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  • Primary research
  • Open Access

New insights into the association between AXIN2 148 C/T, 1365 C/T, and rs4791171 A/G variants and cancer risk

Contributed equally
Cancer Cell International201919:119

https://doi.org/10.1186/s12935-019-0840-z

  • Received: 19 February 2019
  • Accepted: 25 April 2019
  • Published:

Abstract

Background

Many epidemiological studies have investigated association of AXIN2 variants on overall cancer risks; however, the available results remain inconsistent.

Methods

An updated analysis was conducted to ascertain a more accurate estimation of the correlation between AXIN2 148 C/T, 1365 C/T, and rs4791171 A/G polymorphisms and cancer risk. We also used in silico tools to assess the effect of AXIN2 expression on cancer susceptibility and overall survival time.

Results

A total of 4281 cases and 3955 control participants were studied. The overall results indicated that AXIN2 148 C/T variant was associated with cancer risk (allelic contrast: OR = 0.88, 95% CI 0.77–0.99, Pheterogeneity = 0.004; dominant model: OR = 0.82, 95% CI 0.69–0.96, Pheterogeneity = 0.022), especially for lung and prostate adenocarcinoma. Similar results were observed in 1365 C/T polymorphism (OR = 0.71, 95% CI 0.61–0.98, Pheterogeneity = 0.873; dominant model: OR = 0.66, 95% CI 0.47–0.94, Pheterogeneity = 0.775). Moreover, in subgroup analysis by ethnicity, similar findings were obtained for Asian and Caucasian populations. Results from in silico tools suggested that AXIN2 expressions in lung adenocarcinoma were lower than that in normal group.

Conclusions

Our findings indicated that AXIN2 148 C/T and 1365 C/T variants may be associated with decreased cancer susceptibility.

Keywords

  • AXIN2
  • Polymorphism
  • Cancer
  • Analysis
  • In silico

Background

The continuing changes in global population and epidemiology indicate that the burden of cancer will continue to increase in the coming decades. Cancer is considered as a multifactorial disease and its occurrence is associated with several factors such as lifestyle, environment and single nucleotide polymorphism (SNP) [13]. With the remarkable development of a series of genotyping technologies including genome-wide association studies (GWAS), our understanding of genetic factors related to carcinogenesis has substantially expanded [46]. Wnt/β-catenin signaling pathway is known to play a central role in the process of embryogenesis, and abnormalities of this pathway are associated with numerous human malignant tumors [7, 8]. Axin2 protein acts as a negative regulator of Wnt pathway and plays a crucial role in cell differentiation, migration, cytometaplasia, and apoptosis [911]. Axin2 protein is also involved in down-regulation of β-catenin translocation ito the nucleus. In this process, Axin2 binds to transcription factors and subsequently inhibits the expression of numerous target genes including vascular matrix metalloproteinases (MMP), cox 2, and endothelial growth factor (VEGF) [12, 13].

Mutations of AXIN2 gene has been identified by previous genotyping technologies. This gene is located on human chromosome 17q23-q24 and composed of 10 exons, which encodes a protein consisting of 843 amino acids [14]. Loss of heterozygosity of this gene was previously identified in a number of carcinomas such as hepatoblastoma, hepatocellular carcinoma, melanoma, gastrointestinal, ovarian, synchronous endometrial carcinomas [1518]. Association between AXIN2 variants and carcinoma susceptibility has also been reported by previous publications. These SNPs including: 148 C>T (rs2240308), 1365 C/T (rs9915936), and rs4791171 A/G (NC_000017.10) [1924]. Study population of these genetic variants has involved numerous ethnicities such as Brazilians, Iranians, Chinese, Saudi Arabians, Indians and Poles [2027]. These studies also evaluated various malignancies; nevertheless, there were ambiguous conclusions on the relationship between the AXIN2 polymorphisms and cancer risk among different case–control studies.

For AXIN2 148 C>T polymorphism, a case–control study observed no statistically significant correlation between controls and prostate adenocarcinoma in Turkish population [27]. However, another two studies identified notable decreased risks in Iranian colorectal cancer subjects and Chinese prostate adenocarcinoma participants [21, 22]. Therefore, a meta analysis with all eligible data based on the inclusion criteria was conducted to further assess the associations between AXIN2 148 C/T, 1365 C/T, and rs4791171 A/G polymorphisms and cancer risk [1933].

Materials and methods

Literature retrieval strategy

PubMed, Web of Science, Google Scholar, and China Wanfang Databases were systematically searched to identify all eligible published articles on AXIN2 variants and cancer susceptibility. The following terms were utilized for searching abstracts and titles: “Axin OR AXIN2”, “polymorphism OR SNP OR variant”, and “cancer OR adenocarcinoma OR carcinoma OR tumor”. The latest search was conducted on Jan 31, 2019 with no language restrictions. Furthermore, we also carefully screened and manually searched the review or original publications for more eligible studies.

Study selection

Two authors independently chose the eligible studies based on the inclusion criteria: (a) case–control studies that evaluated the association between AXIN2 148 C/T, 1365 C/T, and rs4791171 A/G variants and cancer risk; (b) studies that involved available information for measuring odds ratio (OR) with 95% confidence intervals (CIs); (c) genotype distribution in controls must be conformed to Hardy-Weinberg equilibrium (HWE).

Data extraction

All related information was independently screened by two investigators (L Shi and B Xu) from each enrolled study, including the name of first author, year of publication, country of origin, ethnicity, source of control, genotyping method, cancer type, total number of participants, P value for HWE, age range, genotyping data of AXIN2 148 C/T, 1365 C/T, and rs4791171 A/G variants in cases and controls. Disagreement should be resolved by discussion with a third author (W Zhang). If the controversial content still existed, it should be addressed by all investigators to reach a consensus.

Statistical analysis

The strength of the relationship between AXIN2 148 C/T, 1365 C/T, and rs4791171 A/G polymorphisms and cancer susceptibility was measured by calculating OR with 95% CI. A total of four genetic models were adopted in the current analysis, including allelic comparison model (M-allele vs. W-allele), homozygote contrast model (MM vs. WW), heterozygote model (MW vs. WW), and dominant model (MM + MW vs. WW). The χ2-test-based Q test was performed to investigate P value for heterogeneity among eligible researches. If P < 0.05, indicating that a significant heterogeneity was found, we employed the random-effects model (DerSimonian–Laird method) [34]. On the other hand, the fixed-effects model (Mantel–Haenszel method) was carried out [35]. We adopted qualitative funnel plot to assess possible publication bias by calculating the standard error of log(OR) for each research plotted against its log(OR). We further conducted quantitative Egger’s test to evaluate funnel plot asymmetry [36]. The web-based program was applied to check for deviations from the Hardy–Weinberg equilibrium (HWE) of distribution frequencies (http://ihg2.helmholtz-muenchen.de/cgibin/hw/hwa1.pl) [37]. The P value more than 0.05 suggested an HWE balance. Moreover, we applied leave-one-out sensitivity analyses to calculate the stability of pooled results [38]. All of the above analyses were conducted by STATA software v11.0 (Stata Corporation, TX).

In silico analysis of AXIN2 expression

An online gene expression database was adopted to investigate the AXIN2 expression in lung and prostate adenocarcinoma tissues and the paracancerous tissues. (http://gemini.cancer-pku.cn/) [39]. RNA expression profiles of 446 pathologically diagnosed lung adenocarcinoma (including 387 Caucasians, 51 African-Americans, and 8 Asians) and 153 prostate adenocarcinoma tissues (containing 147 Caucasians and 6 African-Americans) were evaluated by this database. The Cancer Genome Atlas (TCGA) samples were also utilized to investigate the high and low expression of AXIN2 on cancer susceptibility and overall survival time. Moreover, the String online server was applied to assess the gene–gene correlation of AXIN2 (http://string-db.org/).

Results

Characteristics of studies

As was shown in Table 1, 15 articles were finally retrieved in the present analysis, which contains 22 case–control studies for AXIN2 148 C/T, 1365 C/T, and rs4791171 A/G variants. There were 2909 cancer subjects and 2907 control volunteers for 148 C/T polymorphism, 587 cancer subjects and 605 controls for 1365 C/T variant, 785 cases and 443 controls for rs4791171 A/G variant. Furthermore, we checked the minor allele frequencies (MAF) of three AXIN2 variants by Trans-Omics for Precision Medicine (TOPMed) online (https://www.ncbi.nlm.nih.gov/snp/) (Fig. 1). MAF of AXIN2 148 C/T were: in Africans, 0.119; Asians, 0.426; Europeans, 0.526; Americans, 0.561; others (including Pacific Islanders), 0.470; Global, 0.474. MAF of AXIN2 1365 C/T were: in Africans, 0.069; East Asians, 0.192; Europeans, 0.114; Americans, 0.100; others, 0.090; Global, 0.104. Finally, MAF of AXIN2 rs4791171 A/G were: in Africans, 0.267; East Asians, 0.370; Europeans, 0.681; Americans, 0.620; others, 0.670; Global, 0.547. In stratified analysis by ethnicity, seven studies were performed in Caucasian populations, twelve studies were in Asian descendants, and two were done in Arabians and one was in Latin descendants. Eight studies were conducted using population based controls and the rest 14 studies were utilizing hospital based controls. The classical genotyping method, PCR-restriction fragment length polymorphism (RFLP) was adopted in nine of these studies.
Table 1

Basic information for included studies of the correlation between AXIN2 148 C/T, 1365 C/T, and rs4791171 A/G variations and cancer risk

Author/year

Origin

Ethnicity

Source

Cancer

Method

Age range

Age range

Case

Control

Case

Control

HWE

148 C/T

Case

Control

TT

TC

CC

TT

TC

CC

Kanzaki 2006 [26]

Japan

Asian

PB

LC

PCR–RFLP

66.4 (mean)

NA

160

109

8

71

81

15

52

42

0.863

Kanzaki 2006 [26]

Japan

Asian

PB

HNC

PCR–RFLP

66.4 (mean)

NA

63

109

9

29

25

15

52

42

0.863

Kanzaki 2006 [26]

Japan

Asian

PB

CRC

PCR–RFLP

66.4 (mean)

NA

113

109

15

44

54

15

52

42

0.863

Gunes 2009 [19]

Turkey

Caucasian

PB

LC

PCR

59.22 ± 9.63

57.01 ± 7.89

100

100

8

47

45

16

52

32

0.501

Gunes 2010 [25]

Turkey

Caucasian

HB

AT

PCR

58.66 ± 8.04

57.01 ± 7.89

100

100

16

45

39

16

52

32

0.501

 Pinarbasi 2011 [27]

Turkey

Caucasian

HB

PC

PCR

70.38 ± 7.78

68.55 ± 4.47

84

100

19

35

30

18

48

34

0.883

Naghibal 2012 [21]

Iran

Asian

HB

CRC

PCR–RFLP

NA

NA

110

179

19

57

34

26

98

55

0.096

Liu 2014 [28]

China

Asian

PB

LC

RT-PCR

57.78 ± 9.89

52.21 ± 10.56

498

533

47

216

235

67

255

211

0.457

Ma 2014 [22]

China

Asian

PB

PC

PCR

71.2 (mean)

70.4 (mean)

103

100

11

31

61

9

52

39

0.153

Mostowska 2014 [24]

Poland

Caucasian

HB

OC

PCR–RFLP

58.4 ± 9.7

57.4 ± 7.5

228

282

46

115

67

65

146

71

0.546

Yadav 2016 [23]

India

Asian

HB

GBC

Taqman

52.05 ± 10.06

43.2 ± 11.5

564

250

119

253

192

44

108

98

0.138

Liu 2016 [32]

China

Asian

HB

PTC

MassARRAY

45.13 ± 10.97

41.9 ± 10.22

53

50

2

24

27

4

29

17

0.084

Kim 2016 [31]

Korea

Asian

HB

HCC

Goldengate

53.8 ± 10.3

41.1 ± 10.3

242

482

18

100

124

41

195

246

0.789

Rosales 2016 [29]

Mexico

Latin

PB

CRC

PCR–RFLP

59.03 (mean)

36.88 (mean)

188

99

54

109

25

18

59

22

0.054

Bahl 2017 [30]

India

Asian

HB

LC

PCR–RFLP

57.38 ± 10.74

53.23 ± 10.44

303

305

54

150

99

80

144

81

0.330

1365 C/T

         

TT

TC

CC

TT

TC

CC

 

Bahl 2017 [30]

India

Asian

HB

LC

PCR–RFLP

57.38 ± 10.74

53.23 ± 10.44

303

305

6

29

268

5

51

249

0.215

Pinarbasi 2011 [27]

Turkey

Caucasian

HB

PC

PCR

70.38 ± 7.78

68.55 ± 4.47

84

100

0

7

77

0

8

92

0.677

Gunes 2010 [25]

Turkey

Caucasian

HB

AT

PCR

58.66 ± 8.039

57.01 ± 7.89

100

100

0

9

91

0

12

88

0.523

Gunes 2009 [19]

Turkey

Caucasian

PB

LC

PCR

59.22 ± 9.63

57.01 ± 7.89

100

100

0

9

91

0

12

88

0.523

rs4791171A/G

         

GG

GA

AA

GG

GA

AA

 

Alanazi 2013 [20]

Saudi

Arabian

HB

BC

RT-PCR

48.0 (mean)

NA

99

83

21

44

34

17

44

22

0.559

Yadav 2016 [23]

India

Asian

HB

GBC

PCR–RFLP

52.05 ± 10.06

43.2 ± 11.5

564

250

228

248

88

97

118

35

0.926

Parine 2019  [33]

Saudi

Arabian

HB

CRC

TaqMan

57.0 (mean)

NA

122

110

27

55

40

24

48

38

0.236

HB hospital-based, PB population-based, AT astrocytoma, BC breast cancer, CRC colorectal cancer, GBC gallbladder cancer, PCR–RFLP polymerase chain reaction and restrictive fragment length polymorphism, RT real time, NA not available, NOS Newcastle–Ottawa Scale, HCC hepatocellular carcinoma, HNC head and neck cancer, HWE Hardy–Weinberg equilibrium of controls, LC lung adenocarcinoma, PC prostate adenocarcinoma, PTC papillary thyroid carcinoma, OC ovarian cancer

Fig. 1
Fig. 1

Minor allele and major allele frequencies for AXIN2 148 C/T (a), 1365 C/T (b), and rs4791171 A/G (c) variants in controls stratified by ethnicity. Vertical line, allele frequency; Horizontal line, allele type

Quantitative synthesis

In the overall analysis, we identified a significant correlation between AXIN2 148 C/T variant and cancer risk (allele contrast: OR = 0.88, 95% CI 0.77–0.99, Pheterogeneity = 0.004, P = 0.041; heterozygote comparison: OR = 0.84, 95% CI 0.75–0.95, Pheterogeneity = 0.112, P = 0.004; dominant genetic model: OR = 0.82, 95% CI 0.69–0.96, Pheterogeneity = 0.022, P = 0.015) (Table 2). In subgroup analysis by race, we observed positive results in Asians (allele contrast: OR = 0.85, 95% CI 0.73–0.98, Pheterogeneity = 0.016, P = 0.027; dominant genetic model: OR = 0.80, 95% CI 0.66–0.96, Pheterogeneity = 0.030, P = 0.020) and Caucasians (dominant genetic model: OR = 0.76, 95% CI 0.59–0.98, Pheterogeneity = 0.701, P = 0.036), (Fig. 2). Moreover, subgroup analysis by cancer type suggested that 148 C/T variant was associated with a decreased cancer risk in lung adenocarcinoma (allele contrast: OR = 0.74, 95% CI 0.65–0.84, P value for heterogeneity = 0.602, P < 0.001; dominant genetic model: OR = 0.70, 95% CI 0.59–0.84, Pheterogeneity = 0.803, P < 0.001, Fig. 3). Similar finding was indicated in prostate adenocarcinoma (heterozygote comparison: OR = 0.54, 95% CI 0.35–0.84, Pheterogeneity = 0.088, P = 0.006; dominant genetic model: OR = 0.62, 95% CI 0.41–0.93, Pheterogeneity = 0.078, P = 0.022). In subgroup analysis by source of control, similar results were also observed in population-based studies. Furthermore, we identified notable correlation between AXIN2 1365 C/T variant and cancer risk (allele contrast: OR = 0.71, 95% CI 0.61–0.98, Pheterogeneity = 0.873, P = 0.038; heterozygote comparison: OR = 0.63, 95% CI 0.44–0.91, Pheterogeneity = 0.668, P = 0.014; dominant model: OR = 0.66, 95% CI 0.47–0.94, Pheterogeneity = 0.775, P = 0.021). For rs4791171 A/G polymorphism, no significant association was indicated (allele comparison, OR = 0.99, 95% CI 0.85–1.17, Pheterogeneity = 0.786, P = 0.864; homozygote contrast, OR = 0.94, 95% CI 0.66–1.33, Pheterogeneity = 0.873, P = 0.728; heterozygote contrast, OR = 0.86, 95% CI 0.62–1.17, Pheterogeneity = 0.522, P = 0.322; dominant model, OR = 0.89, 95% CI 0.66–1.19, Pheterogeneity = 0.575, P = 0.429).
Table 2

Stratified analyses of AXIN2 148 C/T, 1365 C/T, and rs4791171 A/G variants on overall cancer risk

Variables

N

Case/control

OR (95% CI)

P h

P

OR (95% CI)

P h

P

OR (95% CI)

P h

P

OR (95% CI)

P h

P

M-allele vs. W-allele

MM vs. WW

MW vs. WW

MM+MW vs. WW

148 C/T

 Total

15

2909/2907

0.88 (0.77–0.99)

0.004

0.041

0.82 (0.63–1.06)

0.007

0.132

0.84 (0.75–0.95)

0.112

0.004

0.82 (0.69–0.96)

0.022

0.015

 Ethnicity

  Asian

10

2209/2226

0.85 (0.73–0.98)

0.016

0.027

0.76 (0.57–1.03)

0.040

0.078

0.84 (0.74–0.96)

0.074

0.009

0.80 (0.66–0.96)

0.030

0.020

  Caucasian

4

512/582

0.85 (0.72–1.01)

0.380

0.061

0.75 (0.53–1.07)

0.304

0.108

0.77 (0.58–1.00)

0.896

0.053

0.76 (0.59–0.98)

0.701

0.036

  Latin

1

188/99

1.48 (1.05–2.09)

0.026

2.64 (1.21–5.78)

0.015

1.63 (0.84–3.13)

0.146

1.86 (0.99–3.51)

0.054

 Cancer type

  LC

4

1061/1047

0.74 (0.65–0.84)

0.602

< 0.001

0.53 (0.40–0.70)

0.360

< 0.001

0.76 (0.63–0.92)

0.865

0.005

0.70 (0.59–0.84)

0.803

< 0.001

  CRC

3

411/387

1.10 (0.90–1.35)

0.071

0.348

1.36 (0.87–2.11)

0.096

0.178

0.96 (0.68–1.34)

0.123

0.796

1.01 (0.74–1.39)

0.060

0.932

  PC

2

187/200

0.83 (0.62–1.12)

0.099

0.223

1.00 (0.54–1.87)

0.509

0.987

0.54 (0.35–0.84)

0.088

0.006

0.62 (0.41–0.93)

0.078

0.022

  Others

7

1250/1273

0.98 (0.87–1.11)

0.217

0.751

0.98 (0.76–1.26)

0.363

0.862

0.96 (0.80–1.15)

0.375

0.640

0.96 (0.81–1.14)

0.218

0.664

 Source

  HB

8

1684/1748

0.94 (0.85–1.04)

0.093

0.219

0.89 (0.72–1.09)

0.128

0.267

0.93 (0.80–1.09)

0.564

0.364

0.92 (0.80–1.07)

0.268

0.272

  PB

7

1225/1159

0.82 (0.65–1.02)

0.009

0.074

0.73 (0.44–1.22)

0.007

0.235

0.74 (0.62–0.88)

0.083

0.001

0.74 (0.56–0.97)

0.037

0.032

1365 C/T

 Total

4

587/605

0.71 (0.61–0.98)

0.873

0.038

1.11 (0.34–3.70)

0.859

0.63 (0.44–0.91)

0.668

0.014

0.66 (0.47–0.94)

0.775

0.021

 Ethnicity

  Asian

1

303/305

0.66 (0.43–0.99)

0.043

1.11 (0.34–3.70)

0.859

0.53 (0.32–0.86)

0.010

0.58 (0.37–0.92)

0.020

  Caucasian

3

284/300

0.81 (0.47–1.38)

0.855

0.440

NA

  

0.80 (0.46–1.39)

0.846

0.428

0.80 (0.46–1.39)

0.846

0.428

 Cancer type

  LC

2

403/405

0.67 (0.46–0.97)

0.806

0.034

1.11 (0.34–3.70)

0.859

0.57 (0.37–0.87)

0.548

0.009

0.61 (0.41–0.91)

0.669

0.016

  PC

1

84/100

1.04 (0.37–2.94)

0.936

NA

  

1.05 (0.36–3.01)

0.934

1.05 (0.36–3.01)

0.934

  AT

1

100/100

0.74 (0.30–1.79)

0.503

NA

  

0.73 (0.29–1.81)

0.490

0.73 (0.29–1.81)

0.490

rs4791171 A/G

 Total

3

785/443

0.99 (0.85–1.17)

0.786

0.864

0.94 (0.66–1.33)

0.873

0.728

0.86 (0.62–1.17)

0.522

0.322

0.89 (0.66–1.19)

0.575

0.429

 Ethnicity

  Asian

1

564/250

1.00 (0.80–1.24)

0.997

0.93 (0.69–1.48)

0.773

0.84 (0.63–1.31)

0.434

0.88 (0.68–1.34)

0.556

  Arabian

2

221/193

0.96 (0.73–1.27)

0.511

0.778

0.96 (0.66–1.62)

0.603

0.843

0.87 (0.56–1.36)

0.257

0.538

0.89 (0.69–1.36)

0.294

0.596

 Cancer type

  BC

1

99/83

0.87 (0.57–1.31)

0.497

0.80 (0.35–1.84)

0.599

0.65 (0.33–1.28)

0.209

0.69 (0.36–1.31)

0.255

  GBC

1

564/250

1.00 (0.80–1.24)

0.997

0.93 (0.59–1.48)

0.773

0.84 (0.53–1.31)

0.434

0.88 (0.58–1.34)

0.556

  CRC

1

122/110

1.04 (0.72–1.51)

0.822

1.07 (0.53–2.17)

0.854

1.09 (0.60–1.96)

0.778

1.08 (0.63–1.87)

0.777

AT astrocytoma, BC breast cancer, CRC colorectal cancer, HB hospital-based, PB population-based, NA not available, LC lung adenocarcinoma, PC prostate adenocarcinoma, GBC gallbladder cancer

aP value of Q-test for heterogeneity test (Pheter)

Fig. 2
Fig. 2

Forest plot of cancer susceptibility correlated with AXIN2 148 C/T variant (heterozygote comparison of TC vs. CC, fixed-effects) in the stratified analyses by ethnicity

Fig. 3
Fig. 3

Forest plot of TC versus CC genetic model of AXIN2 148 C/T polymorphism in the stratified analyses by cancer type (fixed-effects)

In silico analysis of AXIN2 expression

Results from in silico tools suggested that AXIN2 expression in normal group was higher than that in lung adenocarcinoma tissue (Fig. 4a). However, no obvious difference was indicated for prostate adenocarcinoma (Fig. 4b). Moreover, we explored whether the AXIN2 expression had an effect on the overall survival time of lung adenocarcinoma patients. However, Kaplan–Meier estimate showed no vital difference of overall survival time between high and low AXIN2 expression groups (P = 0.40, Fig. 5).
Fig. 4
Fig. 4

In silico analysis of AXIN2 expressions in lung adenocarcinoma (a) and prostate adenocarcinoma (b)

Fig. 5
Fig. 5

Association of AXIN2 expression and the overall survival (OS) time among lung adenocarcinoma participants. Expression of AXIN2 was decreased in lung adenocarcinoma tissue (a). However, no vital influence of overall survival time was indicated between high and low AXIN2 expression groups (b, P > 0.05)

Publication bias and sensitivity analyses

Egger’s test and Begg’s funnel plot were utilized to evaluate publication bias in all of enrolled studies. We demonstrated no publication bias for AXIN2 148 C/T polymorphism (allelic contrast, t = − 0.52, P = 0.614; TT vs. CC, t = − 0.66, P = 0.519; heterozygote comparison, t = − 0.30, P = 0.771; TT + TC vs. CC, t = − 0.34, P = 0.741), AXIN2 1365 C/T variant (allelic comparison, t = 2.20, P = 0.159; TC vs. CC, t = 2.18, P = 0.161) and rs4791171 A/G polymorphism (G-allele versus A-allele, t = − 0.55, P = 0.680; homozygote contrast, t = − 0.62, P = 0.645; GA vs. AA, t = − 0.72, P = 0.602; dominant model, t = − 0.78, P = 0.577). As shown in Fig. 6, results from funnel plots appeared symmetrical in the overall analysis under dominant model, which indicated a lack of publication bias. Sensitivity analyses were also utilized to assess the pooled OR by omission of any one study. The results suggested that the current data from pooled ORs were relatively stable. No single study can substantially change the overall OR (Fig. 7).
Fig. 6
Fig. 6

Begg’s funnel plot of publication bias for AXIN2 148 C/T (a), 1365 C/T (b), and rs4791171 A/G (c) under dominant model

Fig. 7
Fig. 7

Sensitivity analyses about AXIN2 148 C/T, 1365 C/T, and rs4791171 A/G variants and cancer risk (Dominant genetic model of MM + MW vs. WW). Leave-one-out sensitivity analyses were carried out to assess the stability of the overall results. No single study can substantially change the overall OR for AXIN2 148 C/T (a), 1365 C/T (b), and rs4791171 A/G (c) polymorphisms

Discussion

To date, large quantities of studies have been conducted to explore whether the variants confer individual’s susceptibility to carcinoma. However, results from the previous publications have yielded controversial results [21, 22]. A previous study based on Indian descendants found a strong protective effect in participants having heterozygous genotype for 1365 C/T variant [30], while another study group did not observe such positive correlation in Turkish population [27]. In 2005, Wu et al. performed a meta-analysis and found that AXIN2 rs2240308 variant may increase the risk of cancer, especially lung cancer in Asian descendants [40]. Two years later, another meta-analysis indicated no obvious correlation between this variant and cancer risk in the overall analysis. Moreover, researches of this article observed that rs2240308 polymorphism was significantly associated with a decreased cancer risk in Asian population [41]. The overall goal of the present study was to evaluate all eligible data based on the inclusion criteria to enhance the statistical powers and draw more accurate conclusions.

In the current study, a total of 4281 cases and 3955 control participants were investigated. The overall results showed evidence that AXIN2 148 C/T variant was associated with decreased cancer risk, especially for lung and prostate adenocarcinoma, which is in line with conclusions identified by Kanzaki et al. Liu et al. and Gune et al. [19, 26, 28]. Similar results were observed in AXIN2 1365 C/T polymorphism (under allelic contrast, heterozygote comparison, and dominant genetic model). Moreover, in subgroup analysis by ethnicity, positive findings were obtained for Asian and Caucasian populations. In the stratified analysis by source of control, similar findings were identified in population-based studies for AXIN2 148 C/T variant, which is consistent with the findings reported by Yu et al. [41]. Moreover, results from in silico tools showed that AXIN2 expressions in lung cancer and prostate cancer are lower than that in normal counterpart. High expression of AXIN2 may have longer OS time than low expression group for lung cancer participants, which were consistent with results derived from the present meta-analysis. Nevertheless, we indicated no significant difference between the high expression and low/medium expression of AXIN2 in prostate cancer patients.

Some limitations of the above analysis should be mentioned. Firstly, the numbers of enrolled articles in the current analysis were still not large enough for the comprehensive analysis, especially for AXIN2 1365 C/T and rs4791171 A/G variants. Four articles towards AXIN2 1365 C/T and three articles for rs4791171 A/G polymorphism were eligible based on the selection criteria. Secondly, insufficient original data from the raw articles limited further evaluation of potential interactions, including relationship between the AXIN2 148 C/T, 1365 C/T, and rs4791171 A/G variants and different tumor grade and stage. Thirdly, meta-analysis was based on unadjusted estimates, which may lead to serious confounding bias. Furthermore, gene–gene interaction would also participate in etiological mechanism of carcinoma. As shown in Fig. 8, at least 20 related genes may be involved in such interaction, which are required to be further investigated in future studies. On the other hand, core advantages in current analysis should also be acknowledged. Firstly, a comprehensive study of the correlation of the AXIN2 148 C/T, 1365 C/T, and rs4791171 A/G variants with overall cancer susceptibility is statistically more powerful than single case–control study. All the studies according to the inclusion criteria were accumulated in our analysis. Secondly, genotype distribution of controls is conformed to Hardy–Weinberg equilibrium (HWE) in any of the enrolled studies and no significant publication bias was found, which indicated that conclusions of the present analysis are relatively trustworthy.
Fig. 8
Fig. 8

AXIN2 correlations crosstalk with other genes determined by String server (Homo sapiens). 20 related genes could participate in the gene–gene interaction

Conclusions

Taken together, the current study showed evidence that AXIN2 148 C/T and 1365 C/T variants may be associated with decreased cancer susceptibility, especially for lung and prostate adenocarcinoma. Future large scale studies with standardized unbiased cases and well-matched control subjects are needed to ascertain these finding in more detail.

Notes

Abbreviations

AT: 

astrocytoma

BC: 

breast cancer

CRC: 

colorectal cancer

GBC: 

gallbladder cancer

HB: 

hospital-based

PB: 

population-based

PCR-RFLP: 

polymerase chain reaction and restrictive fragment length polymorphism

RT: 

real time

NA: 

not available

NOS: 

Newcastle–Ottawa Scale

HCC: 

hepatocellular carcinoma

HNC: 

head and neck cancer

HWE: 

Hardy–Weinberg equilibrium of controls

LC: 

lung adenocarcinoma

PC: 

prostate adenocarcinoma

PTC: 

papillary thyroid carcinoma

OC: 

ovarian cancer

Declarations

Authors’ contributions

BX and WZ contributed to the design of the study, WY and QW searched the databases, LS, XYW and BX extracted the data, LS and LZ wrote the manuscript, LZ and QW interpreted the results and revised the manuscript. All authors read and approved the final manuscript.

Acknowledgements

Not applicable.

Competing interests

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. We also declare that there was no non-financial competing interests in the manuscript.

Availability of data and materials

All data generated and analyzed during this study are included in this published article. Please contact author for data requests.

Consent for publication

Not applicable.

Ethics approval and consent to participate

Not applicable.

Funding

Not applicable.

Publisher’s Note

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Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Department of Oncology, Affiliated Hospital of Jiangnan University, Wuxi, 214000, Jiangsu, China
(2)
Department of Cardiology, Taizhou People’s Hospital, Taizhou, 225300, Jiangsu, China
(3)
Department of Urology, The Affiliated Changzhou No. 2 People’s Hospital of Nanjing Medical University, Changzhou, 213003, Jiangsu, China
(4)
Department of Oncology, Taizhou People’s Hospital, 210 Yingchun Road, Taizhou, 225300, Jiangsu, China

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