Disease Markers

Disease Markers / 2021 / Article

Research Article | Open Access

Volume 2021 |Article ID 6615001 | https://doi.org/10.1155/2021/6615001

Jing Wen, Jia Li, Xinyuan Liang, Aiping Wang, "Association of Polymorphisms in Vitamin D-Metabolizing Enzymes DHCR7 and CYP2R1 with Cancer Susceptibility: A Systematic Review and Meta-Analysis", Disease Markers, vol. 2021, Article ID 6615001, 11 pages, 2021. https://doi.org/10.1155/2021/6615001

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

Academic Editor: Kishore Chaudhry
Received22 Nov 2020
Accepted30 Apr 2021
Published22 May 2021


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 [1721]. 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.

No.First authorYearEthnicitySample sizeSource of control groupsGenotyping methodAdjusted factorsCitation

1Isabel S. Carvalho2019Caucasian (Portugal)500500PBPCR-RFLPAge, sex[12]
2Prajjalendra Barooah2019Caucasian (Indian)60102HBPCR-RFLPAge, sex[13]
3Jianzhou Yang2017Asian (China)565557PBGenomeLab SNPstreamAge, sex[35]
4Alison M. Mondul2015Caucasian (European)86189960HBTaqMan or genome-wide scansAge[36]
5Tess V. Clendenen2015Caucasian (Swedish)7331432PBIllumina GoldenGate assayAge, menopausal status[37]
6Fabio Pibiri2014African (African-American)902760PBSequenom MassARRAYAge, sex, ancestry[38]
7Touraj Mahmoudi2014Caucasian (Iranian)290354HBPCR-RFLPAge, BMI, sex[9]
8Wei Wang2014Caucasian (Hispanic)826779PBIllumina GoldenGate assayAge, BMI
Wei Wang2014Mixed (non-Hispanic)224130PBIllumina GoldenGate assayAge, BMI[39]
9Christian M. Lange2013Asian (Japanese)8031253HBCompetitive allele-specific TaqMan PCRSex[40]
10Alison M. Mondul2013Caucasian93789986PBTaqManAge, ethnicity[41]
11Laura N. Anderson2013Caucasian (Canada)6281192PBMassARRAYAge, sex[11]
12Marissa Penna-Martinez2012Caucasian (Germany)253302PBTaqManNM[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.

First authorYearGeneSNPsaType of cancerEthnicitySample sizeCaseControlQuality scoreIncluded in meta-analysis
CaseControlHomozygote wildHeterozygoteHomozygote variantHomozygote wildHeterozygoteHomozygote variant

Isabel S. Carvalho2019CYP2R1rs2060793 (G/A)TCCaucasian (Portugal)500500189236751832566170.047Noc
2019DHCR7rs12785878 (T/G)TCCaucasian (Portugal)500500150251991972346970.971Yes

Prajjalendra Barooah2019CYP2R1rs10741657 (G/A)HCCCaucasian (Indian)60102282395135164.50.025Noc

Jianzhou Yang2017DHCR7rs3829251 (G/A)ESCCAsian (China)565557302218452832324280.557Nob

Alison M. Mondul2015CYP2R1rs10741657 (G/A)BCCaucasian (European)861899603276404113013708476614866.50.475Yes
2015DHCR7rs12785878 (T/G)BCCaucasian (European)92241056049353620669567440528345.50.003Noc

Tess V. Clendenen2015CYP2R1rs10741657 (G/A)BCCaucasian (Swedish)73314322003581754057203077.50.696Yes
2015DHCR7rs12785878 (T/G)BCCaucasian (Swedish)7331433273371895716592037.50.562Yes
2015DHCR7rs1790349 (A/G)BCCaucasian (Swedish)7321431348326587525711087.50.978Yes

Fabio Pibiri2014CYP2R1rs12794714 (G/A)CRCAfrican (African-American)90276063825311501239209.50.175Yes

Touraj Mahmoudi2014CYP2R1rs12794714 (G/A)CRCCaucasian (Iranian)29035493135621101677770.364Yes

Wei Wang2014CYP2R1rs2060793 (G/A)BCMixed (Hispanic)8267793033911323153561089.50.644Yes
2014CYP2R1rs2060793 (G/A)Caucasian (non-Hispanic)22413089104315158218.50.512Yes
2014DHCR7rs12785878 (T/G)BCMixed (Hispanic)8267791894102271853532417.50.013Noc
2014DHCR7rs12785878 (T/G)BCCaucasian (non-Hispanic)22413012390116255138.50.876Yes
2014DHCR7rs1790349 (A/G)BCMixed (Hispanic)82677957322726527227258.50.927Yes
2014DHCR7rs1790349 (A/G)BCCaucasian (non-Hispanic)224130168524953058.50.195Yes

Christian M. Lange2013CYP2R1rs10741657 (G/A)HCCAsian (Japanese)80312533203771064825971748.50.615Yes
2013DHCR7rs12785878 (T/G)HCCAsian (Japanese)8031253843363831535435578.50.247Yes
2013DHCR7rs12785878 (T/G)HCCCaucasian (German)1162086344911377188.50.353Yes

Alison M. Mondul2013CYP2R1rs10741657 (G/A)PC1Caucasian9378998634814392150537894667153090.137Yes
2013DHCR7rs12785878 (T/G)PC1Caucasian96201022549793816825522140479578Noc

Laura N. Anderson2013CYP2R1rs10741657 (G/A)PC2Caucasian (Canada)62511912622867745155019080.304Yes
2013CYP2R1rs12794714 (G/A)PC2Caucasian (Canada)628119218030714139955923480.131Yes

Marissa Penna-Martinez2012CYP2R1rs10741657 (G/A)TCCaucasian (German)2533029611047119139447.50.742Yes
2012CYP2R1rs12794714 (G/A)TCCaucasian (German)253302781304594144647.50.522Yes

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.

StratificationHeterozygote vs. wild-typeMutation homozygote vs. wild-typeDominant modelRecessive modelAllelic model
OR (95% CI) (%)OR (95% CI) (%)OR (95% CI) (%)OR (95% CI)(%)OR (95% CI) (%)

rs10741657 (G/A)60.987 (0.947-1.028)0.52201.006 (0.906-1.117)0.90550.90.995 (0.957-1.034)0.79918.41.030 (0.978-1.084)0.26441.20.998 (0.949-1.050)0.94351.2
 Caucasian50.989 (0.948-1.031)0.58701.016 (0.905-1.142)0.78558.20.997 (0.959-1.038)0.90130.81.030 (0.936-1.133)0.54350.21.003 (0.948-1.061)0.91758.5
 Asian10.951 (0.786-1.152)0.608NA0.918 (0.694-1.214)0.547NA0.944 (0.787-1.131)0.531NA0.943 (0.727-1.223)0.658NA0.957 (0.840-1.089)0.503NA
Type of cancer
 Breast cancer20.963 (0.907-1.023)0.22701.008 (0.927-1.095)0.85820.70.974 (0.920-1.031)0.36101.030 (0.955-1.111)0.44214.90.995 (0.957-1.036)0.81731.1
 Pancreas cancer 210.895 (0.726-1.103)0.289NA0.698 (0.514-0.947)0.021NA0.844 (0.693-1.029)0.093NA0.740 (0.557-0.984)0.038NA0.848 (0.736-0.978)0.023NA
 Prostate cancer11.024 (0.963-1.090)0.445NA1.071 (0.984-1.165)0.114NA1.036 (0.977-1.098)0.236NA1.057 (0.978-1.142)0.165NA1.033 (0.992-1.076)0.118NA
 Hepatocellular carcinoma10.951 (0.786-1.152)0.608NA0.918 (0.694-1.214)0.547NA0.944 (0.787-1.131)0.531NA0.943 (0.727-1.223)0.658NA0.957 (0.840-1.089)0.503NA
 Thyroid caner10.981 (0.679-1.416)0.918NA1.324 (0.810-2.164)0.263NA1.063 (0.755-1.499)0.725NA1.338 (0.853-2.098)0.205NA1.122 (0.881-1.429)0.352NA
Source of controls
 HB20.959 (0.903-1.018)0.16900.984 (0.905-1.070)0.70900.965 (0.912-1.021)0.21401.007 (0.933-1.088)0.8500.984 (0.946-1.024)0.4370
 PB41.012 (0.957-1.071)0.67701.020 (0.829-1.256)0.84964.61.022 (0.969-1.078)0.41523.51.032 (0.867-1.229)0.72360.71.007 (0.913-1.110)0.8962.3
rs12794714 (G/A)41.007 (0.825-1.231)0.942510.908 (0.609-1.352)0.634680.993 (0.789-1.249)0.95366.20.907 (0.664-1.239)0.53958.90.968 (0.807-1.160)0.72372.5
 Caucasian31.127 (0.951-1.336)0.16701.134 (0.921-1.397)0.23640.91.130 (0.964-1.326)0.13210.51.056 (0.881-1.265)0.55824.81.074 (0.967-1.192)0.18341.2
 African10.831 (0.672-1.028)0.088NA0.432 (0.205-0.910)0.027NA0.800 (0.650-0.985)0.036NA0.457 (0.217-0.959)0.039NA0.800 (0.666-0.960)0.017NA
Type of cancer
 Colorectal cancer20.862 (0.718-1.035)0.11100.681 (0.317-1.466)0.32669.10.841 (0.705-1.003)0.05400.717 (0.344-1.493)0.37468.90.866 (0.753-0.997)0.04644.1
 Pancreas cancer11.217 (0.973-1.524)0.086NA1.336 (1.016-1.775)0.038NA1.252 (1.014-1.546)0.036NA1.185 (0.936-1.500)0.157NA1.167 (1.017-1.339)0.028NA
 Thyroid caner11.088 (0.742-1.595)0.666NA0.847 (0.522-1.377)0.504NA1.014 (0.706-1.455)0.94NA0.805 (0.526-1.230)0.315NA0.939 (0.740-1.191)0.604NA
Source of controls
 HB10.956 (0.669-1.367)0.806NA0.952 (0.617-1.469)0.825NA0.955 (0.684-1.333)0.787NA0.978 (0.671-1.427)0.909NA0.973 (0.780-1.213)0.806NA
 PB31.023 (0.787-1.331)0.86466.90.856 (0.476-1.538)0.602781.004 (0.742-1.360)0.97877.30.839 (0.522-1.348)0.46872.50.963 (0.754-1.231)0.76581.6
rs2060793 (G/A)21.121 (0.923-1.362)0.24701.184 (0.902-1.554)0.223191.136 (0.946-1.364)0.17201.113 (0.866-1.430)0.4026.31.098 (0.964-1.250)0.168.4
rs12785878 (T/G)51.168 (1.027-1.328)0.0188.31.074 (0.736-1.569)0.7173.11.136 (0.935-1.381) (0.762-1.357)0.9167.51.064 (0.906-1.250)0.44870.1
 Caucasian41.178 (1.021-1.358)0.02430.10.980 (0.562-1.710)0.94479.21.108 (0.854-1.436)0.44164.80.929 (0.584-1.477)0.75673.71.031 (0.814-1.305)0.80277.2
 Asian11.127 (0.836-1.520)0.433NA1.252 (0.931-1.684)0.136NA1.191 (0.898-1.579)0.226NA1.139 (0.954-1.361)0.15NA1.120 (0.980-1.281)0.097NA
Type of cancer
 Breast cancer21.048 (0.756-1.454)0.77850.10.699 (0.341-1.433)0.32863.50.961 (0.658-1.404)0.83863.90.795 (0.616-1.025)0.07742.40.899 (0.665-1.215)0.48866.1
 Hepatocellular carcinoma21.098 (0.851-1.415)0.47201.209 (0.913-1.599)0.18501.135 (0.893-1.442)0.30201.127 (0.947-1.341)0.17701.102 (0.972-1.250)0.1290
 Thyroid caner11.409 (1.068-1.859)0.015NA1.884 (1.297-2.738)0.001NA1.517 (1.167-1.972)0.002NA1.542 (1.102-2.158)0.012NA1.376 (1.150-1.645)<0.001NA
Source of controls
 HB21.098 (0.852-1.415)0.47101.209 (0.913-1.599)0.18501.135 (0.893-1.442)0.30201.127 (0.947-1.341)0.17701.102 (0.972-1.250)0.1290
 PB31.193 (1.028-1.385)0.0249.30.988 (0.501-1.945)0.97185.91.125 (0.815-1.552)0.47475.30.925 (0.528-1.623)0.78782.31.038 (0.777-1.388)0.884.5
rs1790349 (A/G)31.060 (0.850-1.323)0.605521.056 (0.793-1.407)0.7101.043 (0.837-1.300)0.70555.10.998 (0.754-1.319)0.98601.048 (0.942-1.167)0.39145.7
 Caucasian21.201 (1.008-1.431)0.0401.094 (0.784-1.526)0.598441.180 (0.998-1.396)0.05321.21.003 (0.727-1.386)0.98330.61.110 (0.972-1.266)0.12237.4
 Mixed10.920 (0.739-1.145)0.453NA0.957 (0.545-1.677)0.877NA0.923 (0.748-1.140)0.458NA0.980 (0.561-1.712)0.944NA0.940 (0.783-1.128)0.505NA

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).

Comparison typeBegg’s testEgger’s test
value value value value

CYP2R1 rs10741657 (G/A)
Heterozygote vs. homozygote wild01-0.70.521
Homozygote variant vs. homozygote wild0.380.707-0.730.503
Dominant model01-0.530.627
Recessive model01-0.290.787
Allelic model0.750.452-0.380.722
CYP2R1 rs12794714 (G/A)
Heterozygote vs. homozygote wild0.340.7340.210.851
Homozygote variant vs. homozygote wild1.020.308-2.840.105
Dominant model0.340.734-0.010.994
Recessive model1.70.089-9.450.011
Allelic model0.340.734-1.120.38
CYP2R1 rs2060793 (G/A)
Heterozygote vs. homozygote wild01NANA
Homozygote variant vs. homozygote wild01NANA
Dominant model01NANA
Recessive model01NANA
Allelic model01NANA
DHCR7 rs12785878 (T/G)
Heterozygote vs. homozygote wild0.240.806-1.640.2
Homozygote variant vs. homozygote wild-0.241-1.760.177
Dominant model-0.241-1.740.18
Recessive model0.240.806-1.150.332
Allelic model0.240.806-1.560.217
DHCR7 rs1790349 (A/G)
Heterozygote vs. homozygote wild010.180.884
Homozygote variant vs. homozygote wild010.130.92
Dominant model01-0.440.737
Recessive model01-2.340.257
Allelic model1.040.296-0.90.532

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).

GenotypeOR (95% CI) valueStatistical poweraPrior probabilityb

rs12785878 (T/G)
GT vs. TT (overall)1.168 (1.027-1.328)0.0180.3120.2350.3900.8530.9830.998
GT vs. TT (Caucasian)1.178 (1.021-1.358)0.0240.2710.3210.4960.8990.9890.999
GT vs. TT (PB)1.193 (1.028-1.385)0.020.2640.2880.4570.8850.9860.999
rs1790349 (A/G)
GA vs. AA (Caucasian)1.201 (1.008-1.431)0.040.2900.4240.6050.9330.9930.999
rs12794714 (G/A)
AA vs. GG (CRC)0.866 (0.753-0.997)0.0460.3670.4010.5820.9270.9920.999

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.


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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