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Cancer Cell International

, 18:155 | Cite as

Single nucleotide polymorphisms and sporadic colorectal cancer susceptibility: a field synopsis and meta-analysis

  • Jing Wen
  • Qian Xu
  • Yuan Yuan
Open Access
Primary research

Abstract

Background

Although mounting non-hereditary colorectal cancer (NHCRC) associated single nucleotide polymorphisms (SNPs) have been observed, no field synopsis and meta-analysis has been conducted through systematically assessing cumulative evidence, during the past 5 years.

Methods

We retrieved the database via the PubMed, Web of Science and Embase gateways to identify publications concerning the associations between SNPs and risk of NHCRC, up to May 1st, 2017. To assess the finding credibility, cumulative evidence was graded based on the Venice criteria. Meta-analysis was also performed for three subgroups including ethnicity (Asian vs Caucasian), primary cancer site (colon vs rectum) and TNM stage (I II vs III IV). Then, we arranged those high quality SNPs into different regions according to their locations on genes to evaluate their functional roles on CRC development.

Results

5114 publications were collected and 1001 of them met our inclusion criteria, which totally included 1788 SNPs in 793 genes or distinct chromosomal loci. Totally, we performed 359 primary and subgroup meta-analyses for 160 SNPs in 96 distinct genes. By utilizing the Venice criteria, we identified 15 high quality SNPs with 25 high credibility significant associations. Furthermore, we artificially divided the high quality SNPs into different groups, based on their SNP loci (exon region, intron region, promoter region, downstream region, non-coding region and intergenic region).

Conclusion

We have identified 15 high quality SNPs which may act as promising genetic biomarkers for clinical NHCRC susceptibility screening and explored their functional roles on the NHCRC development based on their locations on genes.

Keywords

Non-hereditary colorectal cancer Single nucleotide polymorphisms Field synopsis Meta-analysis 

Abbreviations

CRC

colorectal cancer

HCRC

hereditary CRC

NHCRC

non-hereditary CRC

SNP

single nucleotide polymorphism

GWAS

genome-wide association studies

CGAS

candidate-gene association studies

VNTR

variable number of tandem repeat

ORs

odds ratios

CIs

confidence intervals

HWE

Hardy–Weinberg equilibrium

CIs

confidence intervals

FDR

false discovery rate

Background

Colorectal cancer (CRC) is the third most frequent cancer and the fourth major cause of cancer death worldwide [1]. Genetic factors play an important role in the carcinogenesis of CRC. Traditionally, CRC can be divided into familial CRC (hereditary CRC, HCRC) and sporadic CRC (non-hereditary CRC, NHCRC). HCRC only accounts for 20–25% of all CRC and is mainly attributed to precise high-penetrance mutations [2]. The overwhelming majority of CRC is NHCRC that can be caused by some genetic defects like single nucleotide polymorphism rather than any exact genetic mutation. Understanding of genetic variation is beneficial to strengthen the precaution, screening and early diagnosis of CRC, which is not only for HCRC but also for NHCRC. In a sense, the prediction and control of NHCRC is more expected than HCRC because it occupies the majority of CRC, and the control measures may be feasible and operable.

Single nucleotide polymorphism (SNP) is a common genetic variation, which may result in different functional products, thus affecting individual susceptibility to diseases. Hence, SNP can be considered as biomarker to predict the risk of sporadic tumor including CRC. During the past three decades, numerous SNPs have been illustrated to be correlated with CRC risk by extensive genome-wide association studies (GWAS) and also candidate-gene association studies (CGAS). Different data based Meta analyses from different angles also reported in the genetic predisposition to NHCRC. Making a general observation of preceding meta-analyses, most of them gathered only a fraction of SNPs and few noticed complete picture of SNPs in NHCRC from a field perspective. It’s worth noting that, there have existed two comprehensive field meta-analyses which demonstrated all CRC risk associated variants, up to 2012, providing directions for future investigators [3, 4]. Inspired by these two articles, we noticed that SNP plays an essential role in the genetic predisposition of CRC, constituting nearly 80% of he significant genetic variants which also include the insertion/deletion polymorphism and variable number of tandem repeat (VNTR). For SNP only, a renewed field synopsis and meta-analysis is required on account of the past 5 years since the latest field synopsis published, and the heterogeneity from ethnicity, primary cancer site and TNM stage must be considered. What’s more, no studies mentioned the role of the whole associated SNPs on CRC development, based on their locations on genes.

In the present systematic review and meta-analysis, we focus on the high quality SNPs (which mean the SNPs are statistically associated with CRC risk in high credibility level, assessed by Venice criteria) in the field of genetic predisposition to NHCRC, involving the correlations of SNPs with ethnicity, primary cancer site (colon or rectal) and TNM stage (I II or III IV). Then, we arranged those high quality SNPs into different regions according to their locations on genes to evaluate their functional roles on CRC development.

Materials and methods

Retrieval strategy

A comprehensive systematic literature search was performed for the publications concerning the association between SNP and risk of NHCRC. We retrieved the database via the PubMed, Web of Science and Embase gateway by using the search terms “(polymorphism or “single nucleotide polymorphism” or SNP or “genome wide association study” or GWAS) and (colon or rectal or rectum or colorectal) and (cancer or tumor or carcinoma or neoplasm)”, up to May 1st, 2017. Moreover, each identified SNP was adopted as a keyword to further improve the search, for instance, ‘XPG’ or ‘rs17655’ in combination with “(colon or rectal or rectum or colorectal) and (cancer or tumor or carcinoma or neoplasm)” as query term.

Inclusion and exclusion criteria

To identify all eligible studies, we adopted the following inclusion criteria: (1) case–control study either candidate-gene association studies (CGAS) or genome-wide association studies (GWAS); (2) explored the correlation between SNP and NHCRC. In addition, the main exclusion criteria were: (1) overlapping studies; (2) no relation to NHCRC or nothing concerning SNPs; (3) no available data or inadaptable SNP genotyping methods; (4) any research published in abstraction form solely (e.g. conference proceedings or scientific meetings) (Fig. 1).
Fig. 1

Flow diagram for selecting eligible studies

Data extraction

Data were independently extracted by two of the authors (Jing Wen and Qian Xu). Items collected from all eligible publications included first author, publication year (unpublished data show study year), race of participants, sample size, genes, SNP locus, genotype counts of cases and controls and HWE in controls. Multiple populations comprising one publication were extracted individually. Concerning GWAS, discovery and replication studies were regarded as separate datasets and were also extracted individually. When it came to eligible articles along with unreported data, we made efforts to contact with the authors.

Assessment of cumulative evidence

The epidemiology credibility of all seemingly significant associations confirmed by our meta-analysis were taken into account by applying Venice criteria [5, 6]. Three categories considered as fundamental criteria to defined the credibility level are as follows:
  1. 1.

    Amount of evidence was evaluated by the total number of both cases and controls expressing the test alleles or genotypes: category ‘A’, ‘B’, ‘C’ represent for large-scale, moderate, or little respectively with over 1000, 100–1000 and less than 100 sample size.

     
  2. 2.

    Replication was classed based on the statistic of heterogeneity: category ‘A’, ‘B’, ‘C’ respectively stand for little inconsistency, moderate inconsistency or large inconsistency (no association) with I2 < 25%, 25–50% and > 50%.

     
  3. 3.

    Protection from bias was classed as ‘A’ with no bias which was improbably to explain the positive result of association, ‘B’ with no obvious bias but could be the reason for the association, or ‘C’ with demonstrable bias. The general checks for bias include: association lost with removal of initial study; small intensity of association (0.87 < OR < 1.15) and existence of publication bias [7, 8].

     

According to criteria mentioned above, the accumulative evidence of associations calculated by meta-analysis were regarded as high credibility level (three grades ‘A’), intermediate credibility level (either ‘A’ or ‘B’), and low credibility level (if any grades ‘C’). Notably, the heterogeneity and bias could be exempted if the P value < 1×10−7 after removing the initial study [8].

Statistics

Statistical analyses in our study were conducted by STATA software, version 11.0 (STATA Corp., College Station, TX, USA). All tests were two-tailed and P values ≤ 0.05 were regarded as the statistical significance level only if we emphasized once more. And it would reach a genome-wide significance level if P < 5 × 10−8 [9]. The Hardy–Weinberg equilibrium (HWE) among genotype distributions of controls was assessed by Chi square test and P values < 0.05 were regarded as statistically significant disequilibrium. Appraisals of the association between the SNPs and colorectal cancer risk were assessed by pooled odds ratios (ORs) and 95% confidence intervals (CIs) calculated by random effect models when heterogeneity of between-study exists [10], otherwise fixed effect model [11]. Begg’s test, as a funnel plot analyses, was implemented to verify significant asymmetry [12] and the modified Egger’s test owns the capacity to correct type I errors through evaluating bias caused by small studies [13]. P value less than 0.10 was regarded as the threshold in both Begg’s or Egger’s test.

In addition, q value was considered as a measure for statistically significant findings in terms of false discovery rate (FDR), which is the proportion that significant findings are truly null hypotheses. For instance, 5% false discovery rate means that among all statistically significant SNPs, 5% of them are not actually associated with CRC risk. And we also considered 0.05 as the threshold of q value [14, 15].

Results

Features of eligible studies

According to the screening process showed in Fig. 1, 5114 publications were collected and 1001 of them met our inclusion criteria, which totally included 1788 SNPs in 793 genes or distinct chromosomal loci with 2,200,290 subjects extracted (cases: 971,074, ratio: 44%, range: 8–10,409, mean: 550).

Based on the ethnicity of study population, investigations for Caucasian (57%) were slightly more than those for Asian. Besides, over a quarter of the available articles detailed the primary site (colon vs rectum) of colorectal cancer, and the articles that mentioned TNM stage of UICC/AJCC also account for 13%. Additionally, nearly a half of the investigated SNPs were exonic SNPs (45%), others were located in intron (20%), 3′-UTR (4%), 5′-UTR (1%), upstream (14%), downstream regions (2%) non-coding (7%) or intergenic regions (6%).

Meta analysis findings

Totally, we performed 359 meta-analyses for 160 SNPs in 96 distinct genes. Each meta-analysis involved at least three studies (CGAS or GWAS) with available co-dominant genotypes and HWE. Of these, 160 were primary meta-analyses and 199 were subgroups meta-analyses defined by ethnicity (Caucasian, n = 90; Asian, n = 53), primary cancer site (colon, n = 22; rectum, n = 14) and TNM stage (I II, n = 10; III IV, n = 10). Of the 359 meta-analyses conducted, 90 (25%) attained statistically significant findings, other 269 being non-significant. Furthermore, 40.3% (n = 145) had little or no heterogeneity (I2 < 25%), 14.2% (n = 51) had moderate heterogeneity (25% ≤ I2 ≤ 50%), and 45.5% (n = 164) had large heterogeneity (I2 > 50%). Comparing the proportion of large study heterogeneity, we found that it was significantly lower for 90 positive SNPs than the remaining SNPs (19.8% and 46.5%, respectively; P value = 6.88E−6). Evaluation of publication bias conducted for all meta-analyses showed that totally 52 of them had statistically significant publication bias, 15 for ethnicity subgroup, 4 for cancer sites subgroup and 2 for TNM stage subgroup. In sensitivity analyses, eight SNPs was no longer significant after removing one record from meta-analysis and five of them showed more than 5% alteration of OR value (rs3918242, rs1048943, rs5498, rs4444903, rs10808556).

Comprehensively considering the impact of the evidence amount, replication consistency (heterogeneity), and protection from bias (derived from publication bias, initial study influence and OR value) on the cumulative evidence, we applied the Venice criteria that could assess the epidemiological credibility for all significant findings. Thus, the high, intermediate and low credibility level of cumulative evidence were detected, which respectively account for 28% (n = 25), 16% (n = 14), 56% (n = 51). Publication bias was the most common cause (41/65) for non-high-quality evidence, and the inter-study heterogeneity could be the second (33/65). From the 25 high credibility significant associations, we identified 15 distinct high quality SNPs which were presented in Fig. 5.

Results from whole population analysis

Significant associations in primary meta-analysis are reported in Table 1, characterized by high (n = 10), intermediate (n = 3) or low quality (n = 23). Seven of the ten high quality SNPs reached a genome-wide significance level, P < 5 × 10−8 (BMP2 rs961253, CASC8 rs1505477, BMP4 rs4444235, SMAD7 rs12953717, CCAT2 rs6983267, TGF-β1 rs1800469, LOC105376400 rs10795668 and GREM1-SCG5 rs4779584). Other three high quality SNPs were: GREM1-SCG5 rs4779584, ADIPOQ rs2241766 and miR-27a rs895819.
Table 1

Genetic variants associated with NHCRC after meta-analyses of at least three independent datasets (primary meta-analyses)

Gene

SNPs

Chr

Allelesa

Datasets

Cases

Controls

OR

CI-U

CI-L

P

FDR

Venice criteria

Level of evidence

BMP2

rs961253

20

C-A

23

40507

40740

1.121

1.098

1.144

7.77E−27

7.15E−25

AAA

High

CASC8

rs10505477

8

T-C

13

12933

13259

0.856

0.828

0.886

9.27E−19

2.84E−17

AAA

High

BMP4

rs4444235

14

T-C

27

39030

39934

1.083

1.061

1.105

8.24E−15

1.89E−13

AAA

High

SMAD7

rs12953717

18

C-T

9

10782

10011

1.163

1.118

1.209

4.93E−14

8.00E−13

AAA

High

CCAT2

rs6983267

8

G-T

26

33098

30415

0.845

0.807

0.884

3.35E−13

3.86E−12

AAA

High

TGF-β1

rs1800469

19

C-T

21

17404

36234

0.911

0.887

0.936

1.59E−11

1.62E−10

AAA

High

LOC105376400

rs10795668

10

G-A

8

5744

5481

0.837

0.791

0.886

8.43E−10

5.27E−09

AAA

High

GREM1-SCG5

rs4779584

15

C-T

16

25151

24548

1.154

1.093

1.217

1.69E−07

8.62E−07

AAA

High

ADIPOQ

rs2241766

3

T-G

7

2400

2972

1.216

1.115

1.326

9.33E−06

3.07E−05

AAA

High

miR-27a

rs895819

19

T-C

5

1562

1852

1.192

1.076

1.320

0.001

0.002

AAA

High

ADIPOR1

rs1342387

1

G-A

5

1843

2563

0.824

0.755

0.900

1.82E−05

5.76E−05

ABA

Intermediate

ICAM1

rs5498 469

19

A-G

3

358

335

0.740

0.595

0.920

0.007

0.012

BAA

Intermediate

PARP-1

rs1136410

1

T-C

3

808

1849

1.181

1.043

1.337

0.009

0.015

ABA

Intermediate

RHPN2

rs10411210

19

C-T

15

22299

23280

0.881

0.844

0.919

6.12E−09

4.69E−08

ABC

Low

SMAD7

rs4464148

18

T-C

8

9736

8573

1.142

1.091

1.196

1.62E−08

1.15E−07

AAC

Low

SMAD7

rs4939827

18

T-C

17

16336

15443

0.859

0.813

0.908

5.94E−08

3.42E−07

ACA

Low

CDH1

rs9929218

16

G-A

17

22459

24079

0.930

0.903

0.958

1.51E−06

5.80E−06

AAC

Low

COLCA1

rs3802842

11

A-C

18

18043

17876

1.149

1.086

1.216

1.62E−06

5.97E−06

ACC

Low

NA

rs719725

9

A-C

14

11820

13119

0.934

0.900

0.968

2.17E−04

5.56E−04

AAC

Low

MLH1

rs63750447

3

T-A

6

1427

1491

2.449

1.426

4.205

0.001

0.002

CCC

Low

XPG

rs17655

13

G-C

8

4752

5648

1.101

1.036

1.170

0.002

0.004

AAC

Low

NAT2

rs1801280

8

T-C

3

2066

2581

0.876

0.807

0.952

0.002

0.004

AAC

Low

NA

rs11568820

12

G-A

5

4278

4693

1.112

1.036

1.194

0.003

0.006

AAC

Low

VDR

rs1544410

12

G-A

14

10404

11213

0.768

0.633

0.930

0.007

0.012

ACC

Low

CCND1

rs9344

11

G-A

21

4757

6680

1.119

1.026

1.220

0.011

0.017

ACC

Low

CDH1

rs16260

16

C-A

8

6062

7045

0.934

0.884

0.988

0.016

0.023

AAC

Low

EGF

rs4444903

4

G-A

8

1234

1377

0.819

0.692

0.969

0.020

0.027

ACC

Low

GREM1

rs16969681

15

C-T

6

7300

9039

1.157

1.022

1.311

0.021

0.028

ACA

Low

MTRR

rs1801394

5

A-G

19

8409

11893

1.046

1.004

1.089

0.030

0.036

ABC

Low

NQO1

rs1800566

16

C-T

13

6016

6905

1.142

1.013

1.288

0.030

0.036

ACC

Low

MMP9

rs3918242

20

C-T

5

829

1096

0.803

0.657

0.980

0.031

0.037

BAC

Low

ERCC1

rs11615

19

C-T

5

982

1251

1.147

1.011

1.302

0.033

0.037

AAC

Low

APC

rs459552

5

A-T

13

9440

10200

0.950

0.905

0.997

0.037

0.041

AAC

Low

CYP1A1

rs1048943

15

A-G

13

3509

3960

1.287

1.011

1.637

0.040

0.043

ACA

Low

GH1

rs2665802

17

T-A

3

2740

3198

0.929

0.863

0.999

0.046

0.047

AAC

Low

RETN

rs1862513

19

C-G

3

1013

1049

1.145

1.000

1.311

0.050

0.050

ABC

Low

Chr chromosome, FDR false discovery rate

aMajor alleles-minor alleles; Venice criteria: A (high), B (moderate), C (weak) credibility for three parameters (amount of evidence, heterogeneity and bias; see text and Additional file 1 for more details); level of evidence: overall level of summary evidence according to the Venice criteria

Results from subgroup analyses

Significant associations in subgroup analyses were shown in Table 2, featured with high (n = 15), intermediate (n = 11) or low quality (n = 28). Results from three stratification analyses (ethnicity, primary cancer site and TNM stage) were illustrated as follows.
Table 2

Genetic variants associated with NHCRC after meta-analyses of at least three independent datasets (subgroup meta-analyses)

Gene

SNPs

Chr

Allelesa

Datasets

Subgroups

Cases

Controls

OR

CI-U

CI-L

P

FDR

Venice criteria

Level of evidence

TGF-β1

rs1800469

19

C-T

16

Asian

14494

32550

0.911

0.885

0.938

2.43E−10

2.03E−09

AAA

High

LOC105376400

rs10795668

10

G-A

4

Asian

2479

2659

0.803

0.741

0.872

1.37E−07

7.41E−07

AAA

High

KRAS

rs712

12

G-T

3

Asian

1355

1219

1.405

1.226

1.610

9.99E−07

4.37E−06

AAA

High

ADIPOR1

rs1342387

1

G-A

3

Asian

1218

1724

0.779

0.697

0.870

9.22E−06

3.07E−05

AAA

High

ADIPOQ

rs2241766

3

T-G

5

Asian

2143

2666

1.215

1.111

1.328

6.03E−05

1.63E−04

AAA

High

miR-196a2

rs11614913

12

C-T

4

Asian

1397

2040

0.836

0.757

0.922

3.54E−04

8.79E−04

AAA

High

BMP2

rs961253

20

C-A

21

Caucasian

38172

38356

1.115

1.092

1.139

3.57E−24

1.64E−22

AAA

High

BMP4

rs4444235

14

T-C

22

Caucasian

34181

34254

1.086

1.063

1.110

5.22E−14

8.00E−13

AAA

High

SMAD7

rs12953717

18

C-T

8

Caucasian

10640

9845

1.161

1.116

1.208

1.16E−13

1.53E−12

AAA

High

GREM1-SCG5

rs4779584

15

C-T

12

Caucasian

21029

19867

1.16

1.092

1.232

1.45E−06

5.80E−06

AAA

High

LOC105376400

rs10795668

10

G-A

4

Caucasian

3265

2822

0.87

0.804

0.942

0.001

0.002

AAA

High

CCND1

rs9344

11

G-A

7

Rectum

775

2241

1.272

1.126

1.436

1.05E−04

2.77E−04

AAA

High

MTHFR

rs1801131

1

A-C

6

Rectum

1625

2987

0.858

0.775

0.949

0.003

0.006

AAA

High

LOC105376400

rs10795668

10

G-A

3

TNM12

766

2423

0.773

0.682

0.875

4.65E−05

1.38E−04

AAA

High

CCND1

rs9344

11

G-A

4

TNM12

343

740

1.366

1.134

1.646

0.001

0.002

AAA

High

ABCB1

rs1045642

7

C-T

5

Asian

1567

1857

0.866

0.786

0.955

0.004

0.007

ABA

Intermediate

CYP1A1

rs1048943

15

A-G

7

Caucasian

1779

1946

1.251

1.038

1.508

0.019

0.026

BAA

Intermediate

MDM2

rs2279744

12

T-G

4

Caucasian

1325

473

0.826

0.704

0.969

0.019

0.026

ABA

Intermediate

CCAT2

rs10090154

8

C-T

3

Caucasian

1084

1049

1.254

1.028

1.529

0.026

0.033

BAA

Intermediate

VDR

rs731236

12

T-C

3

Colon

677

578

1.291

1.06

1.573

0.011

0.017

BAA

Intermediate

PPARG

rs1801282

3

C-G

4

Colon

998

3368

0.772

0.619

0.963

0.022

0.029

BAA

Intermediate

TP53

rs1042522

17

G-C

5

TNM12

249

975

1.41

1.147

1.734

0.001

0.002

BAA

Intermediate

XRCC1

rs25487

19

G-A

3

TNM12

165

420

1.323

1.021

1.713

0.034

0.038

BBA

Intermediate

miR-196a2

rs11614913

12

C-T

3

TNM12

279

1249

0.823

0.682

0.994

0.043

0.045

ABA

Intermediate

MTHFR

rs1801133

1

C-T

4

TNM34

207

715

1.715

1.338

2.200

2.09E−05

6.39E−05

BAA

Intermediate

CCND1

rs9344

11

G-A

5

TNM34

340

860

1.254

1.046

1.503

0.014

0.021

ABA

Intermediate

CCAT2

rs6983267

8

G-T

8

Asian

11190

10699

0.846

0.791

0.906

1.43E−06

5.80E−06

ACA

Low

miR-27a

rs895819

19

T-C

3

Asian

1174

1212

1.237

1.096

1.395

0.001

0.002

AAC

Low

BMP4

rs4444235

14

T-C

4

Asian

4136

4765

1.089

1.026

1.156

0.005

0.009

ABC

Low

MMP9

rs3918242

20

C-T

4

Asian

702

888

0.754

0.605

0.939

0.012

0.018

BAC

Low

PTGS2

rs20417

1

G-C

3

Asian

1285

1740

1.422

1.08

1.872

0.012

0.018

BBC

Low

GREM1-SCG5

rs4779584

15

C-T

4

Asian

4122

4681

1.134

1.003

1.283

0.045

0.047

ACC

Low

C11orf92–C11orf93

rs3802842

11

A-C

11

Caucasian

12983

12139

1.139

1.095

1.184

6.32E−11

5.81E−10

ABC

Low

SMAD7

rs4464148

18

T-C

7

Caucasian

9020

7860

1.139

1.087

1.194

4.74E−08

3.11E−07

AAC

Low

CCAT2

rs6983267

8

G-T

17

Caucasian

21026

18580

0.85

0.798

0.904

2.97E−07

1.44E−06

ACA

Low

SMAD7

rs4939827

18

T-C

13

Caucasian

12657

11621

0.853

0.801

0.908

5.86E−07

2.69E−06

ACA

Low

CDH1

rs9929218

16

G-A

13

Caucasian

18291

19311

0.926

0.897

0.956

2.17E−06

7.67E−06

AAC

Low

RHPN2

rs10411210

19

C-T

12

Caucasian

19240

20177

0.859

0.799

0.925

5.25E−05

1.51E−04

ACA

Low

TERT

rs2736100

5

G-T

8

Caucasian

14486

15588

1.072

1.036

1.109

5.83E−05

1.62E−04

AAC

Low

NA

rs719725

9

A-C

12

Caucasian

10035

10281

0.935

0.898

0.973

0.001

0.002

AAC

Low

NQO1

rs1800566

16

C-T

9

Caucasian

4496

4794

1.119

1.039

1.206

0.003

0.006

AAC

Low

CDH1

rs16260

16

C-A

5

Caucasian

5603

6646

0.927

0.876

0.981

0.008

0.013

AAC

Low

TGF-β1

rs1800469

19

C-T

5

Caucasian

2910

3684

0.915

0.845

0.987

0.021

0.028

ABC

Low

MTR

rs1805087

1

A-G

10

Caucasian

6884

8873

0.936

0.884

0.991

0.023

0.029

ABC

Low

NA

rs11568820

12

G-A

4

Caucasian

2701

2721

1.107

1.011

1.213

0.028

0.035

AAC

Low

RFC/SLC19A1

rs1051266

21

A-G

5

Caucasian

1922

3142

0.915

0.843

0.992

0.032

0.037

AAC

Low

GREM1

rs16969681

15

C-T

5

Caucasian

6411

8146

1.175

1.003

1.378

0.046

0.047

ACA

Low

CCAT2

rs6983267

8

G-T

5

Colon

1593

2439

0.823

0.706

0.959

0.012

0.018

ACA

Low

CCND1

rs9344

11

G-A

8

Colon

877

2394

1.147

1.024

1.284

0.018

0.025

AAC

Low

MSH6

rs1042821

2

C-T

3

Colon

3448

5447

1.091

1.008

1.181

0.032

0.037

AAC

Low

SMAD7

rs4939827

18

T-C

4

colon

2259

2809

0.92

0.85

0.995

0.037

0.041

ABC

Low

ABCB1

rs1045642

7

C-T

5

Colon

469

635

1.198

1.009

1.423

0.039

0.042

AAC

Low

CCAT2

rs6983267

8

G-T

4

Rectum

652

1718

0.756

0.599

0.953

0.018

0.025

ACA

Low

CCAT2

rs6983267

8

G-T

4

TNM34

747

2185

0.834

0.739

0.941

0.003

0.006

AAC

Low

Chr chromosome, FDR false discovery rate

aMajor alleles-minor alleles; Venice criteria: A (high), B (moderate), C (weak) credibility for three parameters (amount of evidence, heterogeneity and bias; see text and Additional file 1 for more details); level of evidence: overall level of summary evidence according to the Venice criteria

Results based-on ethnicity
Disparate race were mentioned in 35 significant associations (see Fig. 2). 21 (60%) of them were identified in Caucasian only (18 from Caucasian subgroup meta-analyses and 3 from primary meta-analyses which included only Caucasian ancestry), 9 (25.7%) of them were indicated in Asian only (8 from Asian subgroup meta-analyses and 1 from primary meta-analyses which only covered Asian ancestry), and 5 (14.3%) SNPs (all from subgroup meta-analyses) obtained their correlations in both Caucasian and Asian.
Fig. 2

Venn diagram demonstrating the distribution of SNPs associated with colorectal cancer in subjects with Asian or Caucasian ethnicity or associated with both ancestries

Totally 11 high quality SNPs were found in ethnicity subgroup analyses, 6 from Asian subgroup (TGF-β1 rs1800469, LOC105376400 rs10795668, KRAS rs712, ADIPOR1 rs1342387, ADIPOQ rs2241766 and miR-196a2 rs11614913) and 5 from Caucasian subgroup (BMP2 rs961253, BMP4 rs4444235, SMAD7 rs12953717, GREM1-SCG5 rs4779584 and LOC105376400 rs10795668).

Results based-on primary cancer site

Different cancer sites were mentioned in 8 significant SNPs (see Fig. 3). 5 (62.5%) of them showed their unique associations with colon cancer in subgroup analyses, 1 (12.5%) showed a sole association with rectum cancer in subgroup analysis, and 2 (25%) revealed their correlations with either colon or rectum cancer. Two high quality SNPs were found in rectum subgroup (CCND1 rs9344 and MTHFR rs1801131).
Fig. 3

Venn diagram demonstrating the distribution of SNPs associated with colon or rectal cancer or associated with both primary cancer sites

Results based-on cancer TNM stage

Subgroup meta-analyses of TNM stage demonstrated 7 SNPs with significant correlations (see Fig. 4). 4 (57.1%) of them simply correlated with TNM stage (I II), 2 (28.6%) of them related to TNM stage (III IV), and only 1 (14.3%) SNP showed it’s correlation with any TNM stage of CRC. Among the 7 significant SNPs, only 2 high quality SNPs (LOC105376400 rs10795668 and CCND1 rs9344) were identified.
Fig. 4

Venn diagram demonstrating the distribution of SNPs associated with specific coloretal cancer TNM stage (I II vs III IV) or associated with full stage

Results based-on SNP location

From 25 high credibility significant associations, 15 distinct high quality SNPs were identified. In order to further explore the role of these high quality SNPs, we artificially divided them into different groups, based on their SNP loci (exon region, intron region, promoter region, downstream region, non-coding region and intergenic region), which was displayed in Fig. 5. What’s more, we also revealed the chromosome distribution of each high quality SNPs.
Fig. 5

We artificially divided the 15 distinct high quality SNPs into six groups, based on their SNP loci on genes

Of the 15 high quality SNPs, 3 are located in exon region (2 synonymous variants: ADIPOQ rs2241766 and CCND1 rs9344; one missense variant: MTHFR rs1801131), 2 in intron region (SMAD7 rs12953717 and ADIPOR1 rs1342387), one in promoter region (TGF-β rs1800469 SNP), one in 3′-UTR region (KRAS rs712) and 5 in non-coding region (CASC8 rs10505477, CCAT2 rs6983267, LOC105376400 rs10795668, miR-27a rs895819, miR-196a2 rs11614913). Distinct from those functional SNPs, there are still 3 high quality SNPs located in intergenic region (BMP2 rs961253, BMP4 rs4444235 and GREM1-SCG5 rs4779584).

The chromosomes distribution of each high quality SNPs were also displayed. In general, the SNPs are evenly and extensively distributed in half of the chromosomes including chromosome 1, 3, 8, 10, 11, 12, 14, 15, 18, 19, 20.

Discussion

In this article, we systematically reviewed the associations between 160 SNPs in 96 distinct genes or chromosomal loci and predisposition to NHCRC or to subgroups identified by ethnicity (Asian vs Caucasian), primary cancer site (colon vs rectum), TNM stage (I II vs III IV) and SNP locations on genes, with the quality assessment of cumulative evidence, and 15 high quality SNPs were ultimately confirmed. Above all, innovations and strengths of the present study ought to be addressed. First, a most comprehensive evaluation of the literature in the field of genetic predisposition to NHCRC was conducted. Second, we first reported 20 SNPs in primary meta-analysis, 24 SNPs in “primary cancer site” subgroup analysis (15 for colon, 9 for rectum) and 10 SNPs in “TNM stage” subgroup analysis. Third, for exploring the functional roles of high quality SNPs on the NHCRC development, we first divided them into six different groups, based on SNP loci on genes. This study provides the latest evidence and clues for the genetic susceptibility to NHCRC. In spite of these strengths, limitations cannot be ignored. First, we only considered allelic genetic model because it was widely regarded as a conservative model between the dominant and recessive model [16]. Second, type I error might exist by utilizing same series in more than one meta-analysis. However, after calculating q values, the incidence of type I error could be minimized. Third, we didn’t analyze gene–gene or gene–environment interactions due to the insufficiency data. Future specialized studies should be designed to reveal their interactions.

High quality SNPs with NHCRC risk

Facing the excessive SNPs with significant associations, it’s crucial to conduct a quality evaluation scientifically to those significant correlations. By utilizing the Venice criteria, we identified 15 high quality SNPs with 25 high credibility significant associations, which may act as promising genetic biomarkers for clinical NHCRC susceptibility screening.

For the whole population, 10 high quality SNPs were evaluated and shown in Table 1. Comparing our results with two published field meta-analyses [3, 4], we found that 8 of the 10 SNPs were assessed as high quality SNPs for the first time, which meant they were used to be non-high-quality SNPs (with intermediate or low credibility level evidence), or even unreported in the past. Interestingly, by observing the gene functions of these high quality SNPs, we noticed that half of them participated in TGF-β/Smad signaling pathway, including TGF-β, SMAD7, BMP2, BMP4 and GREM1. This discovery could indirectly verified the crucial role of TGF-β/Smad signaling pathway on CRC pathogenesis by regulating their target genes [17]. In addition, there were four other high quality SNPs in non-coding RNA (including 1 micro-RNA: miR-27a; 3 long non-coding RNA: CASC8, CCAT2 and LOC105376400), which revealed that the aberrant expression of non-coding RNA could also be tightly related to CRC diagnosis [18, 19, 20]. Moreover, there was also one high quality SNP in ADIPOQ (adiponectin) gene, reminding that the deficiency of adiponectin might be one of the fundamental risk factors for NHCRC [21, 22].

From the perspective of ethnicity, the apparent contrast between Caucasian and Asian population on the distribution of associated SNPs was presented in Fig. 2, which suggested that the molecular mechanism of CRC development couldn’t always be the same among different ethnicities. Of note, 6 high quality SNPs were evaluated in Asian subgroup, all of which were first identified as high quality SNPs for Asian population; while 5 high quality SNPs were evaluated in Caucasian subgroup, and 3 were newly identified for Caucasian population. Observing the gene functions of these SNPs, KRAS, an important oncogene, caught our attention. It participated in RAF/MEK/MAPK, ERK and AKT signal pathways, regulating the CRC cell proliferation and differentiation [23, 24].

From the aspect of primary cancer location, the different findings between colon and rectal cancer indicated that they not only differ in anatomic site, but also in molecular profile. A study illustrated that colon and rectal cancer differ in embryological origin, metastasis manner and mutational profile, requiring various neo-adjuvant treatment and surgical methods [25]. Nevertheless, none of the two publications have been concerned with the “primary cancer location” subgroup analyses. Herein, 2 high quality SNPs were demonstrated in rectal subgroup analysis. These results elucidated that the risk factors for rectum cancer development might be the aberrant expression of MTHFR (which leaded to abnormal folate metabolism [26, 27, 28]) or CCND1 (which could promote cell cycle G1/S transition [29, 30]).

From malignant level perspective, TNM stage subgroup was first analyzed in our study with a high positive rate (8/20, 40%) and the diversity between stage I II and III IV also exist. It illustrated that SNPs could not only predict the NHCRC development, but also remind the degree of malignancy, directing the physical test frequency for patient and the treatment for doctor. Based on the limited pathological parameters provided by researchers, only 20 SNPs were analyzed in this subgroup and 2 of them (LOC105376400 rs10795668 and CCND1 rs9344) were identified as high quality SNPs in TNM stage (I II). Further studies should pay more attention to the association between polymorphisms and NHCRC malignancy degree.

Functional roles of high quality SNPs based on location

SNPs can influence the CRC susceptibility through complicated genetic and epigenetic mechanisms which depends on the their gene functions and their locations on genes. Hence, we arranged 15 high quality SNPs into different regions (including exon, intron, promoter, non-coding and also intergenic region) to focus on their feasible mechanisms on facilitating NHCRC development.

In exon region, the missense SNP (MTHFR rs1801131) make its contribution to the NHCRC by reducing the activity of enzyme [31, 32]. Besides, the prime mechanisms for synonymous SNPs are their influence on mRNA expression level by altering splicing or stability of mRNA (such as ADIPOQ rs2241766 and CCND1 rs9344) [33, 34, 35, 36].

Indeed, SNPs in intron region probably exert larger effects on target genes than we hitherto thought, on account of the plenty of functional elements in this region, including cis-acting RNA elements, intron splice enhancers and intron splice silencers and so on [37]. However, high quality SNPs in this region are shown to be associated with mRNA expression level without precise interpretation (such as SMAD7 rs12953717 and rs4464148) [38]. Hence, the mechanisms of high quality intronic SNPs should not be ignored by further researchers and studies concerned with these SNPs are still found wanting.

Regarding the SNPs located in promoter region, it has revealed that they can alter the binding ability to transcription factors, affecting the transcriptional efficiency of genes (such as TGF-β1 rs1800469) [39]. Moreover, the 3′-UTR region of genes contain multiple microRNA binding sites. Hence, SNPs in this region are speculated to disrupt the microRNA binding sites, leading to an increased expression level of target genes (such as KRAS rs712, predicted by a bioinformatics website: ‘snpinfo.niehs.nih.gov’).

For SNPs in non-coding region, we found that high quality SNPs were detected in both microRNAs (miRNA) and long non-coding RNAs (lncRNA), which could indirectly participate in CRC cancerogenesis by interacting with encoding mRNA. SNPs in miRNA have a crucial influence on its synthesis and down-regulation (such as miR-196a2 rs11614913 and miR-27a rs895819) [20, 40, 41] and can also regulate the binding capacity to target genes (such as miR-196a2 rs11614913) [42]. In addition, SNPs in lncRNA can lead to an aberrant expression of lncRNA by disrupting its vital regulatory region (such as CASC8 rs1505477) [43], and regulate the expression level of target genes by modulating the binding of transcription factors (TFs) to its promoter region (such as CASC8 rs1505477, CCAT2 rs6983267 and LOC105376400 rs10795668) [44, 45, 46, 47].

Furthermore, their were also three high quality SNPs: BMP2 rs961253, BMP4 rs4444235 and GREM1 rs4779584, not located in known genes. Further studies are required to explain their association with CRC risk. Additionally, data in our study revealed that high quality SNPs are diffused distributed in coding or non-coding region of chromosomes: 1, 3, 8, 10, 11, 12, 14, 15, 18, 19 and 20, which indicated the complicated molecular mechanisms for CRC generation involve numerous genomic and epigenomic variants.

Conclusion and expectations

In this systematic review and large-scale meta-analysis, we identified 15 distinct high quality SNPs associated with NHCRC risk and first reported 20 SNPs in primary meta-analysis, 24 SNPs in subgroup analysis (15 for colon, 9 for rectum) and 10 SNPs in TNM stage subgroup analysis. The comprehensive survey in the field of genetic predisposition to sporadic colorectal cancer generalized the current situation of the study on NHCRC susceptibility SNPs, providing useful data for investigators to design future studies.

Notes

Authors’ contributions

YY and QX conceived and designed the study. JW and QX were responsible for the data extraction, cumulative evidence assessment and statistics. JW and YY wrote the manuscript. All authors read and approved the final manuscript.

Acknowledgements

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Availability of data and materials

The authors declare that the data supporting the findings of this study are available within the article.

Consent for publication

Not applicable.

Ethics approval and consent to participate

Not applicable.

Funding

This study was supported partly by the grants from the 13th 5 years for the National Key Research and Development Program & Key Special Project (2018YFC1311600).

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary material

12935_2018_656_MOESM1_ESM.doc (238 kb)
Additional file 1: Table S1. Detailed information of meta-analyses results for 25 high credibility significant associations. Table S2. Meta-analysis results: SNPs with non-significant associations to CRC risk.

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© The Author(s) 2018

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 and Affiliations

  1. 1.Tumor Etiology and Screening Department of Cancer Institute and General SurgeryThe First Hospital of China Medical UniversityShenyangChina
  2. 2.Key Laboratory of Cancer Etiology and Prevention in Liaoning Education DepartmentThe First Hospital of China Medical UniversityShenyangChina
  3. 3.Key Laboratory of GI Cancer Etiology and Prevention in Liaoning ProvinceThe First Hospital of China Medical UniversityShenyangChina

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