Disease Markers
Volume 2021 (2021), Article ID 6615001, 11 pages
https://doi.org/10.1155/2021/6615001

Research Article

Association of Polymorphisms in Vitamin D-Metabolizing Enzymes DHCR7 and CYP2R1 with Cancer Susceptibility: A Systematic Review and Meta-Analysis

Jing Wen,¹ Jia Li,¹ Xinyuan Liang,¹ and Aiping Wang²

¹Physical Examination Center, The First Hospital of China Medical University, Shenyang, Liaoning 110001, China
²Department of Nursing, The First Hospital of China Medical University, Shenyang, Liaoning 110001, China

Correspondence should be addressed to Aiping Wang; jianghaoran88@hotmail.com

Received 22 November 2020; Accepted 30 April 2021; Published 22 May 2021

Academic Editor: Kishore Chaudhry

Copyright © 2021 Jing Wen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

The deficiency of vitamin D has been reported to be relevant to cancer risk. DHCR7 and CYP2R1 are crucial components of vitamin D-metabolizing enzymes. Thus, accumulating researchers are concerned with the correlation between polymorphisms of DHCR7 and CYP2R1 genes and cancer susceptibility. Nevertheless, the conclusions of literatures are inconsistent. We conducted an integrated review for the correlation of DHCR7 and CYP2R1 SNPs with cancer susceptibility. In the meanwhile, a meta-analysis was performed using accessible data to clarify the association between DHCR7 and CYP2R1 SNPs and overall cancer risk. Literatures which meet the rigid inclusion and exclusion criteria were involved. The association of each SNP with cancer risk was calculated by odds ratios (ORs). 12 case-control designed studies covering 23780 cases and 27307 controls were ultimately evolved in the present meta-analysis of five SNPs (DHCR7 rs12785878 and rs1790349 SNP; CYP2R1 rs10741657, rs12794714, and rs2060793 SNP). We found that DHCR7 rs12785878 SNP was significantly related to cancer risk in the whole population, Caucasian subgroup, and hospital-based (HB) subgroup. DHCR7 rs1790349 SNP was analyzed to increase cancer risk in Caucasians. Moreover, CYP2R1 rs12794714-A allele had correlation with a lower risk of colorectal cancer. Our findings indicated that rs12785878, rs1790349, and rs12794714 SNPs might potentially be biomarkers for cancer susceptibility.

1. Introduction

Vitamin D, also regarded as 1,25-dihydroxyvitamin D3, is a pivotal steroid prohormone which has a significant role to play in musculoskeletal health [1]. Additionally, compelling evidence reveals the roles of vitamin D on extraskeletal diseases, such as infectious disease [2], cardiovascular disease [3], autoimmune disease [4], neurodegeneration [5], and cancer [6]. Deficiency of vitamin D has been reported to be relevant to oral squamous cell carcinoma [7], breast cancer [8], colorectal cancer [9], prostate cancer [10], pancreas cancer [11], thyroid cancer [12], hepatocellular carcinoma [13], and ovarian cancer [14]. Furthermore, vitamin D supplementation may decrease the death of cancer by 16% [15].

There has an individual variability in serum vitamin D stores which cannot be explained alone by age, sunlight exposure, body mass index, or dietary intake [12]. Studies have demonstrated that vitamin D level is highly heritable [16]. Genetic and epigenetic factors can impact several crucial steps along the metabolic pathway of vitamin D. Genes who directly participate in the vitamin D pathway gene are DHCR7, CYP2R1, VDR, CYP24A1, CYP27B1, and so on, and the aberrant expressions of them have been demonstrated to be associated with vitamin D concentrations and cancer [17–21]. Genome-wide association studies (GWAS) have detected the correlations of 25-hydroxyvitamin D concentrations with single nucleotide polymorphisms (SNPs) on genes that participated in the vitamin D metabolic pathway [1, 16].

DHCR7, located on chromosome 11q13.4, encodes ultimate enzyme 7-dehydrocholesterol reductase which catalyzes the conversion of the vitamin D3 precursor (7-dehydrocholesterol) to cholesterol, instead of vitamin D3 [22]. Cytochrome P450 family 2 subfamily R member 1 (CYP2R1, on chromosome 11p15.2) encodes vitamin D 25-hydroxylase which catalyzes the initial hydroxylation reaction of vitamin D synthesis, converting vitamin D to 25-hydroxyvitamin D [9]. Increasing correlational studies were concerned with DHCR7 and CYP2R1 polymorphisms and susceptibility to cancer. Some studies confirmed the associations, whereas others remained skeptical or denied their correlations. The aim of the present study was to explore whether the DHCR7 or CYP2R1 SNPs are related to cancer risk.

We comprehensively reviewed the eligible studies and analyzed all available data. Our aim is to explore the association of DHCR7 and CYP2R1 SNPs with cancer risk, supplying clues to researchers for screening novel cancer biomarkers.

2. Materials and Methods

2.1. Retrieval Strategy

Two investigators (J.W. and J.L.), respectively, carried out a comprehensive literature retrieval in PubMed and Web of Science database up to February 2020, by using the following query terms: “CYP2R1/cytochrome P450 family 2 subfamily R member 1/DHCR7/7-dehydrocholesterol reductase”, “polymorphism/SNP/variant/variation”, and “cancer/carcinoma/neoplasm/tumor/”. All enrolled articles must satisfy inclusion standards: (1) case-control or nested case-control designed study; (2) in regard to the association of DHCR7 and CYP2R1 SNPs with predisposition to cancer. Meanwhile, publications meeting the following exclusion standards were removed: (1) letters or reviews; (2) repeated records; (3) irrelevant to DHCR7 and CYP2R1 SNPs or carcinoma; (4) without any available genotype distribution data.

2.2. Data Extraction

Data was collected by two investigators (J.W. and J.L.) independently and came to a consensus regarding all items. Essential characteristics extracted from each qualified publication comprised first author, year of publication, ethnicity, sample size, type of carcinoma, gene, SNPs, genotype distribution frequency of case and control groups, control group source (hospital-based (HB) or population-based (PB)), Hardy-Weinberg equilibrium (HWE), adjustment factors, and genotyping method. When multiple studies were conducted in one article, data were collected individually.

2.3. Methodology Quality Assessment

Two authors (J.W. and X.L.) scored the quality of each enrolled publication independently, based on a scoring scheme mentioned in prior literature [23, 24]. Six evaluation items were involved in the scoring scheme: representativeness of cases, control source, ascertainment of carcinomas, sample size, HWE in the control group, and quality assurance of genotyping methods. The quality assessment scores ranged from 0 to 10. Study with no less than 5 quality scores was recognized as an eligible study which could be enrolled in subsequent analysis.

2.4. False-Positive Report Probability

False-positive report probability (FPRP) was computed to estimate whether our study findings are “noteworthy.” Initially, we computed the statistic power of the test based on the sample size, ORs, and values by using NCSS-PASS software (USA, version 11.0.7). Then, we drew the FPRP values from a calculation formula which had been reported in earlier researches, and was regarded as a noteworthy finding [25].

2.5. Statistical Analysis

The chi-square test ( test) was conducted to compute the HWE for genotype frequency distribution of CYP2R1 and DHCR7 polymorphisms in controls. The correlation of each CYP2R1 and DHCR7 polymorphism with carcinoma risk was computed by odds ratio (OR) with its 95% confidence interval (95% CI). Cochran’s -based test was adopted to estimate the heterogeneity of interstudy (significance set as , ). We pooled the results by means of a fixed-effects model when no interstudy heterogeneity arose; the random-effects model was adopted otherwise. Besides, the recessive and dominant genetic models were, respectively, considered as variant homozygote vs. heterozygote/wild homozygote, and heterozygote/variant homozygote vs. wild homozygote. Publication bias was estimated using the rank correlation test (Begg’s test) and linear regression methods (Egger’s test). Sensitivity analysis was calculated to show whether the merged findings were steady enough after removing those outlying studies. All the mentioned statistical analyses were calculated by STATA software (STATA Corp., College Station, TX, USA, version 11.0). All values were for two-tailed tests, and less than 0.05 was regarded as statistically significant.

3. Results

3.1. Features of Eligible Studies and Analyzed SNPs

Totally 137 publications were gathered through database retrieval after removing duplicate hits. 125 articles were removed after browsing titles and abstracts: 21 were functional studies; 6 were review or meeting; 8 were not case-control studies; 17 were not related to DHCR7 or CYP2R1 SNPs; 53 were not concerned with carcinoma; and 13 were not correlated with carcinoma risk. Therefore, 19 studies are ought to be involved in the present analysis. Nevertheless, 7 publications lost original data, 5 of which were genome-wide association studies. And we were not able to contact with authors. Thus, 12 case-control designed studies were finally evolved in the present meta-analysis, covering 23780 cases and 27307 controls, which is shown in Figure 1. The features of these eligible studies which met the quality assessment criterion are listed in Table 1.

Figure 1: The flow chart of identification for studies included in the meta-analysis based on PRISMA guidelines.

Table 1: Characteristics of eligible studies.

No.
First author
Year
Ethnicity
Sample size
Source of control groups
Genotyping method
Adjusted factors
Citation

Case
Control

1
Isabel S. Carvalho
2019
Caucasian (Portugal)
500
500
PB
PCR-RFLP
Age, sex
[12]

2
Prajjalendra Barooah
2019
Caucasian (Indian)
60
102
HB
PCR-RFLP
Age, sex
[13]

3
Jianzhou Yang
2017
Asian (China)
565
557
PB
GenomeLab SNPstream
Age, sex
[35]

4
Alison M. Mondul
2015
Caucasian (European)
8618
9960
HB
TaqMan or genome-wide scans
Age
[36]

5
Tess V. Clendenen
2015
Caucasian (Swedish)
733
1432
PB
Illumina GoldenGate assay
Age, menopausal status
[37]

6
Fabio Pibiri
2014
African (African-American)
902
760
PB
Sequenom MassARRAY
Age, sex, ancestry
[38]

7
Touraj Mahmoudi
2014
Caucasian (Iranian)
290
354
HB
PCR-RFLP
Age, BMI, sex
[9]

8
Wei Wang
2014
Caucasian (Hispanic)
826
779
PB
Illumina GoldenGate assay
Age, BMI

Wei Wang
2014
Mixed (non-Hispanic)
224
130
PB
Illumina GoldenGate assay
Age, BMI
[39]

9
Christian M. Lange
2013
Asian (Japanese)
803
1253
HB
Competitive allele-specific TaqMan PCR
Sex
[40]

10
Alison M. Mondul
2013
Caucasian
9378
9986
PB
TaqMan
Age, ethnicity
[41]

11
Laura N. Anderson
2013
Caucasian (Canada)
628
1192
PB
MassARRAY
Age, sex
[11]

12
Marissa Penna-Martinez
2012
Caucasian (Germany)
253
302
PB
TaqMan
NM
[42]

Note: HB: hospital based; PB: population based; PCR-RFLP: in reaction-restriction fragment length polymorphism; NM: not mentioned.

Six polymorphisms were able to be involved in our systematic review, including rs10741657 G/A, rs12794714 G/A, rs2060793 G/A, rs3829251 G/A, rs12785878 T/G, and rs1790349 A/G. The frequency distribution of DHCR7 and CYP2R1 SNPs genotype is shown in Table 2. Six records, however, were removed from quantitative synthesis owing to the insufficient study number for some loci or being not conformed to HWE (). Consequently, five SNPs were covered in the eventual meta-analysis. For DHCR7, the analyzed SNPs were rs12785878 T/G and rs1790349 A/G; for CYP2R1, the analyzed SNPs were rs10741657 G/A, rs12794714 G/A, and rs2060793 G/A.

Table 2: Genotype frequency distributions of lncRNA SNPs studied in included studies.

First author
Year
Gene
SNPsa
Type of cancer
Ethnicity
Sample size
Case
Control
Quality score

Included in meta-analysis

Case
Control
Homozygote wild
Heterozygote
Homozygote variant
Homozygote wild
Heterozygote
Homozygote variant

Isabel S. Carvalho
2019
CYP2R1
rs2060793 (G/A)
TC
Caucasian (Portugal)
500
500
189
236
75
183
256
61
7
0.047
Noc

2019
DHCR7
rs12785878 (T/G)
TC
Caucasian (Portugal)
500
500
150
251
99
197
234
69
7
0.971
Yes

Prajjalendra Barooah
2019
CYP2R1
rs10741657 (G/A)
HCC
Caucasian (Indian)
60
102
28
23
9
51
35
16
4.5
0.025
Noc

Jianzhou Yang
2017
DHCR7
rs3829251 (G/A)
ESCC
Asian (China)
565
557
302
218
45
283
232
42
8
0.557
Nob

Alison M. Mondul
2015
CYP2R1
rs10741657 (G/A)
BC
Caucasian (European)
8618
9960
3276
4041
1301
3708
4766
1486
6.5
0.475
Yes

2015
DHCR7
rs12785878 (T/G)
BC
Caucasian (European)
9224
10560
4935
3620
669
5674
4052
834
5.5
0.003
Noc

Tess V. Clendenen
2015
CYP2R1
rs10741657 (G/A)
BC
Caucasian (Swedish)
733
1432
200
358
175
405
720
307
7.5
0.696
Yes

2015
DHCR7
rs12785878 (T/G)
BC
Caucasian (Swedish)
733
1433
273
371
89
571
659
203
7.5
0.562
Yes

2015
DHCR7
rs1790349 (A/G)
BC
Caucasian (Swedish)
732
1431
348
326
58
752
571
108
7.5
0.978
Yes

Fabio Pibiri
2014
CYP2R1
rs12794714 (G/A)
CRC
African (African-American)
902
760
638
253
11
501
239
20
9.5
0.175
Yes

Touraj Mahmoudi
2014
CYP2R1
rs12794714 (G/A)
CRC
Caucasian (Iranian)
290
354
93
135
62
110
167
77
7
0.364
Yes

Wei Wang
2014
CYP2R1
rs2060793 (G/A)
BC
Mixed (Hispanic)
826
779
303
391
132
315
356
108
9.5
0.644
Yes

2014
CYP2R1
rs2060793 (G/A)
Caucasian (non-Hispanic)
224
130
89
104
31
51
58
21
8.5
0.512
Yes

2014
DHCR7
rs12785878 (T/G)
BC
Mixed (Hispanic)
826
779
189
410
227
185
353
241
7.5
0.013
Noc

2014
DHCR7
rs12785878 (T/G)
BC
Caucasian (non-Hispanic)
224
130
123
90
11
62
55
13
8.5
0.876
Yes

2014
DHCR7
rs1790349 (A/G)
BC
Mixed (Hispanic)
826
779
573
227
26
527
227
25
8.5
0.927
Yes

2014
DHCR7
rs1790349 (A/G)
BC
Caucasian (non-Hispanic)
224
130
168
52
4
95
30
5
8.5
0.195
Yes

Christian M. Lange
2013
CYP2R1
rs10741657 (G/A)
HCC
Asian (Japanese)
803
1253
320
377
106
482
597
174
8.5
0.615
Yes

2013
DHCR7
rs12785878 (T/G)
HCC
Asian (Japanese)
803
1253
84
336
383
153
543
557
8.5
0.247
Yes

2013
DHCR7
rs12785878 (T/G)
HCC
Caucasian (German)
116
208
63
44
9
113
77
18
8.5
0.353
Yes

Alison M. Mondul
2013
CYP2R1
rs10741657 (G/A)
PC¹
Caucasian
9378
9986
3481
4392
1505
3789
4667
1530
9
0.137
Yes

2013
DHCR7
rs12785878 (T/G)
PC¹
Caucasian
9620
10225
4979
3816
825
5221
4047
957
8

Noc

Laura N. Anderson
2013
CYP2R1
rs10741657 (G/A)
PC²
Caucasian (Canada)
625
1191
262
286
77
451
550
190
8
0.304
Yes

2013
CYP2R1
rs12794714 (G/A)
PC²
Caucasian (Canada)
628
1192
180
307
141
399
559
234
8
0.131
Yes

Marissa Penna-Martinez
2012
CYP2R1
rs10741657 (G/A)
TC
Caucasian (German)
253
302
96
110
47
119
139
44
7.5
0.742
Yes

2012
CYP2R1
rs12794714 (G/A)
TC
Caucasian (German)
253
302
78
130
45
94
144
64
7.5
0.522
Yes

Note: : the value for Hardy-Weinberg equilibrium in control groups; amajor/minor; bexcluded due to the limited number for this locus; cexcluded due to the SNP not being in accordance with HWE. The results are in bold if ; TC = thyroid cancer; HCC = hepatocellular carcinoma; ESCC = esophageal squamous cell carcinoma; BC = breast cancer; PC¹ = prostate cancer; PC² = pancreas cancer.

3.2. Quantitative Data Synthesis of Five SNPs in DHCR7 and CYP2R1 Genes

3.2.1. Two Polymorphisms in DHCR7 Gene

Five eligible studies were collected to evaluate the relationships between DHCR7 SNPs and risk of carcinoma, on the basis of entire population. The rs12785878 T/G SNP was illustrated to be associated with incremental cancer risk. The correlation of rs12785878 T/G SNP was discovered under the heterozygote genotype model (TG vs. TT: (1.027-1.328), , Table 3). The relationship between rs1790349 A/G SNP and carcinoma risk was not found in the initial analysis.

Table 3: Meta-analysis of the association between common SNPs and cancer risk.

Stratification

Heterozygote vs. wild-type
Mutation homozygote vs. wild-type
Dominant model
Recessive model
Allelic model

OR (95% CI)

(%)
OR (95% CI)

(%)
OR (95% CI)

(%)
OR (95% CI)

(%)
OR (95% CI)

(%)

CYP2R1

rs10741657 (G/A)
6
0.987 (0.947-1.028)
0.522
0
1.006 (0.906-1.117)
0.905
50.9
0.995 (0.957-1.034)
0.799
18.4
1.030 (0.978-1.084)
0.264
41.2
0.998 (0.949-1.050)
0.943
51.2

Ethnicity

    Caucasian
5
0.989 (0.948-1.031)
0.587
0
1.016 (0.905-1.142)
0.785
58.2
0.997 (0.959-1.038)
0.901
30.8
1.030 (0.936-1.133)
0.543
50.2
1.003 (0.948-1.061)
0.917
58.5

    Asian
1
0.951 (0.786-1.152)
0.608
NA
0.918 (0.694-1.214)
0.547
NA
0.944 (0.787-1.131)
0.531
NA
0.943 (0.727-1.223)
0.658
NA
0.957 (0.840-1.089)
0.503
NA

Type of cancer

    Breast cancer
2
0.963 (0.907-1.023)
0.227
0
1.008 (0.927-1.095)
0.858
20.7
0.974 (0.920-1.031)
0.361
0
1.030 (0.955-1.111)
0.442
14.9
0.995 (0.957-1.036)
0.817
31.1

    Pancreas cancer 2
1
0.895 (0.726-1.103)
0.289
NA
0.698 (0.514-0.947)
0.021
NA
0.844 (0.693-1.029)
0.093
NA
0.740 (0.557-0.984)
0.038
NA
0.848 (0.736-0.978)
0.023
NA

    Prostate cancer
1
1.024 (0.963-1.090)
0.445
NA
1.071 (0.984-1.165)
0.114
NA
1.036 (0.977-1.098)
0.236
NA
1.057 (0.978-1.142)
0.165
NA
1.033 (0.992-1.076)
0.118
NA

    Hepatocellular carcinoma
1
0.951 (0.786-1.152)
0.608
NA
0.918 (0.694-1.214)
0.547
NA
0.944 (0.787-1.131)
0.531
NA
0.943 (0.727-1.223)
0.658
NA
0.957 (0.840-1.089)
0.503
NA

    Thyroid caner
1
0.981 (0.679-1.416)
0.918
NA
1.324 (0.810-2.164)
0.263
NA
1.063 (0.755-1.499)
0.725
NA
1.338 (0.853-2.098)
0.205
NA
1.122 (0.881-1.429)
0.352
NA

Source of controls

    HB
2
0.959 (0.903-1.018)
0.169
0
0.984 (0.905-1.070)
0.709
0
0.965 (0.912-1.021)
0.214
0
1.007 (0.933-1.088)
0.85
0
0.984 (0.946-1.024)
0.437
0

    PB
4
1.012 (0.957-1.071)
0.677
0
1.020 (0.829-1.256)
0.849
64.6
1.022 (0.969-1.078)
0.415
23.5
1.032 (0.867-1.229)
0.723
60.7
1.007 (0.913-1.110)
0.89
62.3

rs12794714 (G/A)
4
1.007 (0.825-1.231)
0.942
51
0.908 (0.609-1.352)
0.634
68
0.993 (0.789-1.249)
0.953
66.2
0.907 (0.664-1.239)
0.539
58.9
0.968 (0.807-1.160)
0.723
72.5

Ethnicity

    Caucasian
3
1.127 (0.951-1.336)
0.167
0
1.134 (0.921-1.397)
0.236
40.9
1.130 (0.964-1.326)
0.132
10.5
1.056 (0.881-1.265)
0.558
24.8
1.074 (0.967-1.192)
0.183
41.2

    African
1
0.831 (0.672-1.028)
0.088
NA
0.432 (0.205-0.910)
0.027
NA
0.800 (0.650-0.985)
0.036
NA
0.457 (0.217-0.959)
0.039
NA
0.800 (0.666-0.960)
0.017
NA

Type of cancer

    Colorectal cancer
2
0.862 (0.718-1.035)
0.111
0
0.681 (0.317-1.466)
0.326
69.1
0.841 (0.705-1.003)
0.054
0
0.717 (0.344-1.493)
0.374
68.9
0.866 (0.753-0.997)
0.046
44.1

    Pancreas cancer
1
1.217 (0.973-1.524)
0.086
NA
1.336 (1.016-1.775)
0.038
NA
1.252 (1.014-1.546)
0.036
NA
1.185 (0.936-1.500)
0.157
NA
1.167 (1.017-1.339)
0.028
NA

    Thyroid caner
1
1.088 (0.742-1.595)
0.666
NA
0.847 (0.522-1.377)
0.504
NA
1.014 (0.706-1.455)
0.94
NA
0.805 (0.526-1.230)
0.315
NA
0.939 (0.740-1.191)
0.604
NA

Source of controls

    HB
1
0.956 (0.669-1.367)
0.806
NA
0.952 (0.617-1.469)
0.825
NA
0.955 (0.684-1.333)
0.787
NA
0.978 (0.671-1.427)
0.909
NA
0.973 (0.780-1.213)
0.806
NA

    PB
3
1.023 (0.787-1.331)
0.864
66.9
0.856 (0.476-1.538)
0.602
78
1.004 (0.742-1.360)
0.978
77.3
0.839 (0.522-1.348)
0.468
72.5
0.963 (0.754-1.231)
0.765
81.6

rs2060793 (G/A)
2
1.121 (0.923-1.362)
0.247
0
1.184 (0.902-1.554)
0.223
19
1.136 (0.946-1.364)
0.172
0
1.113 (0.866-1.430)
0.402
6.3
1.098 (0.964-1.250)
0.16
8.4

DHCR7

rs12785878 (T/G)
5
1.168 (1.027-1.328)
0.018
8.3
1.074 (0.736-1.569)
0.71
73.1
1.136 (0.935-1.381)
0.2
53.2
1.017 (0.762-1.357)
0.91
67.5
1.064 (0.906-1.250)
0.448
70.1

Ethnicity

    Caucasian
4
1.178 (1.021-1.358)
0.024
30.1
0.980 (0.562-1.710)
0.944
79.2
1.108 (0.854-1.436)
0.441
64.8
0.929 (0.584-1.477)
0.756
73.7
1.031 (0.814-1.305)
0.802
77.2

    Asian
1
1.127 (0.836-1.520)
0.433
NA
1.252 (0.931-1.684)
0.136
NA
1.191 (0.898-1.579)
0.226
NA
1.139 (0.954-1.361)
0.15
NA
1.120 (0.980-1.281)
0.097
NA

Type of cancer

    Breast cancer
2
1.048 (0.756-1.454)
0.778
50.1
0.699 (0.341-1.433)
0.328
63.5
0.961 (0.658-1.404)
0.838
63.9
0.795 (0.616-1.025)
0.077
42.4
0.899 (0.665-1.215)
0.488
66.1

    Hepatocellular carcinoma
2
1.098 (0.851-1.415)
0.472
0
1.209 (0.913-1.599)
0.185
0
1.135 (0.893-1.442)
0.302
0
1.127 (0.947-1.341)
0.177
0
1.102 (0.972-1.250)
0.129
0

    Thyroid caner
1
1.409 (1.068-1.859)
0.015
NA
1.884 (1.297-2.738)
0.001
NA
1.517 (1.167-1.972)
0.002
NA
1.542 (1.102-2.158)
0.012
NA
1.376 (1.150-1.645)
<0.001
NA

Source of controls

    HB
2
1.098 (0.852-1.415)
0.471
0
1.209 (0.913-1.599)
0.185
0
1.135 (0.893-1.442)
0.302
0
1.127 (0.947-1.341)
0.177
0
1.102 (0.972-1.250)
0.129
0

    PB
3
1.193 (1.028-1.385)
0.02
49.3
0.988 (0.501-1.945)
0.971
85.9
1.125 (0.815-1.552)
0.474
75.3
0.925 (0.528-1.623)
0.787
82.3
1.038 (0.777-1.388)
0.8
84.5

rs1790349 (A/G)
3
1.060 (0.850-1.323)
0.605
52
1.056 (0.793-1.407)
0.71
0
1.043 (0.837-1.300)
0.705
55.1
0.998 (0.754-1.319)
0.986
0
1.048 (0.942-1.167)
0.391
45.7

Ethnicity

    Caucasian
2
1.201 (1.008-1.431)
0.04
0
1.094 (0.784-1.526)
0.598
44
1.180 (0.998-1.396)
0.053
21.2
1.003 (0.727-1.386)
0.983
30.6
1.110 (0.972-1.266)
0.122
37.4

    Mixed
1
0.920 (0.739-1.145)
0.453
NA
0.957 (0.545-1.677)
0.877
NA
0.923 (0.748-1.140)
0.458
NA
0.980 (0.561-1.712)
0.944
NA
0.940 (0.783-1.128)
0.505
NA

Note: OR: odds ratio; CI: confidence interval. The results are in bold if .

In stratified analyses, rs12785878 T/G SNP was quantitatively analyzed in “ethnicity,” “type of carcinoma,” and “source of control group” subgroups, and the rs1790349 A/G SNP was analyzed in the “ethnicity” subgroup. For rs12785878 T/G SNP, correlations calculated under the heterozygote genotype model (TG vs. TT) were observed in “Caucasian population” and “PB” subgroups (Caucasian: (1.021-1.358), ; PB: (1.028-1.385), , Table 3). For rs1790349 A/G SNP, association was only manifested in the “Caucasian population” subgroup (AG vs. AA: (1.008-1.431), , Table 3).

3.2.2. Three Polymorphisms in CYP2R1 Gene

Nine eligible publications were involved to estimate the association intensity of CYP2R1 polymorphisms and overall carcinoma risk. Nevertheless, none of these SNPs manifest significant correlations with risk of carcinoma in any genetic models.

Then, stratified analyses of rs10741657 G/A and rs12794714 G/A SNPs were conducted based on “ethnicity,” “type of carcinoma,” and “source of control group,” on account of the presence of between-study heterogeneity. For rs12794714 G/A SNP, its allelic models had correlation with a decreased genetic predisposition to colorectal cancer (A vs. G: (0.753-0.997), , Table 3). Correlations could not be elucidated among any of the stratified analyses of rs10741657 G/A SNP.

3.3. Sensitivity Analysis

Sensitivity analysis was adopted to assess the impact of each study on summarized findings, by means of calculating the OR (95% CI) before and after deleting each article from the pooled analysis. For rs12785878 T/G SNP, it made no sense after the removal of two articles (Isabel S. Carvalho 2019, Tess V. Clendenen 2015) individually (Supplementary Table S1).

3.4. Publication Bias

Potential publication bias was evaluated for all covered publications by means of two test methods mentioned above. The publication bias was found in rs12794714 G/A SNP under the recessive model, for in both tests, which might be because of the deficient publications with negative results or the defective methodological design for small-scale studies (Table 4).

Table 4: The results of Begg’s and Egger’s test for the publication bias.

Comparison type
Begg’s test
Egger’s test

value
value
value
value

CYP2R1 rs10741657 (G/A)

Heterozygote vs. homozygote wild
0
1
-0.7
0.521

Homozygote variant vs. homozygote wild
0.38
0.707
-0.73
0.503

Dominant model
0
1
-0.53
0.627

Recessive model
0
1
-0.29
0.787

Allelic model
0.75
0.452
-0.38
0.722

CYP2R1 rs12794714 (G/A)

Heterozygote vs. homozygote wild
0.34
0.734
0.21
0.851

Homozygote variant vs. homozygote wild
1.02
0.308
-2.84
0.105

Dominant model
0.34
0.734
-0.01
0.994

Recessive model
1.7
0.089
-9.45
0.011

Allelic model
0.34
0.734
-1.12
0.38

CYP2R1 rs2060793 (G/A)

Heterozygote vs. homozygote wild
0
1
NA
NA

Homozygote variant vs. homozygote wild
0
1
NA
NA

Dominant model
0
1
NA
NA

Recessive model
0
1
NA
NA

Allelic model
0
1
NA
NA

DHCR7 rs12785878 (T/G)

Heterozygote vs. homozygote wild
0.24
0.806
-1.64
0.2

Homozygote variant vs. homozygote wild
-0.24
1
-1.76
0.177

Dominant model
-0.24
1
-1.74
0.18

Recessive model
0.24
0.806
-1.15
0.332

Allelic model
0.24
0.806
-1.56
0.217

DHCR7 rs1790349 (A/G)

Heterozygote vs. homozygote wild
0
1
0.18
0.884

Homozygote variant vs. homozygote wild
0
1
0.13
0.92

Dominant model
0
1
-0.44
0.737

Recessive model
0
1
-2.34
0.257

Allelic model
1.04
0.296
-0.9
0.532

Note: the results are in bold if .

3.5. FPRP Analyses

Eventually, we assessed the FPRP for our significant findings. For studies of uncommon neoplasm or common tumors with small sample size, the FRPR value less than 0.5 would make a massive improvement over previous practice, based on the professional guide of FPRP calculation. Since the present study is the first meta-analysis to estimate the association between DHCR7 and CYP2R1 SNPs and cancer risk, we consider 0.5 as the FPRP threshold. The FPRP values of rs12785878 SNP (prior probability 0.25/0.1) were less than 0.5, and FPRP values of rs1790349 and rs12794714 SNPs were also less than 0.5 (prior probability 0.25), suggesting these significant associations are deserving of attention (Table 5).

Table 5: False-positive report probability values for correlations between genotype frequency of DHCR7 and CYP2R1 and cancer risk.

Genotype
OR (95% CI)
value
Statistical powera
Prior probabilityb

0.25
0.1
0.01
0.001
0.0001

rs12785878 (T/G)

GT vs. TT (overall)
1.168 (1.027-1.328)
0.018
0.312
0.235
0.390
0.853
0.983
0.998

GT vs. TT (Caucasian)
1.178 (1.021-1.358)
0.024
0.271
0.321
0.496
0.899
0.989
0.999

GT vs. TT (PB)
1.193 (1.028-1.385)
0.02
0.264
0.288
0.457
0.885
0.986
0.999

rs1790349 (A/G)

GA vs. AA (Caucasian)
1.201 (1.008-1.431)
0.04
0.290
0.424
0.605
0.933
0.993
0.999

rs12794714 (G/A)

AA vs. GG (CRC)
0.866 (0.753-0.997)
0.046
0.367
0.401
0.582
0.927
0.992
0.999

Note: CI: confidence interval; OR: odds ratio; astatistical power was computed using the sample size of case and control, OR, and values; bthe false-positive report probability is in italics if the .

4. Discussion

In the present article, a comprehensive review was performed for the correlation of SNPs in DHCR7 and CYP2R1 genes with overall cancer risk. And a meta-analysis was conducted for five prevalent SNPs (DHCR7: rs12785878 T/G and rs1790349 A/G; CYP2R1: rs10741657 G/A, rs12794714 G/A, and rs2060793 G/A) for the first time. Our findings showed that rs12785878, rs1790349, and rs12794714 SNPs were related to cancer susceptibility in the whole population or in some subgroups, which means they might participate in cancerogenesis. No associations were discovered in other polymorphisms.

4.1. Polymorphisms in DHCR7

DHCR7 encodes an enzyme 7-dehydrocholesterol reductase which converts 7-dehydrocholesterol into cholesterol. This enzyme is a critical regulatory switch between vitamin D3 and cholesterol, for both biosynthesis processes require 7-dehydrocholesterol as substrate [26]. Moreover, DHCR7 has been assumed to be a correlated gene for vitamin D concentration and carcinoma risk [1].

Regarding rs12785878 T/G, it has been illustrated to be a 25(OH) D concentration-related SNP [1]. We found significant correlations between rs12785878 SNP and cancer susceptibility in the whole population, Caucasian subgroup, and population-based subgroup. rs12785878 SNP is located 8000 bases upstream from 5 prime UTR region of DHCR7, and it is still unclear whether it has an impact on gene expression or has a linkage disequilibrium with some other functional SNPs. The present meta-analysis of rs12785878 SNP encompasses 5 case-control studies. Only one of the five studies, however, was in accordance with our consequence. For the rs1790349 A/G SNP, it was computed to be associated with cancer risk in the Caucasian subgroup under heterozygote genotype. The rs1790349 SNP is located in the intergenic region near DHCR7 and has also been identified to be a 25(OH) D concentration-associated SNP in genome-wide association study [16, 27, 28]. Our analysis of rs1790349 SNP involves only 2 case-control studies, so further expansion of sample volume is needed.

4.2. Polymorphisms in CYP2R1

CYP2R1, as a vital important 25-hydroxylase, metabolizes vitamin D to 25(OH) D in the liver [29]. The genetic variations in CYP2R1 were correlated with the impaired activity of 25-hydroxylases, which influence the serum 25(OH) D level [30]. Association of serum 25(OH) D level with cancer susceptibility has been revealed in breast cancer [20], gastric cancer [31], thyroid cancer [32], prostate cancer [33], colorectal cancer [34], and so on. Thus, accumulating researchers were concerned with the correlation between CYP2R1 SNPs and cancer susceptibility.

For rs12794714 (G/A) SNP, we analyzed a significant relationship between A allele-rs12794714 SNP and decreased risk of colorectal cancer (CRC). Located in exon 1 region of CYP2R1, rs12794714 G/A SNP may function as an exon splicing enhancer (ESE)/exon splicing silencer (ESS) to impact gene expression, whereas it is a synonymous variant (https://snpinfo.niehs.nih.gov/). The A allele-rs12794714 SNP has been illustrated to be associated with higher serum 25-hydroxyviatamin D concentrations [16]; thus, it may reduce the cancer risk. Thus far, the protective effect of rs12794714 has only been demonstrated in CRC. Further studies remain desired concerning rs12794714 and cancer.

4.3. Limitations and Conclusions

It ought to be mentioned that the present study has several limitations. First and foremost, association studies of DHCR7 and CYP2R1 polymorphisms with cancer predisposition remain limited. Further researches are demanded for updated meta-analyses. Moreover, several items without accessible original records were removed from ultimate analysis, which might cause publication bias.

Overall, we comprehensively assessed the correlation of DHCR7 and CYP2R1 SNPs with carcinoma risk. Additionally, a meta-analysis was conducted based on all accessible data for five polymorphisms. The consequence demonstrated that 3 (re12794714, rs12785878, and rs1790349) of the 5 SNPs were associated with cancer risk in whole population or in some subgroups, indicating that they might be feasible biomarkers for cancer susceptibility.

Abbreviations

SNP: | Single nucleotide polymorphism
GWAS: | Genome-wide association studies
ORs: | Odds ratios
CI: | Confidence intervals
HWE: | Hardy-Weinberg equilibrium
FPRP: | False-positive report probability
ESE: | Exon splicing enhancer
ESS: | Exon splicing silencer.

Data Availability

The authors declare that all relevant data are presented within the paper.

Conflicts of Interest

The authors declare that there are no conflicts of interest.

Authors’ Contributions

Aiping Wang conceived and designed the study. Jing Wen and Lia Li were responsible for the data extraction. Jing Wen and Xinyuan Liang were responsible for the quality assessment. Jing Wen and Aiping Wang wrote the manuscript, and Aiping Wang revised the manuscript.

Supplementary Materials

Table S1: ORs (95% CIs) of sensitivity analysis. (Supplementary Materials)

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