | 25 | 0.007 | 8 | |
Hong 2012[ | 中国 | 亚洲 | Taqman | 746 | 179 | 11 | 895 | 156 | 9 | 0.448 | 8 | |
Gonzalez 2014[ | 智利 | 美洲 | Taqman | 128 | 18 | 1 | 147 | 23 | 2 | 0.322 | 8 | |
张军喜2014[ | 中国 | 亚洲 | PCR | 56 | 9 | 0 | 113 | 16 | 1 | 0.608 | 8 | |
Oliveira 2015[ | 巴西 | 美洲 | RFLP | 138 | 66 | 3 | 167 | 69 | 4 | 0.297 | 8 | |
Stubljar 2015[ | 斯洛文尼亚 | 欧洲 | RFLP | 27 | 5 | 0 | 83 | 22 | 3 | 0.312 | 7 | |
Zabaglia 2015[ | 巴西 | 美洲 | RFLP | 17 | 4 | 3 | 33 | 4 | 3 | 0.001 | 6 | |
张德忠2015[ | 中国 | 亚洲 | RFLP | 373 | 112 | 15 | 396 | 95 | 9 | 0.243 | 8 | |
Du 2017[ | 中国 | 亚洲 | RFLP | 204 | 184 | 12 | 326 | 60 | 14 | <0.01 | 7 | |
Xu 2016[ | 中国 | 亚洲 | RFLP | 142 | 66 | 88 | 237 | 50 | 32 | <0.01 | 8 | |
Fukayama 2019[ | 日本 | 亚洲 | RFLP | 120 | 2 | 7 | 101 | 2 | 0 | 0.921 | 8 | |
Bounder 2020[ | 摩洛哥 | 非洲 | 直接测序 | 26 | 6 | 8 | 56 | 4 | 3 | <0.01 | 7 | |
Dantas 2020[ | 巴西 | 美洲 | DSASP | 74 | 0 | 28 | 8 | 41 | 53 | 0.986 | 8 | |
Nezamzadeh 2021[ | 伊朗 | 亚洲 | RFLP | 41 | 7 | 3 | 73 | 5 | 1 | 0.020 | 7 |
表1 纳入研究的基本特征
Tab. 1 Basic characteristics of the included studies
纳入研究 | 国家 | 地区 | 检测方式 | 病例组 | 对照组 | HWE 检验 | NOS 评分 | |||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
GG | GA | AA | GG | GA | AA | |||||||
Jang 2001[ | 朝鲜 | 亚洲 | RFLP | 46 | 4 | 2 | 85 | 7 | 0 | 0.704 | 8 | |
Wu 2002[ | 中国 | 亚洲 | 直接分型 | 114 | 27 | 9 | 180 | 27 | 13 | <0.01 | 6 | |
El-Omar 2003[ | 美国 | 美洲 | Taqman | 201 | 87 | 26 | 152 | 52 | 6 | 0.548 | 8 | |
Machado 2003[ | 葡萄牙 | 欧洲 | PCR/SSCP | 179 | 105 | 3 | 231 | 69 | 4 | 0.650 | 8 | |
Wu 2003[ | 中国 | 亚洲 | 直接分型 | 176 | 31 | 13 | 185 | 29 | 16 | <0.01 | 8 | |
Glas 2004[ | 德国 | 欧洲 | RFLP | 66 | 19 | 3 | 105 | 36 | 4 | 0.669 | 8 | |
Lee 2004[ | 朝鲜 | 亚洲 | 直接分型 | 297 | 43 | 1 | 218 | 42 | 1 | 0.493 | 8 | |
Wu 2004[ | 中国 | 亚洲 | RFLP | 163 | 29 | 12 | 171 | 26 | 13 | <0.01 | 7 | |
Garza-González 2005[ | 墨西哥 | 美洲 | 直接分型 | 0 | 8 | 55 | 1 | 35 | 179 | 0.608 | 6 | |
Lee 2005[ | 朝鲜 | 亚洲 | RFLP | 112 | 10 | 0 | 103 | 17 | 0 | 0.404 | 8 | |
Li 2005[ | 中国 | 亚洲 | RFLP | 55 | 4 | 0 | 228 | 34 | 2 | 0.560 | 8 | |
Lu 2005[ | 中国 | 亚洲 | DNPLC | 214 | 36 | 0 | 274 | 24 | 2 | 0.081 | 8 | |
Perri 2005[ | 意大利 | 欧洲 | RFLP | 152 | 30 | 2 | 290 | 65 | 7 | 0.146 | 8 | |
Zambon 2005[ | 意大利 | 欧洲 | Taqman | 95 | 31 | 3 | 496 | 138 | 10 | 0.910 | 7 | |
Kamanrar 2006[ | 芬兰 | 欧洲 | Taqman | 86 | 23 | 3 | 154 | 52 | 2 | 0.292 | 6 | |
Kim 2006[ | 朝鲜 | 亚洲 | RFLP | 199 | 34 | 4 | 400 | 59 | 2 | 0.911 | 8 | |
Morgan 2006[ | 洪都拉斯 | 美洲 | Taqman | 151 | 17 | 0 | 149 | 12 | 0 | 0.623 | 8 | |
邢培祥2006[ | 中国 | 亚洲 | 芯片检测 | 36 | 27 | 2 | 50 | 20 | 1 | 0.523 | 8 | |
Hou 2007[ | 波兰 | 欧洲 | Taqman | 186 | 98 | 21 | 304 | 109 | 15 | 0.187 | 8 | |
Sugimoto 2007[ | 日本 | 亚洲 | RFLP | 101 | 4 | 0 | 169 | 3 | 0 | 0.908 | 7 | |
曾庆东2007[ | 中国 | 亚洲 | 芯片检测 | 72 | 54 | 4 | 100 | 40 | 2 | 0.367 | 8 | |
Crusius 2008[ | 欧洲 | 欧洲 | Real-time PCR | 170 | 64 | 2 | 820 | 274 | 31 | 0.165 | 8 | |
Barbosa 2009[ | 巴西 | 美洲 | RFLP | 24 | 5 | 1 | 86 | 13 | 1 | 0.528 | 7 | |
贾皑2009[ | 中国 | 亚洲 | PCR | 96 | 2 | 8 | 91 | 3 | 14 | <0.01 | 7 | |
Burada 2012[ | 罗马 | 欧洲 | Taqman | 78 | 26 | 1 | 196 | 44 | 2 | 0.784 | 8 | |
王美丽2012[ | 中国 | 亚洲 | RFLP | 16 | 75 | 21 | 14 | 64 | 21 | 0.003 | 7 | |
尹东2011[ | 中国 | 亚洲 | PCR | 265 | 43 | 3 | 398 | 79 | 8 | 0.085 | 7 | |
Bhayal 2013[ | 印度 | 亚洲 | ARMSPCR | 32 | 76 | 6 | 76 | 128 | 25 | 0.007 | 8 | |
Hong 2012[ | 中国 | 亚洲 | Taqman | 746 | 179 | 11 | 895 | 156 | 9 | 0.448 | 8 | |
Gonzalez 2014[ | 智利 | 美洲 | Taqman | 128 | 18 | 1 | 147 | 23 | 2 | 0.322 | 8 | |
张军喜2014[ | 中国 | 亚洲 | PCR | 56 | 9 | 0 | 113 | 16 | 1 | 0.608 | 8 | |
Oliveira 2015[ | 巴西 | 美洲 | RFLP | 138 | 66 | 3 | 167 | 69 | 4 | 0.297 | 8 | |
Stubljar 2015[ | 斯洛文尼亚 | 欧洲 | RFLP | 27 | 5 | 0 | 83 | 22 | 3 | 0.312 | 7 | |
Zabaglia 2015[ | 巴西 | 美洲 | RFLP | 17 | 4 | 3 | 33 | 4 | 3 | 0.001 | 6 | |
张德忠2015[ | 中国 | 亚洲 | RFLP | 373 | 112 | 15 | 396 | 95 | 9 | 0.243 | 8 | |
Du 2017[ | 中国 | 亚洲 | RFLP | 204 | 184 | 12 | 326 | 60 | 14 | <0.01 | 7 | |
Xu 2016[ | 中国 | 亚洲 | RFLP | 142 | 66 | 88 | 237 | 50 | 32 | <0.01 | 8 | |
Fukayama 2019[ | 日本 | 亚洲 | RFLP | 120 | 2 | 7 | 101 | 2 | 0 | 0.921 | 8 | |
Bounder 2020[ | 摩洛哥 | 非洲 | 直接测序 | 26 | 6 | 8 | 56 | 4 | 3 | <0.01 | 7 | |
Dantas 2020[ | 巴西 | 美洲 | DSASP | 74 | 0 | 28 | 8 | 41 | 53 | 0.986 | 8 | |
Nezamzadeh 2021[ | 伊朗 | 亚洲 | RFLP | 41 | 7 | 3 | 73 | 5 | 1 | 0.020 | 7 |
图2 基于不同地区对TNF-α-308多态性与胃癌易感性的森林图(等位模型,A vs G) 注:点表示各项研究的OR值;横线表示95%${CI}$;底部菱形表示合并后的OR值和95%${CI}$
Fig.2 Forest plot of TNF-α-308 polymorphism and gastric cancer susceptibility based on different regions (Allele model, A vs G) Note: Dots indicate OR of each study; horizontal lines indicate 95%${CI}$; bottom diamonds indicate combined OR and 95%${CI}$
图3 基于不同地区对TNF-α-308多态性与胃癌易感性的森林图(杂合模型,AG vs GG)
Fig. 3 Forest plot of TNF-α-308 polymorphism and gastric cancer susceptibility based on different regions (Heterozygote model, AG vs GG)
图4 基于不同地区对TNF-α-308多态性与胃癌易感性的森林图(显性模型,AA+AG vs GG)
Fig. 4 Forest plot of TNF-α-308 polymorphism and gastric cancer susceptibility based on different regions (Dominant model, AA+AG vs GG)
图5 基于不同地区对TNF-α-308多态性与胃癌易感性的森林图(纯合模型,AA vs GG)
Fig. 5 Forest plot of TNF-α-308 polymorphism and gastric cancer susceptibility based on different regions (Homozygote model, AA vs GG)
图6 基于不同地区对TNF-α-308多态性与胃癌易感性的森林图(隐性模型,AA vs AG+GG)
Fig. 6 Forest plot of TNF-α-308 polymorphism and gastric cancer susceptibility based on different regions (Recessive model, AA vs AG+GG)
图7 TNF-α-308多态性与胃癌易感性的敏感性分析 a:等位模型,A vs G; b:纯合模型,AA vs GG; c:杂合模型,AG vs GG; d: 显性模型,AA+AG vs GG; e: 隐性模型,AA vs AG+GG 注:圆圈表示剔除该项研究后的合并 O R值;横线表示置信区间的上下边界
Fig. 7 Sensitivity analysis of TNF-α-308 polymorphism and susceptibility to gastric cancer a:Allele model, A vs G; b:Homozygote model, AA vs GG; c:Heterozygote model, AG vs GG; d:Dominant model, AA+AG vs GG; e:Recessive model, AA vs AG+GG Note: Circles indicate the combined O R after excluding the study; horizontal lines indicate the upper and lower bounds of the confidence intervals
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