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. 2016 Dec 26;8(6):9806–9822. doi: 10.18632/oncotarget.14190

NFKB1-94insertion/deletion ATTG polymorphism and cancer risk: Evidence from 50 case-control studies

Wen Fu 1,#, Zhen-Jian Zhuo 2,#, Yung-Chang Chen 3,#, Jinhong Zhu 4, Zhang Zhao 1, Wei Jia 1, Jin-Hua Hu 1, Kai Fu 1, Shi-Bo Zhu 1, Jing He 1, Guo-Chang Liu 1
PMCID: PMC5354772  PMID: 28039461

Abstract

Nuclear factor-kappa B1 (NF-κB1) is a pleiotropic transcription factor and key contributor to tumorigenesis in many types of cancer. Numerous studies have addressed the association of a functional insertion (I)/deletion (D) polymorphism (-94ins/delATTG, rs28362491) in the promoter region of NFKB1 gene with the risk of various types of cancer; however, their conclusions have been inconsistent. We therefore conducted a meta-analysis to reevaluate this association. PubMed, EMBASE, China National Knowledge infrastructure (CNKI), and WANFANG databases were searched through July 2016 to retrieve relevant studies. After careful assessment, 50 case-control studies, comprising 18,299 cases and 23,484 controls were selected. Crude odds ratios (ORs) and 95% confidence intervals (CIs) were used to determine the strength of the association. The NFKB1 -94ins/delATTG polymorphism was associated with a decreased risk of overall cancer in the homozygote model (DD vs. II): OR = 0.75, 95% CI = 0.64-0.87); heterozygote model (ID vs. II): OR = 0.91, 95% CI = 0.83-0.99; recessive model (DD vs. ID/II): OR = 0.81, 95% CI = 0.71-0.91; dominant model (ID/DD vs. II): OR = 0.86, 95% CI = 0.78-0.95; and allele contrast model (D vs. I): OR = 0.88, 95% CI = 0.81-0.95). Subgroup and stratified analyses revealed decreased risks for lung cancer, nasopharyngeal carcinoma, prostate cancer, ovarian cancer, and oral squamous cell carcinoma, and this association held true also for Asians (especially Chinese subjects) in hospital-based studies, and in studies with quality scores less than nine. Well-designed, large-scale case-control studies are needed to confirm these results.

Keywords: NFKB1-94ins/delATTG, polymorphism, cancer risk, meta-analysis

INTRODUCTION

Cancer is a substantial public health burden, with an estimated 1.7 million new cancer cases and 0.6 million cancer-related deaths in the United States in 2016 [1]. Although the etiology of carcinogenesis has not yet been fully elucidated, many lines of evidence suggest that cancer is a multifactorial disease caused by intricate interactions between multiple hereditary and environmental factors [2, 3]. Numerous studies have demonstrated that inflammation is critically implicated in the development of some cancers [4, 5]. In view of this, it is plausible that genetic polymorphisms in inflammation-related genes could modify cancer susceptibility [6–8].

Nuclear factor-kappa B (NF-κB) is a pleiotropic transcription factor discovered by Sen and Baltimore in 1986 [9]. In mammals, the NF-κB family consists of five members: c-Rel (Rel), Rel B, p65 (RelA), p50/p105 (NF-κB1), and p52/p100 (NF-κB2) [10]. This group of molecules function as key regulators of a variety of genes implicated in diverse biological events including cell survival, apoptosis, inflammation, differentiation, and autophagy [11, 12]. Recently, high levels of NF-κB have been observed in many cancers, including pancreatic cancer [13], lung cancer [14], colorectal cancer [15], breast cancer [16], melanoma [17], and multiple myeloma [18]. Although various dimeric forms of NF-κB exist, the most common is the p50 and p65/RelA heterodimer, encoded by the NFKB1 and RelA genes, respectively [19].

The human NFKB1 gene, spanning 156-kb, is located on chromosome 4q23-q24 and encodes a 105 kD protein (p105) which is cleaved into an active subunit (p50) [20]. Several variations have been identified in the NFKB1 gene, including rs72696119 (C>G), rs28362491 (-94 ins/del ATTG), rs4648068 (A>G), and rs12509517 (G>C) [21]. Among these, NFKB1 rs28362491, namely the -94insertion/deletion ATTG polymorphism, is potentially functional and the most widely investigated [21]. This modification occurs between two important regulatory elements (activator protein 1 and κB binding site) in the promoter region of the NFKB1 gene. The deletion of four bases (ATTG) reduces or prevents the binding to nuclear proteins and leads to lower transcript levels of the NFKB1 gene, thereby changing mRNA stability and regulating translation efficiency [21, 22].

Numerous case-control studies have assessed the association between the NFKB1 -94ins/delATTG promoter polymorphism and cancer risk, with discrepant results. While some studies indicated an increased risk for some types of cancers [23–25], other studies showed instead a decreased risk, or no association [26, 27]. Several meta-analyses attempted to solve the controversy, but did not yield consistent results [28–33]. To provide a more precise evaluation of such association, we performed a comprehensive, updated meta-analysis. In addition, to minimize random errors and strengthen the robustness of our conclusions, we also performed trial sequential analysis (TSA).

RESULTS

Study characteristics

The study selection process for this meta-analysis is shown in Figure 1, 258 potentially relevant published records were retrieved from PubMed, EMBASE, China National Knowledge infrastructure (CNKI), and WANFANG databases. After screening the titles and reading the abstracts, 69 studies remained and were carefully reviewed. Among these, 23 publications were further excluded: 7 were case-only studies [34–39]; 6 were meta-analyses [28–33]; 5 deviated from Hardy-Weinberg equilibrium (HWE) [40–44]; 3 were duplicated publications [45–47]; 1 was a review [48], and 1 lacked sufficient data to calculate odds ratio (OR) and 95% confidence interval (CI) [49]. Thus, 46 publications were included in the final analysis [23–27, 48, 50–89]. Among these, publications that contained different case groups but used the same controls, or that studied one cancer type in different populations, were considered separate studies.

Figure 1. Flowchart of the study inclusion protocol.

Figure 1

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After this selection procedure, 50 studies extracted from 46 publications with 18,299 cases and 23,484 controls ultimately entered our final meta-analysis (Table 1). 38 of these studies included Asians subjects, and 12 included Caucasians. Regarding cancer types, 6 studies addressed hepatocellular carcinoma, 5 lung cancer, 4 colorectal cancer, 4 nasopharyngeal carcinoma, 4 prostate cancer, 4 ovarian cancer, 3 bladder cancer, 3 gastric cancer, 3 cervical cancer, 2 oral squamous cell carcinoma, 2 breast cancer, and 10 studies addressed other cancers. Moreover, 40 studies used a population-based design, and 10 were hospital-based. 19 studies had a quality score >9, and the remaining 31 had a quality score ≤9.

Table 1. Characteristics of the studies included in the current meta-analysis.

Surname Year Cancer type Country Ethnicity Control Source Genotype method Case Control MAF HWE Score
II ID DD All II ID DD All
Lin [25] 2006 OSCC China Asian HB PCR-PAGE 59 103 50 212 43 100 58 201 0.54 0.993 7
Riemann [26] 2006 Colorectal cancer Germany Caucasian HB Pyro sequencing 54 58 27 139 118 141 48 307 0.39 0.586 9
Riemann [26] 2006 CLL Germany Caucasian HB Pyro sequencing 18 41 13 72 118 141 48 307 0.39 0.586 9
Riemann [26] 2006 RCC Germany Caucasian HB Pyro sequencing 47 76 17 140 118 141 48 307 0.39 0.586 9
Bu [48] 2007 Melanoma Sweden Caucasian HB PCR-RFLP 67 84 34 185 116 255 67 438 0.44 0.000 10
Riemann [83] 2007 Bladder cancer Germany Caucasian HB Pyro sequencing 88 124 30 242 118 141 48 307 0.39 0.586 10
Lehnerdt [27] 2008 HNSCC Germany Caucasian HB Pyro sequencing 132 179 53 364 118 141 48 307 0.39 0.586 8
He [82] 2009 HCC China Asian HB PCR-RFLP 83 84 35 202 97 183 124 404 0.53 0.070 9
He [89] 2009 HCC China Asian HB PCR-RFLP 55 65 30 150 70 136 94 300 0.54 0.130 8
Lo [24] 2009 Gastric cancer China Asian HB PCR 62 89 31 182 20 62 34 116 0.56 0.361 7
Zhang [81] 2009 Prostate cancer China Asian HB PCR-PAGE 46 57 14 117 44 68 31 143 0.45 0.624 8
Zhou [80] 2009 NPC China Asian HB PCR-PAGE 74 67 22 163 71 90 42 203 0.43 0.177 7
Tang [79] 2010 Bladder cancer China Asian HB PCR–PAGE 89 92 26 207 74 108 46 228 0.44 0.565 10
Zhou [78] 2010 Cervical cancer China Asian HB PCR–PAGE 108 105 20 233 135 166 64 365 0.40 0.297 9
Fan [77] 2011 Ovarian cancer China Asian HB PCR-CE 78 84 17 179 76 103 44 223 0.43 0.396 8
Lin [76] 2012 OSCC China Asian HB TaqMan 116 246 100 462 81 271 168 520 0.58 0.099 9
Song [92] 2012 Colorectal cancer China Asian HB PCR-RFLP 363 500 138 1001 297 522 186 1005 0.44 0.102 14
Tang [87] 2012 HCC China Asian HB PCR-RFLP 52 84 14 150 57 82 11 150 0.35 0.011 7
Ungerback [75] 2012 Colorectal cancer Sweden Caucasian HB TaqMan 114 187 43 344 256 270 96 622 0.37 0.079 8
Vangsted [74] 2012 Multiple myeloma Denmark Caucasian PB TaqMan 110 163 55 328 655 778 253 1686 0.38 0.303 7
Arisawa [73] 2013 Gastric cancer Japan Asian PB PCR-SSCP 172 239 68 479 342 435 103 880 0.36 0.046 11
Cheng [23] 2013 HCC China Asian HB TaqMan 42 64 29 135 81 271 168 520 0.58 0.099 7
Huang [72] 2013 Lung cancer China Asian PB TaqMan 372 459 225 1056 355 491 210 1056 0.43 0.090 10
Huang [72] 2013 Lung cancer China Asian PB TaqMan 169 230 104 503 189 289 145 623 0.46 0.092 10
Huo [71] 2013 Ovarian cancer China Asian HB Mass ARRAY 83 82 22 187 71 103 47 221 0.45 0.399 7
Kopp [70] 2013 Prostate cancer Denmark Caucasian PB RT-PCR 128 152 54 334 109 161 64 334 0.43 0.741 11
Li [69] 2013 Bladder cancer China Asian HB TaqMan 189 269 151 609 223 324 93 640 0.40 0.156 11
Liu [86] 2013 NPC China Asian PB TaqMan 116 135 49 300 86 143 71 300 0.48 0.443 12
Song [84] 2013 EC China Asian HB PCR-RFLP 42 52 6 100 56 39 5 100 0.25 0.588 6
Song [85] 2013 Cervical cancer China Asian HB PCR-RFLP 34 56 10 100 37 55 8 100 0.36 0.044 5
Umar [68] 2013 ESCC India Asian HB PCR 131 132 27 290 160 129 22 311 0.28 0.561 10
Gao [67] 2014 HCC China Asian PB TaqMan 68 102 40 210 171 160 79 410 0.39 0.000 12
Hua [66] 2014 Gastric cancer China Asian HB Mass ARRAY 92 182 127 401 120 230 83 433 0.46 0.144 9
Oltulu [65] 2014 Lung cancer Turkey Caucasian HB PCR-RFLP 35 44 16 95 46 47 6 99 0.30 0.194 7
Wang [64] 2014 Breast cancer China Asian HB PCR-RFLP 93 210 171 474 162 216 123 501 0.46 0.003 9
Zhang [63] 2014 HCC China Asian PB PCR 205 312 107 624 542 790 274 1606 0.42 0.631 10
Chen [62] 2015 Ovarian cancer China Asian HB Mass ARRAY 120 195 95 410 85 235 122 442 0.54 0.136 9
Cui [61] 2015 Prostate cancer China Asian HB PCR-RFLP 198 246 99 543 212 355 186 753 0.48 0.125 10
Han [60] 2015 Prostate cancer China Asian PB PCR-RFLP 63 339 534 936 38 331 567 936 0.78 0.230 12
Kopp [59] 2015 Colorectal cancer Denmark Caucasian PB KASP 320 449 146 915 679 787 253 1719 0.38 0.311 11
Li [58] 2015 Osteosarcoma China Asian HB PCR-RFLP 60 114 46 220 50 106 66 222 0.54 0.551 9
Liu [57] 2015 NPC China Asian HB TaqMan 236 331 117 684 274 438 195 907 0.46 0.420 9
Liu [57] 2015 NPC China Asian HB TaqMan 316 438 152 906 336 512 224 1072 0.45 0.262 9
Pallavi [56] 2015 Cervical cancer Iran Asian HB PCR-RFLP 98 116 26 240 73 104 113 290 0.57 0.000 9
Wang [55] 2015 Lung cancer China Asian HB PCR-RFLP 113 219 89 421 89 205 131 425 0.55 0.595 10
Wang [54] 2015 Thyroid carcinoma China Asian HB PCR-PAGE 106 186 60 352 171 209 79 459 0.40 0.273 11
Zhang [53] 2015 Lung cancer China Asian HB PCR-RFLP 434 252 32 718 352 290 76 718 0.31 0.162 9
Eskandari [52] 2016 Breast cancer Iran Asian HB AS-PCR 96 122 18 236 62 106 35 203 0.43 0.368 8
Lu [51] 2016 Ovarian cancer China Asian HB PCR-RFLP 115 351 221 687 95 339 253 687 0.61 0.271 10
Rybka [50] 2016 AML Poland Caucasian HB PCR 25 30 7 62 43 69 14 126 0.38 0.079 4
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MAF, minor allele frequency; HWE, Hardy-Weinberg equilibrium; OSCC, oral squamous cell carcinoma; CLL, chronic lymphocytic leukemia; RCC, renal cell carcinoma; HNSCC, head and neck squamous cell carcinoma; HCC, hepatocellular carcinoma; NPC, nasopharyngeal carcinoma; ESCC, esophageal squamous cell carcinoma; EC, endometrial carcinoma; AML, acute myeloid leukemia; PB, population based; HB, hospital based; PCR-PAGE, polymerase chain reaction-polyacrylamide gel electrophoresis; PCR-RFLP, polymerase chain reaction-restriction fragment length polymorphism; PCR-CE, polymerase chain reaction-capillary electrophoresis; PCR-SSCP, polymerase chain reaction-single strand conformation polymorphism; KASP, kompetitive allele specific PCR; AS-PCR, allele-specific polymerase chain reaction.

Meta-analysis results

The main results of this meta-analysis are shown in Table 2 and Figure 2. Overall, the pooled analysis demonstrated a significant, negative association between the NFKB1 -94ins/delATTG polymorphism and overall cancer risk under all five genetic models (described in the Materials and Methods section): DD vs. II: OR = 0.75, 95% CI = 0.64-0.87; ID vs. II: OR = 0.91, 95% CI = 0.83-0.99; DD vs. ID/II: OR = 0.81, 95% CI = 0.71-0.91; ID/DD vs. II: OR = 0.86, 95% CI = 0.78-0.95; and D vs. I: OR = 0.88, 95% CI = 0.81-0.95.

Table 2. Meta-analysis of the association between the NFKB1 -94ins/delATTG (rs28362491) polymorphism and overall cancer risk.

Variables No. of studies Sample size Homozygous Heterozygous Recessive Dominant Allele
DD vs. II ID vs. II DD vs. ID/II ID/DD vs. II D vs. I
OR (95% CI) Phet OR (95% CI) Phet OR (95% CI) Phet OR (95% CI) Phet OR (95% CI) Phet
All 50 18299/ 23484 0.75 (0.64-0.87) <0.001 0.91 (0.83-0.99) <0.001 0.81 (0.71-0.91) <0.001 0.86 (0.78-0.95) <0.001 0.88 (0.81-0.95) <0.001
Cancer type
HCC 6 1471/ 3390 0.65 (0.38-1.11) <0.001 0.82 (0.56-1.19) <0.001 0.74 (0.54-1.02) 0.006 0.75 (0.50-1.15) <0.001 0.80 (0.61-1.07) <0.001
Lung 5 2793/ 2921 0.77 (0.48-1.26) <0.001 0.84 (0.74-0.96) 0.337 0.82 (0.54-1.26) <0.001 0.83 (0.67-1.03) 0.012 0.88 (0.70-1.09) <0.001
Colorectal 4 2399/ 3653 0.96 (0.65-1.43) 0.001 1.08 (0.79-1.47) <0.001 0.92 (0.69-1.22) 0.022 1.05 (0.77-1.44) <0.001 1.01 (0.82-1.24) <0.001
NPC 4 2053/ 2482 0.66 (0.56-0.78) 0.467 0.85 (0.75-0.97) 0.537 0.73 (0.63-0.85) 0.755 0.79 (0.69-0.90) 0.371 0.81 (0.74-0.89) 0.316
Prostate 4 1930/ 2166 0.59 (0.48-0.72) 0.684 0.74 (0.62-0.88) 0.803 0.77 (0.65-0.92) 0.267 0.69 (0.59-0.81) 0.760 0.79 (0.72-0.87) 0.540
Ovarian 4 1463/ 1573 0.54 (0.40-0.73) 0.173 0.73 (0.61-0.87) 0.416 0.68 (0.52-0.89) 0.102 0.67 (0.56-0.79) 0.462 0.75 (0.65-0.86) 0.181
Bladder 3 1058/ 1175 0.93 (0.40-2.21) <0.001 0.95 (0.74-1.22) 0.193 0.97 (0.43-2.17) <0.001 0.96 (0.67-1.37) 0.026 0.97 (0.66-1.43) <0.001
Gastric 3 1062/ 1429 0.97 (0.41-2.31) <0.001 0.88 (0.60-1.30) 0.032 1.11 (0.57-2.15) <0.001 0.90 (0.54-1.49) 0.002 0.98 (0.65-1.50) <0.001
Cervical 3 573/ 755 0.41 (0.15-1.11) 0.001 0.85 (0.67-1.08) 0.627 0.44 (0.17-1.12) 0.001 0.69 (0.45-1.04) 0.050 0.66 (0.39-1.10) <0.001
OSCC 2 674/ 721 0.49 (0.33-0.72) 0.222 0.67 (0.51-0.88) 0.570 0.63 (0.49-0.81) 0.308 0.60 (0.46-0.77) 0.377 0.70 (0.60-0.82) 0.300
Breast 2 710/ 704 0.92 (0.13-6.42) <0.001 1.13 (0.51-2.54) 0.002 0.85 (0.20-3.61) <0.001 1.13 (0.38-3.37) <0.001 1.04 (0.43-2.53) <0.001
Others 10 2113/ 4263 1.07 (0.88-1.31) 0.281 1.16 (0.94-1.42) 0.006 1.00 (0.86-1.16) 0.483 1.14 (0.94-1.38) 0.008 1.06 (0.95-1.19) 0.062
Ethnicity
 Asians 38 15079/ 18673 0.67 (0.55-0.80) <0.001 0.86 (0.79-0.94) <0.001 0.75 (0.65-0.86) <0.001 0.80 (0.72-0.89) <0.001 0.83 (0.76-0.91) <0.001
 Chinese 34 13834/ 16989 0.68 (0.56-0.81) <0.001 0.84 (0.77-0.93) <0.001 0.77 (0.67-0.88) <0.001 0.80 (0.71-0.89) <0.001 0.84 (0.76-0.91) <0.001
 Caucasians 12 3220/ 6559 1.08 (0.92-1.27) 0.221 1.11 (0.94-1.30) 0.004 1.02 (0.88-1.17) 0.269 1.10 (0.95-1.28) 0.010 1.06 (0.98-1.15) 0.141
Source of control
 HB 40 12614/ 15682 0.70 (0.58-0.85) <0.001 0.88 (0.80-0.98) <0.001 0.76 (0.65-0.89) <0.001 0.84 (0.74-0.94) <0.001 0.85 (0.77-0.94) <0.001
PB 10 5685/ 9550 0.95 (0.79-1.15) 0.001 1.00 (0.86-1.15) 0.002 0.98 (0.88-1.09) 0.161 0.98 (0.84-1.14) <0.001 0.98 (0.90-1.08) <0.001
Quality score
>9 19 9894/ 13117 0.87 (0.73-1.04) <0.001 0.93 (0.84-1.04) <0.001 0.92 (0.80-1.05) <0.001 0.91 (0.81-1.03) <0.001 0.94 (0.86-1.02) <0.001
≤9 31 8405/ 12115 0.68 (0.53-0.86) <0.001 0.89 (0.79-1.01) <0.001 0.73 (0.60-0.88) <0.001 0.83 (0.71-0.96) <0.001 0.84 (0.75-0.95) <0.001
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Het, heterogeneity; HCC, hepatocellular carcinoma; NPC, nasopharyngeal carcinoma; OSCC, oral squamous cell carcinoma; HB, hospital-based; PB, population-based.

Figure 2. Forest plot of the association between the NFKB1 -94ins/delATTG polymorphism and overall cancer susceptibility in the allele contrast model.

Figure 2

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The horizontal lines represent the study-specific ORs and 95% CIs, respectively. The diamond represents the pooled OR and corresponding 95% CI.

Stratified analysis by cancer type revealed that the -94ins/delATTG polymorphism significantly decreased lung cancer risk (ID vs. II: OR = 0.84, 95% CI = 0.74-0.96), nasopharyngeal carcinoma risk (DD vs. II: OR = 0.66, 95% CI = 0.56-0.78; ID vs. II: OR = 0.85, 95% CI = 0.75-0.97; DD vs. ID/II: OR = 0.73, 95% CI = 0.63-0.85; ID/DD vs. II: OR = 0.79, 95% CI = 0.69-0.90; D vs. I: OR = 0.81, 95% CI = 0.74-0.89), prostate cancer risk (DD vs. II: OR = 0.59, 95% CI = 0.48-0.72; ID vs. II: OR = 0.74, 95% CI = 0.62-0.88; DD vs. ID/II: OR = 0.77, 95% CI = 0.65-0.92; ID/DD vs. II: OR = 0.69, 95% CI = 0.59-0.81; D vs. I: OR = 0.79, 95% CI = 0.72-0.87), ovarian cancer risk (DD vs. II: OR = 0.54, 95% CI = 0.40-0.73; ID vs. II: OR = 0.73, 95% CI = 0.61-0.87; DD vs. ID/II: OR = 0.68, 95% CI = 0.52-0.89; ID/DD vs. II: OR = 0.67, 95% CI = 0.56-0.79; D vs. I: OR = 0.75, 95% CI = 0.65-0.86), and oral squamous cell carcinoma risk (DD vs. II: OR = 0.49, 95% CI = 0.33-0.72; ID vs. II: OR = 0.67, 95% CI = 0.51-0.88; DD vs. ID/II: OR = 0.63, 95% CI = 0.49-0.81; ID/DD vs. II: OR = 0.60, 95% CI = 0.46-0.77; D vs. I: OR = 0.70, 95% CI = 0.60-0.82). However, no correlation was observed between NFKB1 -94ins/delATTG polymorphism and other types of cancer.

When stratified by population, a significant association between NFKB1 -94ins/delATTG polymorphism and decreased cancer risk among Asians was detected under all genetic models (DD vs. II: OR = 0.67, 95% CI = 0.55-0.80; ID vs. II: OR = 0.86, 95% CI = 0.79-0.94; DD vs. ID/II: OR = 0.75, 95% CI = 0.65-0.86; ID/DD vs. II: OR = 0.80, 95% CI = 0.72-0.89; D vs. I: OR = 0.83, 95% CI = 0.76-0.91). As most of the studies were performed on the Chinese population, we determined the association of NFKB1 -94ins/delATTG polymorphism with cancer risk on Chinese subjects. In this case, the results also showed a protective role against cancer (DD vs. II: OR = 0.68, 95% CI = 0.56-0.81; ID vs. II: OR = 0.84, 95% CI = 0.77-0.93; DD vs. ID/II: OR = 0.77, 95% CI = 0.67-0.88; ID/DD vs. II: OR = 0.80, 95% CI = 0.71-0.89; D vs. I: OR = 0.84, 95% CI = 0.76-0.91). No association was observed, however, for Caucasians.

Upon stratification based on the sources of controls, the NFKB1 -94ins/delATTG polymorphism had a protective role against cancer in hospital-based groups (DD vs. II: OR = 0.70, 95% CI = 0.58-0.85; ID vs. II: OR = 0.88, 95% CI = 0.80-0.98; DD vs. ID/II: OR = 0.76, 95% CI = 0.65-0.89; ID/DD vs. II: OR = 0.84, 95% CI = 0.74-0.94; D vs. I: OR = 0.85, 95% CI = 0.77-0.94).

After stratification by quality score, a significantly decreased cancer risk was observed for studies with quality scores ≤9 (DD vs. II: OR = 0.68, 95% CI = 0.53-0.86; DD vs. ID/II: OR = 0.73, 95% CI = 0.60-0.88; ID/DD vs. II: OR = 0.83, 95% CI = 0.71-0.96; D vs. I: OR = 0.84, 95% CI = 0.75-0.95).

Heterogeneity and sensitivity analysis

Statistically significant between-study heterogeneity was found in the pooled analysis under the five genetic models (P < 0.001). Thus, the random-effect model was applied to calculate the ORs and 95% CIs. Sensitivity analysis using the leave-one-out cross-validation method was conducted to assess the impact of each single study on the overall risk estimates. The omission of each individual study did not have substantial influence on the risk estimates, supporting the credibility and reliability of this meta-analysis (data not shown).

Publication bias

Publication bias was assessed by Begg's funnel plot and quantitative Egger's test. The funnel plot showed a symmetrical shape (Figure 3), suggesting no publication bias, a conclusion further supported by the Egger's test (DD vs. II: P = 0.158; ID vs. II: P = 0.340, DD vs. ID/II: P = 0.157; ID/DD vs. II: P = 0.221; and D vs. I: P = 0.250).

Figure 3. Funnel plot analysis to detect publication bias for NFKB1 -94ins/delATTG polymorphism under the allele contrast model.

Figure 3

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Each point represents a separate study.

Trial sequential analysis and false-positive report probability (FPRP) analyses

To minimize random errors and strengthen the robustness of our conclusions, we performed TSA (Figure 4). This analysis showed that the cumulative z-curve crossed the trial sequential monitoring boundary before reaching the required information size, suggesting that the cumulative evidence is sufficient and no further evidence is needed to verify the conclusions.

Figure 4. Trial sequential analysis for NFKB1 -94ins/delATTG polymorphism under the allele contrast model.

Figure 4

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We finally calculated the FPRP values for all observed significant findings. With the assumption of a prior probability of 0.1, the FPRP values were all <0.20, suggesting that these significant associations were noteworthy (Table 3).

Table 3. False-positive report probability values for associations between the NFKB1 -94ins/delATTG polymorphism and overall cancer risk.

Variables OR (95% CI) P a Power b Prior Probability
0.25 0.1 0.01 0.001 0.0001
Homozygous (DD vs. II)
 All 0.75 (0.64-0.87) 2.45*10-4 1.000 0.001 0.002 0.024 0.197 0.710
 NPC 0.66 (0.56-0.78) 1.99*10-6 0.652 0.000 0.000 0.000 0.003 0.030
 Prostate 0.59 (0.48-0.72) 6.40*10-7 0.860 0.000 0.000 0.000 0.001 0.007
 Ovarian 0.54 (0.40-0.73) 6.62*10-6 0.612 0.000 0.000 0.001 0.011 0.098
 OSCC 0.49 (0.33-0.72) 3.26*10-4 0.388 0.003 0.008 0.077 0.457 0.894
 Asians 0.67 (0.55-0.80) 1.81*10-5 1.000 0.000 0.000 0.002 0.018 0.153
 Chinese 0.68 (0.56-0.81) 3.45*10-5 1.000 0.000 0.000 0.003 0.033 0.257
 HB 0.70 (0.58-0.85) 3.30*10-4 1.000 0.001 0.003 0.032 0.248 0.767
 QS ≤9 0.68 (0.53-0.86) 1.63*10-3 1.000 0.005 0.014 0.139 0.620 0.942
Heterozygous (ID vs. II)
 All 0.91 (0.83-0.99) 0.024 1.000 0.066 0.175 0.700 0.959 0.996
 Lung 0.84 (0.74-0.96) 7.74*10-3 1.000 0.023 0.065 0.434 0.885 0.987
 NPC 0.85 (0.75-0.97) 0.017 1.000 0.048 0.131 0.623 0.943 0.994
 Prostate 0.74 (0.62-0.88) 7.44*10-4 0.998 0.002 0.007 0.069 0.427 0.882
 Ovarian 0.73 (0.61-0.87) 5.68*10-4 0.979 0.002 0.005 0.054 0.367 0.853
 OSCC 0.67 (0.51-0.88) 3.99*10-3 0.800 0.015 0.043 0.331 0.833 0.980
 Asians 0.86 (0.79-0.94) 7.59*10-4 1.000 0.002 0.007 0.070 0.431 0.884
 Chinese 0.84 (0.77-0.93) 4.59*10-4 1.000 0.001 0.004 0.043 0.314 0.821
 HB 0.88 (0.80-0.98) 0.015 1.000 0.043 0.118 0.596 0.937 0.993
Recessive (DD vs. ID/II)
 All 0.81 (0.71-0.91) 3.22*10-4 1.000 0.001 0.003 0.031 0.243 0.763
 NPC 0.73 (0.63-0.85) 3.26*10-5 0.841 0.000 0.000 0.004 0.037 0.279
 Prostate 0.77 (0.65-0.92) 3.78*10-3 0.999 0.011 0.033 0.272 0.791 0.974
 Ovarian 0.68 (0.52-0.89) 4.62*10-3 0.976 0.014 0.041 0.319 0.826 0.979
 OSCC 0.63 (0.49-0.81) 2.37*10-4 0.356 0.002 0.006 0.062 0.399 0.869
 Asians 0.75 (0.65-0.86) 6.02*10-5 1.000 0.000 0.001 0.006 0.057 0.376
 Chinese 0.77 (0.67-0.88) 1.44*10-4 1.000 0.000 0.001 0.014 0.126 0.591
 HB 0.76 (0.65-0.89) 5.23*10-4 1.000 0.002 0.005 0.049 0.343 0.840
 QS ≤9 0.73 (0.60-0.88) 1.23*10-3 1.000 0.004 0.011 0.109 0.551 0.925
Dominant (ID/DD vs. II)
 All 0.86 (0.78-0.95) 2.68*10-4 1.000 0.008 0.024 0.210 0.728 0.964
 NPC 0.79 (0.69-0.90) 2.94*10-4 0.997 0.001 0.003 0.028 0.227 0.747
 Prostate 0.69 (0.59-0.81) 8.36*10-6 0.796 0.000 0.000 0.001 0.010 0.095
 Ovarian 0.67 (0.56-0.79) 3.45*10-6 0.531 0.000 0.000 0.001 0.006 0.061
 OSCC 0.60 (0.46-0.77) 9.87*10-5 0.186 0.002 0.005 0.050 0.347 0.842
 Asians 0.80 (0.72-0.89) 9.05*10-5 1.000 0.000 0.001 0.009 0.083 0.475
 Chinese 0.80 (0.71-0.89) 9.39*10-5 1.000 0.000 0.001 0.009 0.086 0.484
 HB 0.84 (0.74-0.94) 2.29*10-3 1.000 0.007 0.020 0.185 0.696 0.958
 QS ≤9 0.83 (0.71-0.96) 0.013 1.000 0.038 0.105 0.563 0.929 0.992
Allele (D vs. I)
 All 0.88 (0.81-0.95) 8.86*10-4 1.000 0.003 0.008 0.081 0.469 0.899
 NPC 0.81 (0.74-0.89) 9.60*10-6 1.000 0.000 0.000 0.001 0.009 0.088
 Prostate 0.79 (0.72-0.87) 1.03*10-6 1.000 0.000 0.000 0.000 0.001 0.010
 Ovarian 0.75 (0.65-0.86) 3.47*10-5 1.000 0.000 0.000 0.003 0.033 0.257
 OSCC 0.70 (0.60-0.82) 1.11*10-5 0.809 0.000 0.000 0.001 0.014 0.121
 Asians 0.83 (0.76-0.91) 4.78*10-5 1.000 0.000 0.000 0.005 0.046 0.323
 Chinese 0.84 (0.76-0.91) 9.24*10-5 1.000 0.000 0.001 0.009 0.085 0.480
 HB 0.85 (0.77-0.94) 9.63*10-4 1.000 0.003 0.009 0.087 0.490 0.906
 QS ≤9 0.84 (0.75-0.95) 4.84*10-3 1.000 0.014 0.042 0.324 0.829 0.980
Open in a new tab

CI, confidence interval; OR, odds ratio; NPC, Nasopharyngeal carcinoma; OSCC, oral squamous cell carcinoma; HB, Hospital based; QS, quality score.

a Chi-square test was adopted to calculate the genotype frequency distributions.

b Statistical power was calculated using the number of observations in the subgroup and the OR and P values in this table.

DISCUSSION

In this meta-analysis, we found that the NFKB1 -94ins/delATTG promoter polymorphism was significantly associated with decreased overall cancer risk under the five genetic models. To the best of our knowledge, this is the most comprehensive meta-analysis on this topic by now.

Numerous studies have suggested that polymorphisms in genes encoding inflammatory response factors, such as TNF-alpha -308G>A [6], IL6 -174G>C [90], and NFKBIA -826C>T [91] may contribute to cancer susceptibility. Song et al. [92] reported that the NFKB1 -94ins/delATTG polymorphism analyzed here (rs28362491) increased the risk of colorectal cancer in a Southern Chinese population; this association was also observed in several publications [71]. However, contradictory conclusions were also reported, namely a null association, or decreased cancer susceptibility. To address this controversy, at least six meta-analyses were performed. The first one, performed in 2011 by Zou et al. [32], included only 2,743 cases and 2,195 controls from 11 studies. They did not observe any association between the -94ins/delATTG variant and overall cancer. However, an ethno-specific association was detected by subgroup analysis; the D allele was protective against cancer in Asians, but increased the risk in Caucasians. Afterwards, Wang et al. [33] conducted an updated meta-analysis including 5,196 cases and 6,614 controls from 19 publications. They found that variant homozygotes (DD) had a decreased risk of cancer compared with wild-type homozygotes (II). The association was also found under the dominant genetic model (DD+DI vs. II). In subgroup analysis, a significantly decreased risk was observed in Asians but not in Caucasians. In addition, this susceptibility was cancer-specific, as it was observed for all cancer types examined, except for colorectal cancer. In 2014, four updated meta-analyses were published. Upon revision of 6,494 cases and 9,884 controls from 23 studies, Xu et al. [29] found that the -94ins/delATTG polymorphism was significantly associated with increased cancer risk under all the inheritance models. Stratified analysis by cancer type showed significant associations for ovarian cancer, hepatocellular carcinoma, and oral squamous cell carcinoma, but not for bladder cancer or lung cancer. Ethnicity subgroup analysis indicated that the polymorphism contributed to cancer risk in the Asian, but not the Caucasian, population. Another study by Yang et al. [28], which included 21 reports with 6,127 cases and 9,238 controls, also detected an increased overall cancer risk. Stratified analysis revealed a significant association between the polymorphism and ovarian, oral, and prostate cancers. These findings were also specific to the Asian population. Duan et al. [31] reviewed a total of 25 studies that included 8,750 cancer cases and 9,170 controls. They found that the insertion allele of the -94ins/delATTG polymorphism significantly increased cancer risk, both in overall genetic analysis as well as in Asians. Stratified analysis revealed that the polymorphism was associated with increased risk for oral squamous cell carcinoma and ovarian cancer, but not for colorectal cancer, bladder cancer, or renal cell cancer. In another meta-analysis involving 7,281 cases and 10,039 controls from 25 case-control studies, Nian et al. [30] found that the -94ins/delATTG polymorphism was significantly associated with decreased susceptibility to cancer in overall population under homozygous, recessive, dominant, and allele contrast models. Subgroups analysis based on ethnicity revealed that the polymorphism conferred decreased cancer susceptibility in the Asian population.

Since then, approximately 20 new relevant case-control studies in English and Chinese have emerged, some containing large samples and convincing results. Our study re-evaluated the impact of the NFKB1 -94ins/delATTG polymorphism on cancer risk. In line with some previous meta-analyses, our pooled analysis revealed a significant association with decreased cancer risk under all five genetic models. Conversely, we found that the del allele of the NFKB1 -94ins/delATTG polymorphism conferred a significantly decreased risk of cancer in the pooled analysis. Compared with the ins allele, the ins allele significantly enhances the binding ability to nuclear proteins and increases transcriptional activity, which eventually upregulates p50 (the active NF-κB1 subunit) expression [21]. Given the tumor-promoting role of p50 and NF-κB, it is biologically plausible that the -94del allele confers decreased cancer susceptibility.

In line with previous data, our study detected a significant association between the -94ins/delATTG polymorphism and cancer risk in Asians, but not in Caucasians, under all five genetic models. It is thus likely that the allelic distribution of this polymorphism vary geographically and ethnically, thus leading to the discrepancies in cancer risk. This may indicate that these groups have distinct environmental or genetic cancer co-etiologies. Stratification by cancer type showed that the NFKB1 -94ins/delATTG polymorphism was inversely associated with the risk of lung cancer, nasopharyngeal carcinoma, prostate cancer, ovarian cancer, and oral squamous cell carcinoma, but no association was found for hepatocellular carcinoma, colorectal cancer, bladder cancer, gastric cancer, cervical cancer, breast cancer, or other cancers. This phenomenon may be partly attributed to the inherent heterogeneity of oncogenic progression in different cancer types [93], although the insufficient statistical power caused by the relatively small number of studies on each cancer type may also be a factor.

The credibility of our conclusions is supported by the inclusion of Chinese-language studies, exclusion of publications with controls violating the Hardy-Weinberg equilibrium, and inclusion of subgroup, publication bias, and sensitivity analyses. Among the limitations of our meta-analysis are a significant between-study heterogeneity, detected in some comparisons, which may diminish the strength of our conclusions. The source of this heterogeneity may be ascribed to sample size, genotyping methods, ethnicity, source of controls, as well as the studies’ diverse quality scores. Second, we assessed the association between the NFKB1 -94ins/delATTG polymorphism and cancer risk from a genetic perspective only, by using unadjusted ORs. Multiple potentially influential factors such as life style, environmental exposure, and gene-environment interactions should be considered to obtain a more precise risk estimation. Third, the number of studies in certain subgroup analyses was too small to obtain a reliable association. For instance, only six publications were included for hepatocellular carcinoma, and fewer studies were available for breast cancer and oral squamous cell carcinoma, which restrains further analysis for risk factors. Finally, the meta-analysis is a type of retrospective study with several inherent drawbacks: inconsistent qualities of primary studies, incomplete histological details, misclassified genotypes, different definitions of disease status, and improperly matched sources of controls.

In conclusion, despite these limitations, and in agreement with several previous studies, this meta-analysis draws the robust conclusion that NFKB1 -94ins/delATTG polymorphism is associated with decreased cancer risk, especially in the Asian population. These findings indicate a possible involvement of NFKB1 in the etiology of tumorigenesis, and suggest the potentially relevant therapeutic value of NF-κB modulation in cancer prevention. Further multi-center, well-designed investigations with larger sample sizes that include gene-environment interactions assessment are warranted to confirm our findings.

MATERIALS AND METHODS

Publication search

We performed a comprehensive literature search by using the PubMed and EMBASE databases, without language limitations, up to July 1, 2016. The following search terms were used: “polymorphism or SNP or single nucleotide polymorphism or variant” and “NFKB1/NF-κB1 or nuclear factor kappa B1”, and “tumor or cancer or neoplasm or carcinoma”. We also searched the China National Knowledge Infrastructure (CNKI) and WANFANG databases to obtain additional, relevant studies. Retrieved articles were manually screened to determine eligible studies. When two or more publications containing overlapping data were found, the largest study was included in the final meta-analysis.

Inclusion/exclusion criteria

All articles included in the current analysis met the following criteria: 1) evaluation of the association between NFKB1 -94ins/delATTG polymorphism and cancer risk; 2) case-control studies; 3) sufficient information provided to estimate ORs and 95% CIs; 4) NFKB1 -94ins/delATTG genotype frequency in agreement with HWE in controls. Exclusion criteria were as follows: 1) case-only studies; 2) meta-analysis or reviews; 3) studies that lacked detailed genotyping data; 4) duplicates of previous publications.

Data extraction

Two authors (Z.Z. and W.F.) evaluated all eligible studies independently and extracted the following information: first author's surname, year of publication, cancer type, country, ethnicity, source of controls, genotyping methods, and genetic distribution of cases and controls. Stratification analyses were conducted by cancer type, ethnicity (Asians, Caucasians), source of control (hospital-based and population-based) and quality score (>9 and ≤9). If a study contained two or more ethnic groups or cancer types, we divided the study accordingly.

Trial sequential analysis

TSA was performed as described by us previously [94]. Briefly, after adopting a level of significance of 5% for type I error and of 30% for type II error, the required information size was calculated, and TSA monitoring boundaries were built.

FPRP analysis

The FPRP values at different prior probability levels for all significant findings were calculated as described by us previously [95]. Briefly, 0.2 was set as FPRP threshold and assigned a prior probability of 0.01 to detect an OR of 0.67 (for protective effects) for an association with genotypes under investigation. A FPRP value <0.2 denoted a noteworthy association.

Statistical methods

Goodness-of-fit χ2 test was used to assess HWE in the control subjects. Departure from HWE was assessed using a P < 0.05 as threshold in each study. The strength of the association between NFKB1 -94ins/delATTG polymorphism and cancer risk was assessed by calculating ORs and corresponding 95% CIs. Five genetic models were adopted: homozygote model (DD, homozygous deletion (del/del) vs. II, homozygous insertion (ins/ins) or wild-type); heterozygote model (ID, heterozygous ins/del vs. II); recessive model (DD vs. ID/II); dominant model (ID/DD vs. II); and allele contrast model (D vs. I). Subgroup and stratification analyses were also performed to test the association by ethnicity, cancer type, source of control and quality score. We performed χ2-based Q-test to assess heterogeneity between study results. The fixed-effects model (Mantel-Haenszel method) was used if the studies were found to be homogeneous (with P > 0.10 for the Q-test). Otherwise, the random-effects model (DerSimonian and Laird method) was adopted to estimate the pooled OR [96–99]. Quality assessment for each study was performed using the quality assessment criteria described previously (Supplementary Table 1) [98]. Sensitivity analysis was carried out by individually removing each study and reanalyzing the pooled risk estimates. Potential publication bias was estimated by Begg's funnel plot and Egger's linear regression, where an asymmetric plot and a P value < 0.05, respectively, indicate the presence of publication bias. All the data management and statistical analyses were completed using STATA software (Stata Corporation, College Station, TX; version 11.0). All the P values were two-sided. A P value of < 0.05 was considered statistically significant.

SUPPLEMENTARY TABLES

Acknowledgments

This study was supported by grants from the National Natural Science Foundation of China (Grant No. 81502046), and the Special Financial Grant from the China Postdoctoral Science Foundation (Grant No. 2014T70836).

Footnotes

CONFLICTS OF INTEREST

The authors declare no competing financial interests.

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