Abstract

Purpose: To provide a comprehensive account of the association of five Lymphotoxin-α (LTA) gene polymorphisms (rs1041981, rs2229094, rs2239704, rs746868, rs909253) with susceptibility to cancer.

Methods: A literature search for eligible candidate gene studies published before 28 February 2020 was conducted in the PubMed, Medline, Google Scholar and Web of Science. The following combinations of main keywords were used: (LTA OR Lymphotoxin alpha OR TNF-β OR tumor necrosis factor-beta) AND (polymorphism OR mutation OR variation OR SNP OR genotype) AND (cancer OR tumor OR neoplasm OR malignancy OR carcinoma OR adenocarcinoma). Potential sources of heterogeneity were sought out via subgroup and sensitivity analysis, and publication bias were estimated.

Results: Overall, a total of 24 articles with 24577 cases and 33351 controls for five polymorphisms of LTA gene were enrolled. We identified that rs2239704 was associated with a reduced risk of cancer. While for other polymorphisms, the results showed no significant association with cancer risk. In the stratified analysis of rs1041981, we found that Asians might have less susceptibility to cancer. At the same time, we found that rs2239704 was negatively correlated with non-Hodgkin lymphoma (NHL). While, for rs909253, an increased risk of cancer for Caucasians and HCC susceptibility were uncovered in the stratified analysis of by ethnicity and cancer type.

Conclusion:LTA rs2239704 polymorphism is inversely associated with the risk of cancer. LTA rs1041981 polymorphism is negatively associated with cancer risk in Asia. While, LTA rs909253 polymorphism is a risk factor for HCC in Caucasian population.

Introduction

Increasing studies have demonstrated that a number of proinflammatory cytokines could be associated with the development of cancer [1,2]. Lymphotoxin-α (LTA) is the predominant member of the tumor necrosis factor (TNF) ligand family, which responds to immune and inflammatory reaction and plays an important role in the pathogenesis of cancer [3,4]. The human LTA gene is located on the short arm of chromosome 6 (6p21.3) [5]. The presence of single nucleic polymorphism (SNP) may affect cytokine expression level, which might be an important mediator of cancer [6,7]. SNP rs1041981 is a mutation of LTA gene at the 804 (C/A) position of exon 3 in codon 26, causing the amino acid threonine to be asparagine, which may be related to the transcriptional regulation of LTA, then activate the lymphocytes and induce apoptosis [8]. While, SNP rs909253 is a mutation of LTA gene at 252 (A/G) position in intron 1, which may lead to increase in the transcriptional activities of LTA [1]. In addition, SNP rs2239704, rs746868 and rs2229094 are associated with the LTA expression, which may affect subsequent inflammatory responses and immunomodulatory diseases, including cancers [9,10].

There are ample evidences that have demonstrated the association between LTA polymorphisms and cancer [11–34]. However, these results are inconsistent and even contradictory, which might be due to the heterogeneity within cancer types, ethnicities, source of control, Hardy–Weinberg equilibrium (HWE), small sample sizes and so on. Huang et al. reported a meta-analysis about this topic, and they found that the LTA rs1041981, rs2239704 and rs2229094 polymorphisms were associated with the increased risk of cancers [35]. However, based on the current studies, we found that more studies were negatively correlated with LTA polymorphisms and cancer [11,13–15,18–24,27,28,31,32]. Therefore, we conducted the current updated systematic review and meta-analysis to accurately determine the association between genetic variation of LTA gene and cancer susceptibility.

Materials and methods

Literature search

We conducted a systematic literature search on PubMed, Medline, Embase, Google Scholar and Web of Science to retrieve all eligible publications on the association between LTA polymorphisms and the risk of cancer (up to 28 February 2020) with the following keywords: (LTA OR Lymphotoxin alpha OR TNF-β OR tumor necrosis factor-beta) AND (polymorphism OR mutation OR variation OR SNP OR genotype) AND (cancer OR tumor OR neoplasm OR malignancy OR carcinoma OR adenocarcinoma). The language of enrolled studies was restricted to English. After carefully screening, five polymorphisms were left for further investigation.

Inclusion and exclusion criteria

Articles enrolled in our meta-analysis satisfied the following inclusion criteria: (1) case–control studies that evaluated the association between LTA polymorphisms and cancer risk; (2) publications focusing on population genetic polymorphisms; (3) articles with sufficient genotype data to assess odds ratios (ORs) and the corresponding 95% CIs; (4) blood sample only for SNP analysis; (5) the control subjects satisfied HWE. The major exclusion criteria were: (1) case-only studies, case reports or reviews; (2) studies without raw data for the LTA genotype.

Data extraction

Two investigators (Jingdong Li and Yaxuan Wang) independently extracted the data from each study. All the case–control studies satisfied the inclusion criteria and consensus for any controversy was achieved. The data from the eligible articles comprise the first author’s name, year of publication, ethnicity, source of control, cancer type and numbers of cases and controls in LTA genotypes. Ethnicity was categorized as ‘Asian’, ‘Caucasian’, and ‘Mixed’.

Statistical analysis

The risk between the LTA polymorphisms and cancer was evaluated using summary ORs and the corresponding 95% CIs in allelic (B vs. A), dominant (BA + BB vs. AA), and recessive (BB vs. BA + AA) models (A: wild allele; B: mutated allele). The Cochrane’s Q-statistic test was used to assess the heterogeneity between studies, and the inconsistency was quantified with the I2 statistic. The substantial heterogeneity was considered significant when I2 > 50% or PQ ≤ 0.1, then, a random-effects model was used; otherwise, the fixed-effects model was applied. Subgroup meta-analysis were performed by cancer type, ethnicity, genotyping, HWE and the source of control. We also conducted sensitivity analysis to assess stability of the results by omitting one study each time to exclude studies. HWE was estimated by the asymptotic test, and deviation was considered when P<0.05. The potential publication bias of the eligible studies was evaluated by Begg’s and Egger’s regression test quantitatively. Trial sequential analysis (TSA) was performed as described by Xie et al. [36]. The required information size was calculated after adopting a level of significance of 5% for type I error and of 30% for type II error. The data was analyzed using the Stata 14.0 software (version 14.0; State Corporation, College Station, Texas, U.S.A.). A two-tailed P<0.05 was considered statistically significant.

Results

Main characteristics of the enrolled studies

The study selection processes were presented in Figure 1. For polymorphisms of LTA gene (rs1041981, rs2229094, rs2239704, rs746868, rs909253), a total of 24 articles (including 43 case–control studies) with 24577 cases and 33351 controls met the inclusion criteria [11–34]. Sixteen of these studies were performed in Asians, 17 studies were performed in Caucasians, 10 studies in Africans and the others were in mixed ethnic groups (including at least one race). Controls of 30 studies were population-based controls and 13 studies were hospital-based controls. All studies were in compliance with HWE except for two studies [30,32]. Table 1 shows the characteristics of all the eligible studies and genotype frequency distributions of the five LTA polymorphisms included in our meta-analysis. Newcastle–Ottawa scale (NOS) was used to evaluate the quality of the enrolled studies, as shown in Table 2.

Flow chart of studies selection process for LTA gene polymorphisms

Figure 1
Flow chart of studies selection process for LTA gene polymorphisms
Figure 1
Flow chart of studies selection process for LTA gene polymorphisms
Table 1
Characteristics of the enrolled studies
SNPFirst authorYearEthnicitySource of controlCancer typeCaseControlHWE
AAABBBAAABBB
rs1041981 Abbas 2010 Caucasian PB OC 1498 1317 332 2481 2399 607 
 Castro 2009 Caucasian PB OC 154 456 341 337 813 557 
 Lee 2004 Asian PB GC 109 156 63 74 132 47 
 Niwa 2005 Asian HB OC 60 59 12 107 165 48 
 Niwa 2007 Asian HB OC 51 43 16 71 114 35 
 Sainz 2012 Caucasian PB OC 833 729 198 794 760 173 
rs2229094 Abbas 2010 Caucasian PB OC 1686 1199 251 2965 2153 359 
 Madeleine 2011 Mixed PB OC 444 329 75 475 334 57 
 Mahajan 2008 Caucasian PB GC 206 74 21 247 150 18 
 Wang 2009 Mixed PB NHL 1043 751 148 978 702 124 
rs2239704 Cerhan 2008 Mixed HB NHL 169 217 55 170 225 79 
 Ennas 2008 Caucasian PB OC 14 17 36 53 23 
 Gu 2014 Asian PB NHL 33 50 10 82 96 25 
 Gu 2014 Asian PB NHL 30 21 13 82 100 47 
 Lan 2006 Mixed PB NHL 165 189 63 186 226 87 
 Mahajan 2008 Caucasian PB GC 85 138 76 105 223 85 
 Purdue 2007 Caucasian HB NHL 202 240 64 162 229 72 
 Wang 2009 Mixed PB NHL 697 754 241 599 714 256 
rs746868 Crusius 2008 Caucasian PB GC 151 205 72 398 545 181 
 Garcia-Gonzalez 2007 Caucasian PB GC 135 194 75 142 191 71 
 Gunter 2006 Mixed HB OC 85 107 27 76 102 27 
 Mahajan 2008 Caucasian PB GC 83 143 74 108 220 84 
rs909253 Cerhan 2008 Mixed HB NHL 179 208 53 207 217 51 
 Cheng 2015 Asian HB NHL 45 71 95 149 56 
 Crusius 2008 Caucasian PB GC 168 218 38 533 472 121 
 Ennas 2008 Caucasian PB OC 29 10 85 24 
 Garcia-Gonzalez 2007 Caucasian PB GC 238 127 39 222 154 28 
 Gu 2014 Asian PB NHL 42 39 11 69 98 36 
 Gu 2014 Asian PB NHL 27 29 104 97 28 
 Gunter 2006 Mixed HB OC 90 101 35 88 92 29 
 Jeng 2014 Asian PB HCC 46 65 39 98 42 10 
 Lakhanpal 2016 Asian HB OC 14 59 47 39 24 37 
 Lan 2006 Mixed PB NHL 240 218 59 274 254 65 
 Lee 2004 Asian PB GC 112 152 64 77 131 46 
 Liu 2013 Asian PB NHL 111 151 29 95 149 56 
 Mahajan 2008 Caucasian PB GC 137 135 29 201 174 38 
 Mou 2015 Asian HB GC 105 75 14 57 48 28 
 Niwa 2005 Asian HB OC 60 59 12 107 165 48 
 Niwa 2007 Asian HB OC 51 43 16 71 114 35 
 Purdue 2007 Caucasian HB NHL 205 265 68 198 233 63 
 Tsai 2017 Asian PB HCC 45 66 39 98 42 10 
 Wang 2009 Mixed PB NHL 778 857 262 788 766 219 
 Yri 2013 Caucasian PB NHL 157 247 76 394 479 136 
SNPFirst authorYearEthnicitySource of controlCancer typeCaseControlHWE
AAABBBAAABBB
rs1041981 Abbas 2010 Caucasian PB OC 1498 1317 332 2481 2399 607 
 Castro 2009 Caucasian PB OC 154 456 341 337 813 557 
 Lee 2004 Asian PB GC 109 156 63 74 132 47 
 Niwa 2005 Asian HB OC 60 59 12 107 165 48 
 Niwa 2007 Asian HB OC 51 43 16 71 114 35 
 Sainz 2012 Caucasian PB OC 833 729 198 794 760 173 
rs2229094 Abbas 2010 Caucasian PB OC 1686 1199 251 2965 2153 359 
 Madeleine 2011 Mixed PB OC 444 329 75 475 334 57 
 Mahajan 2008 Caucasian PB GC 206 74 21 247 150 18 
 Wang 2009 Mixed PB NHL 1043 751 148 978 702 124 
rs2239704 Cerhan 2008 Mixed HB NHL 169 217 55 170 225 79 
 Ennas 2008 Caucasian PB OC 14 17 36 53 23 
 Gu 2014 Asian PB NHL 33 50 10 82 96 25 
 Gu 2014 Asian PB NHL 30 21 13 82 100 47 
 Lan 2006 Mixed PB NHL 165 189 63 186 226 87 
 Mahajan 2008 Caucasian PB GC 85 138 76 105 223 85 
 Purdue 2007 Caucasian HB NHL 202 240 64 162 229 72 
 Wang 2009 Mixed PB NHL 697 754 241 599 714 256 
rs746868 Crusius 2008 Caucasian PB GC 151 205 72 398 545 181 
 Garcia-Gonzalez 2007 Caucasian PB GC 135 194 75 142 191 71 
 Gunter 2006 Mixed HB OC 85 107 27 76 102 27 
 Mahajan 2008 Caucasian PB GC 83 143 74 108 220 84 
rs909253 Cerhan 2008 Mixed HB NHL 179 208 53 207 217 51 
 Cheng 2015 Asian HB NHL 45 71 95 149 56 
 Crusius 2008 Caucasian PB GC 168 218 38 533 472 121 
 Ennas 2008 Caucasian PB OC 29 10 85 24 
 Garcia-Gonzalez 2007 Caucasian PB GC 238 127 39 222 154 28 
 Gu 2014 Asian PB NHL 42 39 11 69 98 36 
 Gu 2014 Asian PB NHL 27 29 104 97 28 
 Gunter 2006 Mixed HB OC 90 101 35 88 92 29 
 Jeng 2014 Asian PB HCC 46 65 39 98 42 10 
 Lakhanpal 2016 Asian HB OC 14 59 47 39 24 37 
 Lan 2006 Mixed PB NHL 240 218 59 274 254 65 
 Lee 2004 Asian PB GC 112 152 64 77 131 46 
 Liu 2013 Asian PB NHL 111 151 29 95 149 56 
 Mahajan 2008 Caucasian PB GC 137 135 29 201 174 38 
 Mou 2015 Asian HB GC 105 75 14 57 48 28 
 Niwa 2005 Asian HB OC 60 59 12 107 165 48 
 Niwa 2007 Asian HB OC 51 43 16 71 114 35 
 Purdue 2007 Caucasian HB NHL 205 265 68 198 233 63 
 Tsai 2017 Asian PB HCC 45 66 39 98 42 10 
 Wang 2009 Mixed PB NHL 778 857 262 788 766 219 
 Yri 2013 Caucasian PB NHL 157 247 76 394 479 136 

Abbreviations: GC, gastric cancer; HB, hospital-based; HCC, hepatocellular carcinoma; N, no; NHL, non-Hodgkin lymphoma; OC, other cancer; PB, population-based; Y, yes.

Table 2
Methodological quality of the enrolled studies according to the NOS
SNPFirst authorAdequacy definitionRepresentativeness of the casesControl selectionControl definitionComparability cases/ controlsExposure ascertainmentSame method ascertainmentNon-response rate
rs1041981 Abbas et al. ** 
 Castro et al. ** ** 
 Lee et al. ** 
 Niwa et al. NA ** 
 Niwa et al. NA ** 
 Sainz et al. ** 
rs2229094 Abbas et al. ** 
 Madeleine et al. ** 
 Mahajan et al. ** 
 Wang et al. ** 
rs2239704 Cerhan et al. NA ** 
 Ennas et al. ** 
 Gu et al. NA ** 
 Gu et al. NA ** 
 Lan et al. ** 
 Mahajan et al. ** 
 Purdue et al. NA ** 
 Wang et al. ** 
rs746868 Crusius et al. NA ** 
 Garcia-Gonzalez et al. ** 
 Gunter et al. NA ** ** 
 Mahajan et al. ** 
rs909253 Cerhan et al. NA ** 
 Cheng et al. NA ** ** 
 Crusius et al. NA ** 
 Ennas et al. ** 
 Garcia-Gonzalez et al. ** 
 Gu et al. NA ** 
 Gu et al. NA ** 
 Gunter et al. NA ** ** 
 Jeng et al. ** 
 Lakhanpal et al. NA ** 
 Lan et al. ** 
 Lee et al. ** 
 Liu et al. ** 
 Mahajan et al. ** 
 Mou et al. NA ** 
 Niwa et al. NA ** 
 Niwa et al. NA ** 
 Purdue et al. NA ** 
 Tsai et al. ** 
 Wang et al. ** 
 Yri et al. NA ** 
SNPFirst authorAdequacy definitionRepresentativeness of the casesControl selectionControl definitionComparability cases/ controlsExposure ascertainmentSame method ascertainmentNon-response rate
rs1041981 Abbas et al. ** 
 Castro et al. ** ** 
 Lee et al. ** 
 Niwa et al. NA ** 
 Niwa et al. NA ** 
 Sainz et al. ** 
rs2229094 Abbas et al. ** 
 Madeleine et al. ** 
 Mahajan et al. ** 
 Wang et al. ** 
rs2239704 Cerhan et al. NA ** 
 Ennas et al. ** 
 Gu et al. NA ** 
 Gu et al. NA ** 
 Lan et al. ** 
 Mahajan et al. ** 
 Purdue et al. NA ** 
 Wang et al. ** 
rs746868 Crusius et al. NA ** 
 Garcia-Gonzalez et al. ** 
 Gunter et al. NA ** ** 
 Mahajan et al. ** 
rs909253 Cerhan et al. NA ** 
 Cheng et al. NA ** ** 
 Crusius et al. NA ** 
 Ennas et al. ** 
 Garcia-Gonzalez et al. ** 
 Gu et al. NA ** 
 Gu et al. NA ** 
 Gunter et al. NA ** ** 
 Jeng et al. ** 
 Lakhanpal et al. NA ** 
 Lan et al. ** 
 Lee et al. ** 
 Liu et al. ** 
 Mahajan et al. ** 
 Mou et al. NA ** 
 Niwa et al. NA ** 
 Niwa et al. NA ** 
 Purdue et al. NA ** 
 Tsai et al. ** 
 Wang et al. ** 
 Yri et al. NA ** 

A study can be awarded a maximum of one star (*) for each numbered item within the Selection and Exposure categories. A maximum of two stars (**) can be given for Comparability. Abbreviations: NA, not applicable.

Quantitative synthesis

rs1041981

The pooled results based on six included studies [11–16] (including 6427 cases and 9714 controls) indicated that no significant association between rs1041981 polymorphism and cancer risk was found. However, in the stratification analysis by ethnicity, we observed that Asian group was significantly related to a reduced risk of cancer in allelic contrast (B vs A: OR = 0.79, 95% confidence interval (CI) = 0.64–0.97, P=0.027, Figure 2) and dominant model (BB+AB vs AA: OR = 0.67, 95% CI = 0.52–0.87, P=0.002). Moreover, when the subgroup analysis was performed based on source of controls, hospital-based control group was significantly related to a decreased risk of cancer in allelic contrast (B vs A: OR = 0.69, 95% CI = 0.55–0.87, P=0.002) and dominant model (BB+AB vs AA: OR = 0.58, 95% CI = 0.42–0.78, P=0.000) (Table 3).

Forest plot of LTA rs1041981 polymorphism and cancer risk in allelic contrast stratified by ethnicity

Figure 2
Forest plot of LTA rs1041981 polymorphism and cancer risk in allelic contrast stratified by ethnicity
Figure 2
Forest plot of LTA rs1041981 polymorphism and cancer risk in allelic contrast stratified by ethnicity
Table 3
Meta-analysis of rs1041981
VariablesnAllelic contrastDominant modelRecessive model
P, OR (99% CI)P (Q test), I2P, OR (99% CI)P (Q test), I2P, OR (99% CI)P (Q test), I2
Total 0.307, 0.94 (0.84, 1.06) 0.002, 73.1% 0.163, 0.89 (0.75, 1.05) 0.002, 73.1% 0.607, 1.03 (0.91, 1.17) 0.214, 29.4% 
Ethnicity 
Asian 0.027, 0.79 (0.64, 0.97) 0.192, 39.4% 0.002, 0.67 (0.52, 0.87) 0.303, 16.2% 0.433, 0.87 (0.63, 1.22) 0.327, 10.5% 
Caucasian 0.782, 1.02 (0.91, 1.14) 0.009, 78.7% 0.951, 1.01 (0.85, 1.18) 0.014, 76.4% 0.380, 1.06 (0.93, 1.22) 0.152, 47.0% 
Source of control 
PB 0.926, 1.00 (0.91, 1.11) 0.022, 68.7% 0.788, 0.98 (0.85, 1.13) 0.029, 66.8% 0.329, 1.06 (0.95, 1.18) 0.287, 20.5% 
HB 0.002, 0.69 (0.55, 0.87) 0.774, 0.0% 0.000, 0.58 (0.42, 0.78) 0.813, 0.0% 0.171, 0.72 (0.46, 1.15) 0.336, 0.0% 
VariablesnAllelic contrastDominant modelRecessive model
P, OR (99% CI)P (Q test), I2P, OR (99% CI)P (Q test), I2P, OR (99% CI)P (Q test), I2
Total 0.307, 0.94 (0.84, 1.06) 0.002, 73.1% 0.163, 0.89 (0.75, 1.05) 0.002, 73.1% 0.607, 1.03 (0.91, 1.17) 0.214, 29.4% 
Ethnicity 
Asian 0.027, 0.79 (0.64, 0.97) 0.192, 39.4% 0.002, 0.67 (0.52, 0.87) 0.303, 16.2% 0.433, 0.87 (0.63, 1.22) 0.327, 10.5% 
Caucasian 0.782, 1.02 (0.91, 1.14) 0.009, 78.7% 0.951, 1.01 (0.85, 1.18) 0.014, 76.4% 0.380, 1.06 (0.93, 1.22) 0.152, 47.0% 
Source of control 
PB 0.926, 1.00 (0.91, 1.11) 0.022, 68.7% 0.788, 0.98 (0.85, 1.13) 0.029, 66.8% 0.329, 1.06 (0.95, 1.18) 0.287, 20.5% 
HB 0.002, 0.69 (0.55, 0.87) 0.774, 0.0% 0.000, 0.58 (0.42, 0.78) 0.813, 0.0% 0.171, 0.72 (0.46, 1.15) 0.336, 0.0% 

Abbreviations: HB, hospital-based; n, number; PB, population-based.

rs2229094

The pooled results based on four included studies [11,17–19] (including 6227 cases and 8562 controls) indicated that no significant association between rs2229094 polymorphism and cancer risk was found. Further subgroup analysis by ethnicity also indicated that no significant result was uncovered (Supplementary Table S1).

rs2239704

The pooled results based on eight included studies [18–24] (including 3550 cases and 3962 controls) suggested that rs2239704 reduced the risk of cancer in allelic contrast (B vs A: OR = 0.90, 95% CI = 0.85–0.97, P=0.003), dominant model (BB+AB vs AA: OR = 0.88, 95% CI = 0.80–0.96, P=0.006) and recessive model (BB vs AA+AB: OR = 0.88, 95% CI = 0.77–0.99, P=0.040). Furthermore, in the stratification analysis by cancer type, we observed that rs2239704 reduced the risk of NHL in allelic contrast (B vs A: OR = 0.89, 95% CI = 0.83–0.96, P=0.001, Figure 3), dominant model (BB+AB vs AA: OR = 0.88, 95% CI = 0.80–0.97, P=0.011) and recessive model (BB vs AA+AB: OR = 0.83, 95% CI = 0.72–0.95, P=0.006). Moreover, when the subgroup analysis was performed based on ethnicity, source of control and genotyping, we found mixed ethnicity was significantly related to a reduced risk of cancer in allelic contrast (B vs A: OR = 0.89, 95% CI = 0.83–0.97, P=0.006), dominant model (BB+AB vs AA: OR = 0.89, 95% CI = 0.79–1.00, P=0.042) and recessive model (BB vs AA+AB: OR = 0.82, 95% CI = 0.71–0.96, P=0.013) (Table 4).

Forest plot of LTA rs2239704 polymorphism and cancer risk in allelic contrast stratified by cancer type
Figure 3
Forest plot of LTA rs2239704 polymorphism and cancer risk in allelic contrast stratified by cancer type

Abbreviations: GC, gastric cancer; OC, other cancer.

Figure 3
Forest plot of LTA rs2239704 polymorphism and cancer risk in allelic contrast stratified by cancer type

Abbreviations: GC, gastric cancer; OC, other cancer.

Table 4
Meta-analysis of rs2239704
VariablesnAllele contrastDominant modelRecessive model
P, OR (99% CI)P (Q test), I2P, OR(99% CI)P (Q test), I2P, OR(99% CI)P (Q test), I2
Total 0.003, 0.90 (0.85, 0.97) 0.819, 0.0% 0.006, 0.88 (0.80, 0.96) 0.831, 0.0% 0.040, 0.88 (0.77, 0.99) 0.444, 0.0% 
Cancer type 
NHL 0.001, 0.89 (0.83, 0.96) 0.876, 0.0% 0.011, 0.88 (0.80, 0.97) 0.626, 0.0% 0.006, 0.83 (0.72, 0.95) 0.958, 0.0% 
GC 0.733, 1.04 (0.84, 1.28) NA 0.371, 0.86 (0.61, 1.20) NA 0.128, 1.32 (0.92, 1.87) NA 
OC 0.605, 0.87 (0.51, 1.47) NA 0.596, 0.81 (0.38, 1.75) NA 0.778, 0.87 (0.34, 2.24) NA 
Ethnicity 
Asian 0.633, 0.94 (0.72, 1.22) 0.264, 19.8% 0.645, 0.92 (0.63, 1.33) 0.084, 66.5% 0.775, 0.93 (0.55, 1.55) 0.791, 0.0% 
Caucasian 0.227, 0.92 (0.81, 1.05) 0.356, 3.3% 0.061, 0.83 (0.68, 1.01) 0.964, 0.0% 0.924, 1.01 (0.79, 1.29) 0.131, 50.8% 
Mixed 0.006, 0.89 (0.83, 0.97) 0.944, 0.0% 0.042, 0.89 (0.79, 1.00) 0.978, 0.0% 0.013, 0.82 (0.71, 0.96) 0.698, 0.0% 
Source of control 
PB 0.033, 0.92 (0.85, 0.99) 0.728, 0.0% 0.029, 0.88 (0.79, 0.99) 0.681, 0.0% 0.258, 0.92 (0.80, 1.06) 0.431, 0.0% 
HB 0.022, 0.86 (0.75, 0.98) 0.849, 0.0% 0.095, 0.85 (0.71, 1.03) 0.581, 0.0% 0.029, 0.75 (0.58, 0.97) 0.710, 0.0% 
Genotyping 
PCR 0.023, 0.91 (0.85, 0.99) 0.546, 0.0% 0.032, 0.89 (0.79, 0.99) 0.548, 0.0% 0.153, 0.90 (0.78, 1.04) 0.174, 37.0% 
TaqMan 0.041, 0.88 (0.77, 0.99) 0.877, 0.0% 0.087, 0.85 (0.71, 1.02) 0.830, 0.0% 0.110, 0.82 (0.64, 1.05) 0.955, 0.0% 
VariablesnAllele contrastDominant modelRecessive model
P, OR (99% CI)P (Q test), I2P, OR(99% CI)P (Q test), I2P, OR(99% CI)P (Q test), I2
Total 0.003, 0.90 (0.85, 0.97) 0.819, 0.0% 0.006, 0.88 (0.80, 0.96) 0.831, 0.0% 0.040, 0.88 (0.77, 0.99) 0.444, 0.0% 
Cancer type 
NHL 0.001, 0.89 (0.83, 0.96) 0.876, 0.0% 0.011, 0.88 (0.80, 0.97) 0.626, 0.0% 0.006, 0.83 (0.72, 0.95) 0.958, 0.0% 
GC 0.733, 1.04 (0.84, 1.28) NA 0.371, 0.86 (0.61, 1.20) NA 0.128, 1.32 (0.92, 1.87) NA 
OC 0.605, 0.87 (0.51, 1.47) NA 0.596, 0.81 (0.38, 1.75) NA 0.778, 0.87 (0.34, 2.24) NA 
Ethnicity 
Asian 0.633, 0.94 (0.72, 1.22) 0.264, 19.8% 0.645, 0.92 (0.63, 1.33) 0.084, 66.5% 0.775, 0.93 (0.55, 1.55) 0.791, 0.0% 
Caucasian 0.227, 0.92 (0.81, 1.05) 0.356, 3.3% 0.061, 0.83 (0.68, 1.01) 0.964, 0.0% 0.924, 1.01 (0.79, 1.29) 0.131, 50.8% 
Mixed 0.006, 0.89 (0.83, 0.97) 0.944, 0.0% 0.042, 0.89 (0.79, 1.00) 0.978, 0.0% 0.013, 0.82 (0.71, 0.96) 0.698, 0.0% 
Source of control 
PB 0.033, 0.92 (0.85, 0.99) 0.728, 0.0% 0.029, 0.88 (0.79, 0.99) 0.681, 0.0% 0.258, 0.92 (0.80, 1.06) 0.431, 0.0% 
HB 0.022, 0.86 (0.75, 0.98) 0.849, 0.0% 0.095, 0.85 (0.71, 1.03) 0.581, 0.0% 0.029, 0.75 (0.58, 0.97) 0.710, 0.0% 
Genotyping 
PCR 0.023, 0.91 (0.85, 0.99) 0.546, 0.0% 0.032, 0.89 (0.79, 0.99) 0.548, 0.0% 0.153, 0.90 (0.78, 1.04) 0.174, 37.0% 
TaqMan 0.041, 0.88 (0.77, 0.99) 0.877, 0.0% 0.087, 0.85 (0.71, 1.02) 0.830, 0.0% 0.110, 0.82 (0.64, 1.05) 0.955, 0.0% 

Abbreviations: GC, gastric cancer; HB, hospital-based; n, number; NA, not applicable; OC, other cancer; PB, population-based; PCR, polymerase chain reaction.

rs746868

The pooled results based on four included studies [18,25–27] (including 1351 cases and 2145 controls) indicated that no significant association between rs746868 polymorphism and risk of cancer was uncovered. Moreover, in the subgroup analysis by cancer type, ethnicity and source of control, similar results were found. (Supplementary Table S2).

rs909253

The pooled results based on 21 included studies [13–15,18–34] (including 7022 cases and 8968 controls) indicated that no significant association between rs909253 polymorphism and cancer risk was found. However, in the stratification analysis by cancer type, we observed that rs909253 polymorphism was significantly related to an increased risk of HCC in allelic contrast (B vs A: OR = 3.52, 95% CI = 2.73–4.54, P=0.000, Figure 4), dominant model (BB+AB vs AA: OR = 4.33, 95% CI = 3.07–6.09, P=0.000) and recessive model (BB vs AA+AB: OR = 4.92, 95% CI = 2.92–8.29, P=0.000). In addition, in the stratification analysis by ethnicity, we observed that rs909253 polymorphism was significantly related to an increased risk of Caucasian ethnicity in allelic contrast (B vs A: OR = 1.10, 95% CI = 1.02–1.20, P=0.019), and mixed ethnicity in allelic contrast (B vs A: OR = 1.09, 95% CI = 1.01–1.17, P=0.024), dominant model (BB+AB vs AA: OR = 1.11, 95% CI = 1.01–1.23, P=0.039). Moreover, in the stratification analysis by source of control, genotyping and HWE, null result was found (Table 5).

Forest plot of LTA rs909253 polymorphism and cancer risk in allelic contrast stratified by cancer type
Figure 4
Forest plot of LTA rs909253 polymorphism and cancer risk in allelic contrast stratified by cancer type

Abbreviations: GC, gastric cancer; HCC, hepatocellular carcinoma; OC, other cancer.

Figure 4
Forest plot of LTA rs909253 polymorphism and cancer risk in allelic contrast stratified by cancer type

Abbreviations: GC, gastric cancer; HCC, hepatocellular carcinoma; OC, other cancer.

Table 5
Meta-analysis of rs909253
VariablesnAllele contrastDominant modelRecessive model
P, OR (99% CI)P (Q test), I2P, OR(99% CI)P (Q test), I2P, OR(99% CI)P (Q test), I2
Total 21 0.349, 1.07 (0.93, 1.23) 0.000, 86.5% 0.198, 1.12 (0.94, 1.34) 0.000, 84.2% 0.993, 1.00 (0.81, 1.23) 0.000, 73.9% 
Cancer type 
NHL 0.717, 0.98 (0.87, 1.10) 0.001, 68.2% 0.599, 1.04 (0.91, 1.18) 0.056, 47.3% 0.360, 0.90 (0.71, 1.13) 0.004, 64.2% 
GC 0.567, 0.94 (0.78, 1.15) 0.006, 72.6% 0.775, 0.96 (0.74, 1.25) 0.007, 71.6% 0.511, 0.87 (0.57, 1.32) 0.005, 73.4% 
HCC 0.000, 3.52 (2.73, 4.54) 0.959, 0.0% 0.000, 4.33 (3.07, 6.09) 0.928, 0.0% 0.000, 4.92 (2.92, 8.29) 1.000, 0.0% 
OC 0.968, 0.99 (0.69, 1.43) 0.001, 79.8% 0.759, 1.11 (0.58, 2.13) 0.000, 87.7% 0.638, 0.93 (0.70, 1.25) 0.555, 0.0% 
Ethnicity 
Asian 11 0.707, 1.07 (0.75, 1.54) 0.000, 92.9% 0.507, 1.17 (0.74, 1.86) 0.000, 91.3% 0.753, 0.92 (0.56, 1.52) 0.000, 85.4% 
Caucasian 0.019, 1.10 (1.02, 1.20) 0.697, 0.0% 0.064, 1.15 (0.99, 1.34) 0.141, 39.6% 0.470, 1.07 (0.90, 1.27) 0.534, 0.0% 
Mixed 0.024, 1.09 (1.01, 1.17) 0.860, 0.0% 0.039, 1.11 (1.01, 1.23) 0.758, 0.0% 0.136, 1.12 (0.96, 1.31) 0.984, 0.0% 
Source of control 
PB 13 0.066, 1.18 (0.99, 1.42) 0.000, 88.4% 0.069, 1.23 (0.98, 1.53) 0.000, 85.4% 0.225, 1.18 (0.90, 1.53) 0.000, 75.7% 
HB 0.370, 0.91 (0.73, 1.12) 0.000, 80.3% 0.858, 0.97 (0.71, 1.34) 0.000, 81.8% 0.119, 0.76 (0.54, 1.07) 0.003, 67.3% 
Genotyping 
PCR 18 0.377, 1.08 (0.91, 1.27) 0.000, 88.5% 0.223, 1.14 (0.92, 1.40) 0.000, 86.5% 0.996, 1.00 (0.78, 1.27) 0.000, 77.7% 
TaqMan 0.706, 1.02 (0.90, 1.16) 0.957, 0.0% 0.649, 1.04 (0.88, 1.23) 0.846, 0.0% 0.929, 1.01 (0.78, 1.31) 0.927, 0.0% 
HWE 
19 0.304, 1.08 (0.94, 1.24) 0.000, 85.8% 0.292, 1.10 (0.92, 1.30) 0.000, 82.5% 0.643, 1.05 (0.85, 1.29) 0.000, 71.5% 
0.985, 1.01 (0.32, 3.21) 0.000, 95.2% 0.592, 1.72 (0.24, 12.6) 0.000, 95.8% 0.403, 0.58 (0.16, 2.10) 0.003, 88.6% 
VariablesnAllele contrastDominant modelRecessive model
P, OR (99% CI)P (Q test), I2P, OR(99% CI)P (Q test), I2P, OR(99% CI)P (Q test), I2
Total 21 0.349, 1.07 (0.93, 1.23) 0.000, 86.5% 0.198, 1.12 (0.94, 1.34) 0.000, 84.2% 0.993, 1.00 (0.81, 1.23) 0.000, 73.9% 
Cancer type 
NHL 0.717, 0.98 (0.87, 1.10) 0.001, 68.2% 0.599, 1.04 (0.91, 1.18) 0.056, 47.3% 0.360, 0.90 (0.71, 1.13) 0.004, 64.2% 
GC 0.567, 0.94 (0.78, 1.15) 0.006, 72.6% 0.775, 0.96 (0.74, 1.25) 0.007, 71.6% 0.511, 0.87 (0.57, 1.32) 0.005, 73.4% 
HCC 0.000, 3.52 (2.73, 4.54) 0.959, 0.0% 0.000, 4.33 (3.07, 6.09) 0.928, 0.0% 0.000, 4.92 (2.92, 8.29) 1.000, 0.0% 
OC 0.968, 0.99 (0.69, 1.43) 0.001, 79.8% 0.759, 1.11 (0.58, 2.13) 0.000, 87.7% 0.638, 0.93 (0.70, 1.25) 0.555, 0.0% 
Ethnicity 
Asian 11 0.707, 1.07 (0.75, 1.54) 0.000, 92.9% 0.507, 1.17 (0.74, 1.86) 0.000, 91.3% 0.753, 0.92 (0.56, 1.52) 0.000, 85.4% 
Caucasian 0.019, 1.10 (1.02, 1.20) 0.697, 0.0% 0.064, 1.15 (0.99, 1.34) 0.141, 39.6% 0.470, 1.07 (0.90, 1.27) 0.534, 0.0% 
Mixed 0.024, 1.09 (1.01, 1.17) 0.860, 0.0% 0.039, 1.11 (1.01, 1.23) 0.758, 0.0% 0.136, 1.12 (0.96, 1.31) 0.984, 0.0% 
Source of control 
PB 13 0.066, 1.18 (0.99, 1.42) 0.000, 88.4% 0.069, 1.23 (0.98, 1.53) 0.000, 85.4% 0.225, 1.18 (0.90, 1.53) 0.000, 75.7% 
HB 0.370, 0.91 (0.73, 1.12) 0.000, 80.3% 0.858, 0.97 (0.71, 1.34) 0.000, 81.8% 0.119, 0.76 (0.54, 1.07) 0.003, 67.3% 
Genotyping 
PCR 18 0.377, 1.08 (0.91, 1.27) 0.000, 88.5% 0.223, 1.14 (0.92, 1.40) 0.000, 86.5% 0.996, 1.00 (0.78, 1.27) 0.000, 77.7% 
TaqMan 0.706, 1.02 (0.90, 1.16) 0.957, 0.0% 0.649, 1.04 (0.88, 1.23) 0.846, 0.0% 0.929, 1.01 (0.78, 1.31) 0.927, 0.0% 
HWE 
19 0.304, 1.08 (0.94, 1.24) 0.000, 85.8% 0.292, 1.10 (0.92, 1.30) 0.000, 82.5% 0.643, 1.05 (0.85, 1.29) 0.000, 71.5% 
0.985, 1.01 (0.32, 3.21) 0.000, 95.2% 0.592, 1.72 (0.24, 12.6) 0.000, 95.8% 0.403, 0.58 (0.16, 2.10) 0.003, 88.6% 

Abbreviations: GC, gastric cancer; HB, hospital-based; HCC, hepatocellular carcinoma; n, number; N, no; OC, other cancer; PB, population-based; PCR, polymerase chain reaction; Y, yes.

Sensitivity analysis and publication bias

Sensitivity analysis were performed to evaluate the influence of each separate case–control study. The results showed that there was no material alteration in corresponding pooled ORs for rs1041981, rs2229094, rs2239704, rs746868, rs909253 (Supplementary Figures S1–S5). In addition, Begg’s test and Egger’s regression test were performed to evaluate the publication bias. As for rs1041981, rs2229094, rs2239704, rs746868 and rs909253, no evidence of publication bias was identified (Supplementary Table S3).

TSA

To evaluate random errors, we performed TSA (Figure 5). This analysis showed that the cumulative z-curve did not cross the trial sequential monitoring boundary and the required information size, suggesting that more evidences are needed to verify the conclusions.

TSA for LTA rs909253 polymorphism under the allele contrast model

Figure 5
TSA for LTA rs909253 polymorphism under the allele contrast model
Figure 5
TSA for LTA rs909253 polymorphism under the allele contrast model

Discussion

In the present study, a total of 24 articles including 43 case–control studies were enrolled to validate the association between five LTA gene polymorphisms (rs1041981, rs2229094, rs2239704, rs746868, rs909253) and the risk of cancer. We identified that rs2239704 was inversely associated with the risk of cancer under different genetic models. However, for LTA rs1041981, rs2229094, rs746868, rs909253 polymorphisms, no significant association with cancer risk was uncovered.

In subgroup meta-analysis stratified by cancer type, we found that rs2239704 was significantly reduced NHL susceptibility. Huang et al. reported rs2239704 polymorphism was correlated with cancer and positive association in North Americans [35]. However, they included studies that contained buccal samples for SNP analysis or insufficient data studies [37–39]. We strictly follow the inclusion and exclusion criteria to include the literature. And our results indicated that rs2239704 was significantly reduced cancer susceptibility in mixed ethnicity, hospital-based control and polymerase chain reaction (PCR) genotyping subgroups. Despite of several possible bias, we still could conclude that rs2239704 could reduce cancer susceptibility.

In the stratified analysis of rs1041981, we found that Asians might have less susceptibility to cancer. Unlike the study by Huang et al. [35], we excluded two studies, one of which was autopsy specimen for SNP analysis [10] and the other was a study of HIV-infected patients [40]. The literature thus incorporated has a better baseline consistency and is more reflective of the real situation. Our results were consistent with the results of Huang et al.[35]. Due to the small sample size, we were unable to evaluate the role of rs1041981 in Caucasians. Larger sample size studies are needed for further evaluation. However, based on the current studies, we might conclude that rs1041981 could reduce cancer susceptibility in Asians.

For LTA rs2229094 and rs746848, only four studies reported their relationship with cancer in each group. No significant results were found. Huang et al. reported positive association between rs2229094 and cancer risk [35], which could be the bias from report by Takei et al. [10]. Because of the small sample size, we could not draw any conclusions based on current literature.

Although the overall analysis of rs909253 indicated a null result for cancer risk, the risk of cancer for Caucasians and HCC susceptibility were significantly increased in the stratified analysis by ethnicity and cancer types. In addition, some of the control groups did not match HWE, we can not exclude the possibility that may cause the bias. Then, subgroup analysis by HWE showed that HWE status did not cause the bias of results. Huang et al. did not report the relationship of rs909253 and cancer risk, because it might be present in high linkage disequilibrium with other four SNPs [8,9]. However, our results identified that the function of rs909253 was opposite to rs2239704 and rs1041981. So, further studies with larger sample size are required to identify the role of LTA rs909253 and the linkage disequilibrium with other SNPs.

In the present study, we have put great effort on carefully searching for eligible studies. In order to obtain more accurate and reliable results, we conducted a comprehensive search to verify more eligible studies. Then, we used NOS to evaluate the quality of the included studies, eliminate low-quality studies and improve overall research quality. In order to provide the sources of heterogeneity, subgroup analysis was performed by ethnicity, cancer type, source of controls, genotyping and so on. In addition, sensitivity analysis was used to confirm the stability of the studies. Egger’s and Begg’s tests were used to assess publication bias. However, several limitations in our study should be noted. First, small sample size limits the reliability of the results for some polymorphisms. Second, we just included the studies published in English, which may influence the effects of the polymorphisms. Third, we mainly evaluated the relationship between LTA polymorphisms with various cancers, and we could not get enough data for some cancer types. Fourth, we did not assess the linkage disequilibrium, which might not reflect the real function correctly. In future, more well-designed case–control studies are needed to investigate the functions of LTA polymorphisms.

Conclusion

Our meta-analysis suggests that LTA rs2239704 polymorphism is inversely associated with the risk of cancer, as is LTA rs1041981 polymorphism in Asia. While, LTA rs909253 polymorphism is a risk factor for HCC in Caucasians. Further studies with larger sample size are needed to confirm these findings.

Competing Interests

The authors declare that there are no competing interests associated with the manuscript.

Funding

The authors declare that there are no sources of funding to be acknowledged.

Author Contribution

Zhenwei Han had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Study concept and design: Zhenwei Han and Jingdong Li. Acquisition of data: Jingdong Li and Yaxuan Wang. Analysis and interpretation of data: Jingdong Li and Yaxuan Wang. Drafting of the manuscript: Jingdong Li and Zhenwei Han. Critical revision of the manuscript for important intellectual content: Jingdong Li, Yaxuan Wang and Xueliang Chang. Statistical analysis: Yaxuan Wang and Xueliang Chang.

Acknowledgements

We thank Dr. Chawnshang Chang at University of Rochester Medical Center for helping with the preparation of the manuscript.

Abbreviations

     
  • CI

    confidence interval

  •  
  • HWE

    Hardy–Weinberg equilibrium

  •  
  • LTA

    lymphotoxin-α

  •  
  • NHL

    non-Hodgkin lymphoma

  •  
  • NOS

    Newcastle–Ottawa scale

  •  
  • OR

    odds ratio

  •  
  • SNP

    single nucleic polymorphism

  •  
  • TNF

    tumor necrosis factor

  •  
  • TSA

    trial sequential analysis

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Author notes

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These authors contributed equally to this work.

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