Edited by: Stephen J Pandol, Cedars-Sinai Medical Center, United States
Reviewed by: Savio George Barreto, Medanta The Medicity, India; Meghit Boumediene Khaled, University of Sidi-Bel-Abbès, Algeria
This article was submitted to Gastrointestinal Sciences, a section of the journal Frontiers in Physiology
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Tumor necrosis factor (TNF)-α, a major part in inflammatory, infectious and tumor processes, and is pivotal at the early stages of gastric cancer. Relationship between its risk and
Gastric cancer (GC) is the fourth major malignancy and the second dominant cause of cancer-induced death in the world (de Martel et al.,
Tumor necrosis factor (TNF)-α belonging to the TNF/TNF receptor cytokine superfamily can be found in plasma or serum of healthy people, as well as some cancer patients (Balkwill,
Two investigators systematically searched PubMed, Elsevier, EMBASE, and CNKI through Oct 5, 2017 to identify relevant studies using the following search terms: “Gastric Neoplasm,” “Stomach Cancer,” “Gastric Cancer,” “Gastric Carcinoma,” “Gastric Adenocarcinoma,” “tumor necrosis factor alpha,” “
Inclusion criteria were: (1) evaluation of relationship between GC risk and TNF-α rs361525 polymorphism; (2) study on humans; (3) provision of enough data for computation of odds ratios (ORs) and 95% confidence intervals (CIs); (4) case- control study. Exclusion criteria were: (1) duplication; (2) case report or review; (3) lack of genotype data; (4) irrelevant topic.
From each included study, data including name of first author, country of origin, publication year, ethnicity, age, and genotype numbers in cases and controls was extracted. When more than one ethnicity were involved, genotype data was processed separately. Data extraction and study quality assessment based on the Newcastle-Ottawa Scale (NOS) (Stang,
Genotype data of TNF-α rs361525 polymorphism and its mRNA expression data were available from the International HapMap Project and GTex portal (
The strength of relationship between GC risk and
The process of study selection is shown in Figure
Selection for eligible papers included in this meta-analysis.
Characteristics of included studies.
Jang et al., |
N/A | N/A | N/A | N/A | HB | Korea | Asian | 52/92 | PCR-RFLP | 0.391 | 6 |
Wu et al., |
N/A | N/A | N/A | N/A | HB | Taiwan | Asian | 150/220 | Sequencing | <0.001 | 5 |
Wu et al., |
84/136 | 88/142 | 60.9 ± 12.6 | 60.7 ± 13.4 | HB | Taiwan | Asian | 220/230 | Sequencing | <0.001 | 6 |
Glas et al., |
71/74 | 41/47 | 65 ± 12.5 | 45 ± 12.5 | HB | Germany | Caucasian | 145/88 | PCR-RFLP | 0.635 | 6 |
Lee et al., |
142/199 | 123/138 | 46.0 ± 12.6 | 48.7 ± 10.9 | HB | Korea | Asian | 341/261 | Sequencing | 0.416 | 6 |
Wu et al., |
78/126 | 84/126 | 60.1 ± 12.1 | 58.7 ± 14.4 | HB | Taiwan | Asian | 204/210 | Sequencing | <0.001 | 6 |
Lu et al., |
67/183 | 83/217 | 59.0 ± 12.3 | 59.1 ± 9.4 | PB | China | Asian | 250/300 | DHPLC | 0.49 | 7 |
Zambon et al., |
N/A | N/A | N/A | N/A | HB | Italy | Caucasian | 129/644 | TaqMan | 0.378 | 6 |
Kamangar et al., |
N/A | N/A | N/A | N/A | PB | Finland | Caucasian | 210/115 | TaqMan | <0.001 | 7 |
Zambon et al., |
60/70 | 72/70 | 58.6 ± 13.3 | 53.5 ± 11.2 | HB | China | Asian | 130/142 | gene chip | 0.23 | 6 |
Hou et al., |
103/202 | 152/275 | <50 39 |
<50 52 |
PB | Poland | Caucasian | 299/412 | TaqMan | 0.492 | 6 |
Garcia-Gonzalez et al., |
146/258 | 138/266 | 73.7 ± 10.3 | 71.3 ± 12.0 | HB | Spain | Caucasian | 404/404 | TaqMan | 0.011 | 6 |
Zeng et al., |
60/70 | 72/70 | 59.0 ± 13.0 | 54.0 ± 11.0 | HB | China | Asian | 130/142 | gene chip | 0.23 | 7 |
Crusius2008 | N/A | N/A | N/A | N/A | PB | Europe | Caucasian | 235/1123 | TaqMan | 0.367 | 8 |
Yang et al., |
25/59 | 100/236 | ≤ 63 43 |
≤63 176 |
PB | Korea | Asian | 83/331 | SNaPshot | 0.457 | 6 |
Bai et al., |
50/64 | 56/63 | 58.3 ± 12.5 | 55.9 ± 14.9 | HB | China | Asian | 114/119 | gene chip | 0.668 | 6 |
Whiteman et al., |
22/247 | 459/896 | <49 21 |
<49 216 |
PB | Australia | Caucasian | 289/1299 | gene chip | 0.007 | 7 |
Yin et al., |
N/A | N/A | N/A | N/A | HB | China | Asian | 310/485 | SNaPshot | 0.369 | 6 |
Essadik et al., |
N/A | N/A | N/A | N/A | PB | Morocco | Caucasian | 93/74 | Sequencing | 0.978 | 7 |
Xu et al., |
169/127 | 180/139 | 44.0 ± 16.6 | 44.3 ± 15.9 | HB | China | Asian | 294/319 | PCR-RFLP | 0.466 | 6 |
The results concerning the relationship between
Meta-analysis of association between TNF-α rs361525 polymorphism and gastric cancer.
A vs. G | 1.06(0.83,1.35) | 0.646 | 1.000 | <0.001 | 66.2 | Random |
AA+GA vs. GG | 1.06(0.83,1.36) | 0.657 | 1.000 | <0.001 | 63.1 | Random |
AA vs. GA+GG | 1.14(0.70,1.85) | 0.782 | 0.782 | 0.053 | 42.4 | Fixed |
AA vs. GG | 1.12(0.69,1.83) | 0.644 | 1.000 | 0.047 | 43.5 | Fixed |
GA vs. GG | 1.05(0.81,1.34) | 0.733 | 0.917 | <0.001 | 60.2 | Random |
Summary of the subgroup analyses in this meta-analysis.
A vs. G | Ethnicity | Asian | 12 | 0.002 | 0.054 | 32.8 | |
Caucasian | 8 | 0.043 | 0.106 | 56.4 | |||
SOC | HB | 13 | 1.26(0.97, 1.64) | 0.084 | 0.117 | 55.3 | |
PB | 7 | 0.77(0.49, 1.22) | 0.271 | 0.265 | 77.2 | ||
HWE | Positive | 14 | 1.19(0.90, 1.59) | 0.226 | 0.183 | 65.7 | |
Negative | 6 | 0.76(0.57, 1.00) | 0.051 | 0.019 | 14.8 | ||
Genotyping | PCR-RFLR | 3 | 0.80(0.35, 1.80) | 0.582 | 0.299 | 58.6 | |
Sequencing | 5 | 0.80(0.45, 1.43) | 0.455 | 0.223 | 53.7 | ||
TagMan | 5 | 0.86(0.62, 1.19) | 0.355 | 0.063 | 47.8 | ||
Other methods | 7 | 0.033 | 0.200 | 71.1 | |||
NOS score | 5 ≤ Score ≤ 6 | 14 | 1.22(0.97, 1.53) | 0.054 | 0.047 | 42.5 | |
Score > 6 | 6 | 0.75(0.41, 1.37) | 0.185 | <0.001 | 83.2 | ||
GA+AA vs. GG | Ethnicity | Asian | 12 | 0.007 | 0.086 | 40.4 | |
Caucasian | 8 | 0.043 | 0.082 | 47.3 | |||
SOC | HB | 13 | 1.25(0.95, 1.65) | 0.111 | 0.122 | 52.6 | |
PB | 7 | 0.79(0.50, 1.24) | 0.301 | 0.253 | 69.6 | ||
HWE | Positive | 14 | 1.16(0.85, 1.57) | 0.357 | 0.216 | 67.0 | |
Negative | 6 | 0.80(0.62, 1.04) | 0.095 | <0.001 | 0.0 | ||
Genotyping | PCR-RFLR | 3 | 0.81(0.36, 1.83) | 0.606 | 0.291 | 56.8 | |
Sequencing | 5 | 0.80(0.42, 1.50) | 0.479 | 0.262 | 51.8 | ||
TagMan | 5 | 0.87(0.66, 1.15) | 0.333 | 0.025 | 24.1 | ||
Other methods | 7 | 1.53(0.99, 2.36) | 0.054 | 0.243 | 72.6 | ||
NOS score | 5 ≤ Score ≤ 6 | 14 | 1.20(0.95, 1.50) | 0.120 | 0.105 | 33.8 | |
Score >6 | 6 | 0.77(0.41, 1.45) | 0.414 | <0.001 | 82.8 | ||
AA vs. GA+GG | Ethnicity | Asian | 7 | 0.018 | 0.300 | 17.1 | |
Caucasian | 6 | 0.51(0.23, 1.12) | 0.095 | 0.040 | 57.2 | ||
SOC | HB | 8 | 1.50(0.84, 2.67) | 0.172 | 0.021 | 57.5 | |
PB | 5 | 0.53(0.19, 1.48) | 0.226 | 0.673 | 0.0 | ||
HWE | Positive | 7 | 0.001 | 0.172 | 33.6 | ||
Negative | 6 | 0.031 | 0.457 | 0.0 | |||
Genotyping | PCR-RFLR | 1 | 0.58(0.02, 14.52) | 0.741 | N/A | N/A | |
Sequencing | 4 | 0.86(0.33, 2.25) | 0.761 | 0.835 | 0.0 | ||
TagMan | 4 | 0.61(0.26, 1.42) | 0.247 | 0.012 | 72.5 | ||
Other methods | 4 | 0.009 | 0.127 | 47.4 | |||
NOS score | 5 ≤ Score ≤ 6 | 8 | 1.50(0.84, 2.67) | 0.172 | 0.021 | 57.5 | |
Score>6 | 5 | 0.53(0.19, 1.48) | 0.226 | 0.673 | 0.0 | ||
AA vs. GG | Ethnicity | Asian | 7 | 0.018 | 0.291 | 18.3 | |
Caucasian | 6 | 0.50(0.23, 1.10) | 0.084 | 0.039 | 57.4 | ||
SOC | HB | 8 | 1.50(0.84, 2.67) | 0.171 | 0.021 | 57.6 | |
PB | 5 | 0.51(0.18, 1.43) | 0.199 | 0.641 | 0.0 | ||
HWE | Positive | 7 | 0.002 | 0.146 | 37.0 | ||
Negative | 6 | 0.029 | 0.446 | 0.0 | |||
Genotyping | PCR-RFLR | 1 | 0.53(0.02, 13.30) | 0.700 | N/A | N/A | |
Sequencing | 4 | 0.85(0.33, 2.21) | 0.740 | 0.794 | 0.0 | ||
TagMan | 4 | 0.60(0.26, 1.40) | 0.236 | 0.013 | 72.4 | ||
Other methods | 4 | 0.009 | 0.121 | 48.4 | |||
NOS score | 5 ≤ Score ≤ 6 | 8 | 1.50(0.84, 2.67) | 0.171 | 0.021 | 57.6 | |
Score>6 | 5 | 0.51(0.18, 1.43) | 0.199 | 0.641 | 0.0 | ||
GA vs. GG | Ethnicity | Asian | 12 | 0.032 | 0.032 | 48.0 | |
Caucasian | 8 | 0.76(0.57, 1.01) | 0.057 | 0.115 | 39.6 | ||
SOC | HB | 13 | 1.21(0.90, 1.63) | 0.210 | 0.011 | 53.7 | |
PB | 7 | 0.82(0.53, 1.26) | 0.358 | 0.009 | 64.9 | ||
HWE | Positive | 14 | 1.09(0.80, 1.50) | 0.585 | <0.001 | 67.6 | |
Negative | 6 | 0.87(0.66, 1.15) | 0.322 | 0.466 | 0.0 | ||
Genotyping | PCR-RFLR | 3 | 0.84(0.38, 1.82) | 0.651 | 0.651 | 52.6 | |
Sequencing | 5 | 0.80(0.39, 1.66) | 0.553 | 0.553 | 49.0 | ||
TagMan | 5 | 0.90(0.69, 1.16) | 0.411 | 0.411 | 7.6 | ||
Other methods | 7 | 1.43(0.91, 2.26) | 0.120 | 0.120 | 73.9 | ||
NOS score | 5 ≤ Score ≤ 6 | 14 | 1.16(0.91, 1.47) | 0.233 | 0.096 | 34.9 | |
Score>6 | 6 | 0.80(0.43, 1.48) | 0.473 | <0.001 | 80.8 |
Forest plot shows odds ratio for the association between TNF-α rs361525 polymorphism and GC risk (A vs. G).
Stratification analyses of ethnicity between TNF-α rs361525 polymorphism and GC risk (AA+GA vs. GG).
Stratification analyses of genotyping methods between TNF-α rs361525 polymorphism and GC risk (A vs. G).
The TNF-α mRNA expression levels by the genotypes of rs361525 polymorphism were significantly different for the whole blood (
The TNF-α mRNA expression levels by the genotypes of rs361525 polymorphism.
In the sensitivity analysis, no overall significant change was found when any single study was removed, suggesting our results are statistically robust. Neither Egger's nor Begg's tests (GA vs. GG, Figure
Begg's tests for publication bias between TNF-α rs361525 polymorphism and risk of GC (GA vs. GG).
The FPRPs for significant results at different
False-positive report probability values for associations between the TNF-α−238 polymorphism and gastric cancer risk.
Asian | 1.46(1.16, 1.85) | 0.002 | 0.605 | 0.247 | 0.768 | 0.971 | ||
Caucasian | 0.72(0.53, 0.99) | 0.043 | 0.670 | 0.366 | 0.864 | 0.985 | 0.998 | |
Other methods | 1.54(1.04, 2.30) | 0.033 | 0.565 | 0.344 | 0.852 | 0.983 | 0.998 | |
Asian | 1.46(1.11, 1.91) | 0.007 | 0.603 | 0.535 | 0.921 | 0.991 | ||
Caucasian | 0.73(0.54, 0.99) | 0.043 | 0.710 | 0.353 | 0.857 | 0.984 | 0.998 | |
Asian | 2.41(1.16, 4.98) | 0.018 | 0.543 | 0.230 | 0.766 | 0.971 | 0.997 | |
HWE-positive | 3.82(1.69, 8.61) | 0.001 | 0.497 | 0.166 | 0.668 | 0.953 | ||
HWE-negative | 0.45(0.21, 0.93) | 0.031 | 0.624 | 0.309 | 0.831 | 0.980 | 0.998 | |
Other methods | 3.43(1.37, 8.61) | 0.009 | 0.522 | 0.134 | 0.630 | 0.945 | 0.994 | |
Asian | 2.41(1.17, 4.98) | 0.018 | 0.543 | 0.230 | 0.766 | 0.971 | 0.997 | |
HWE-positive | 3.68(1.64, 8.28) | 0.002 | 0.529 | 0.272 | 0.791 | 0.974 | ||
HWE-negative | 0.44(0.21, 0.92) | 0.029 | 0.628 | 0.293 | 0.820 | 0.979 | 0.998 | |
Other methods | 3.40(1.36, 8.47) | 0.009 | 0.531 | 0.627 | 0.944 | 0.994 | ||
Asian | 1.40(1.03, 1.91) | 0.032 | 0.661 | 0.304 | 0.827 | 0.980 | 0.988 |
It is hypothesized chronic inflammation plays a crucial role in the etiology of GC and other cancers. Epplein et al. found the upregulated circulating levels of inflammation-related cytokines such as TNF-α may intensify the risk of GC (Epplein et al.,
Many studies have reported the relationship between
However, this study also has some limitations. Firstly, no subgroup analyses of confounding factors such as age, sex, smoking or
In conclusion, this meta-analysis indicates the
ZK conceived the entire study. TX and HZ analyzed the data. TX and ZK performed statistical analysis. ZK and HZ wrote the paper. All authors read and agreed with the final version of this paper.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.