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. 2024 Feb 28;15:1302527. doi: 10.3389/fgene.2024.1302527

Revealing the association between vitamin D metabolic pathway gene variants and lung cancer risk: a systematic review and meta-analysis

Mohamed I Elsalahaty 1,#, Samar Sami Alkafaas 2,†, Aya O Bashir 3,†, Khaled A El-Tarabily 4,*, Mohamed T El-Saadony 5, Eman H Yousef 6,†
PMCID: PMC10933101  PMID: 38482381

Abstract

Lung cancer is a crucial global issue, with more than one million deaths annually. While smoking is considered the main etiology of the disease, several genetic variants are associated with it. Alterations in vitamin D pathway genes have also been studied in regards to lung cancer, but the findings have been inconclusive. We here present a systematic review and meta-analysis of seven genes in this pathway: CYP2R1, CYP27B1, CYP24A1, CYP3A4, CYP3A5, GC, and VDR. Four databases (PubMed, Scopus, Cochrane Library, and Web of Science (WOS) databases) were searched. From these, 16 eligible case–control studies comprising 6,206 lung cancer cases and 7,272 health controls were obtained. These studies were subjected to comprehensive data extraction and quality scoring, and the pooled odds ratio with a 95% confidence interval was calculated to estimate the effect of each variant along with heterogeneity analysis and a risk of bias assessment. Our meta-analysis revealed an association between CYP3A4 (rs2740574) and lung cancer in the allelic, heterozygous, and dominant models. In addition, both VDR (Fok1: rs2228570) and VDR (Cdx-2: rs11568820) displayed a protective role in lung cancer development in the heterozygous and dominant models. Furthermore, VDR (Taq1: rs731236) showed a decreased risk of lung cancer in the allelic, homozygous, and recessive models. Similarly, VDR (BsmI: rs1544410) had a positive effect on lung cancer risk when subjected to allelic and recessive models. Our meta-analysis revealed the lack of association of CYP2R1 (rs10741657), CYP27B1 (rs3782130), CYP27B1 (rs10877012), CYP24A1 (rs6068816), CYP24A1 (rs4809960), CYP3A5 (rs776746), GC (rs7041), GC (rs4588), and VDR (ApaI: rs7975232) with lung cancer. Our work revealed that CYP3A4 (rs2740574) can represent an independent risk factor for lung cancer. This conclusion can aid better personalized medicine for lung cancer management, while further assessment for genetic variants of CYP3A4, CYP27B1, CYP24A1, GC, and VDR is still required to address more robust evidence.

Keywords: gene variants, lung cancer, polymorphism, vitamin D, vitamin D receptor

1 Introduction

Lung cancer is the most robust lethal carcinoma worldwide, accounting for 1.76 million deaths annually (Bade and Dela Cruz, 2020). Small-cell lung cancer (SCLC) and non-small-cell lung cancer (NSCLC) are two primary histological subtypes of lung cancer. NSCLC accounts for 80%–85% of lung cancer cases and includes adenocarcinoma, squamous cell carcinoma, and large-cell carcinoma (Ben et al., 2021). Smoking has been identified as the leading factor contributing to the incidence of lung cancer. Other risk factors include viral infections, exposure to heavy metals, radiation, asbestos, and air pollution (Li et al., 2019). However, these etiologies rarely account for lung cancer, suggesting that genetic factors play a significant role in its incidence (Zhu et al., 2021). Genome-wide association studies (GWASs) in populations from Europe and Asia have conclusively proven the relationship between genetic polymorphisms and the risk of developing lung cancer (Zhou et al., 2020). Therefore, the identification of vulnerable genes and high-risk populations is one of the main goals of lung cancer research for achieving early prevention and treatment (Wei et al., 2021).

Vitamin D is a seco-steroidal prohormone which is synthesized and metabolized by a series of reactions catalyzed by several enzymes. First, pro-vitamin D, absorbed from food or produced in the skin after exposure to sunlight, is converted by the vitamin D 25-hydroxylase enzyme CYP2R1 in the liver (Janoušek et al., 2022). A summary of this process is shown in Figure 1. The 25(OH) D produced is then converted into 1,25-dihydroxyvitamin D [1.25(OH)2D3] by the 25-hydroxyvitamin D-1α-hydroxylase enzyme that is encoded by the CYP27B1 gene in the kidney. Additionally, 25(OH)D3 can also be converted into 24.25(OH)2D3 by hydroxylation at C-24 through the mitochondrial inner membrane enzyme 1,25-dihydroxyvitamin D (3) 24-hydroxylase encoded by the CYP24A1 gene (Christakos et al., 2015).

FIGURE 1.

FIGURE 1

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Genes involved in the synthesis and metabolism of vitamin D. UV light, ultraviolet light; GC, group-specific component; VDR, vitamin D receptor.

Cytochrome P450 3A4 is a microsomal enzyme encoded by the CYP3A4 gene that plays a plethora of roles in the xenobiotic transformation process of many drugs and endogenous substances. It is involved in the conversion of vitamin D metabolites into their corresponding inactive molecules, such as 4β,25-OH-D3. Additionally, it activates 23R- and 24S-mediated conversion of 1α,25(OH)2D3 into inactive 1α,23R,25(OH)2D3 and 1α,24S,25(OH)2D3 (Jones et al., 2014). Thus, CYP3A4 was extensively studied in the metabolism of vitamin D (Wang et al., 2012; Piotrowska et al., 2019; Kasarla et al., 2022a). Nevertheless, vitamin D inactivation is mainly dependent on CYP3A4, and the cytochrome P450 3A5 enzyme encoded by the CYP3A5 gene catalyzes 23- or 24-hydroxylation of 1,25-(OH)2D3 (Xu et al., 2006; Klyushova et al., 2022).

Additionally, 1,25-dihydroxyvitamin D (3) 24-hydroxylase encoded by the CYP24A1 gene is considered a factor in the vitamin D transformation process and participates in the degradation process of both 25-OH-D3 and 1,25-(OH)2D3 into 24-hydroxylated products in a tissue-dependent manner (Jones et al., 2012; Wang et al., 2013). In fact, it has been suggested that alterations in the vitamin D metabolism result mainly from the induction of hepatic P450 enzymes, including CYP3A4 and CYP24A1 (Wang et al., 2013). Furthermore, in the case of vitamin D toxicity, the liver microsomal enzymes CYP3A4 and CYP3A5 and the extrarenal enzyme CYP24A1 promote the hydroxylation of vitamin D3 into inactive metabolites (Klyushova et al., 2022; Kasarla et al., 2022b). Vitamin D-binding proteins encoded by the GC gene transport the active metabolite 1.25- (OH)2D3 to target tissues where it can bind to the vitamin D receptor gene VDR and regulate physiological genes (Kasarla et al., 2022b).

Vitamin D is involved in a variety of cellular processes such as proliferation, differentiation, metastasis, angiogenesis, and apoptosis (Wei et al., 2018). Vitamin D levels have been linked to the risk of developing several cancers, including breast (Kim and Je, 2014), colorectal (Garland and Gorham, 2017), and prostate (Gilbert et al., 2011). Additionally, a previous meta-analysis has reported an inverse association between serum vitamin D levels and lung cancer risk (Zhang et al., 2015a). Furthermore, it has been found that vitamin D could suppress the metastatic growth of lung cancer cells in animal models (Nakagawa et al., 2005; Zhang et al., 2015b). Therefore, results from previous reports have revealed the potentially preventive role of vitamin D against lung cancer.

Genetic variations in vitamin D genes are potential modulators of their enzymatic functions, levels of expression, and subsequent roles in the susceptibility, progression, and prognosis of lung cancer (Pineda Lancheros et al., 2021; Pineda Lancheros et al., 2022). CYP2R1 gene polymorphisms are linked to an increased risk of NSCLC mortality, particularly in elderly NSCLC patients who have not received treatment. Furthermore, the prognosis of NSCLC may be impacted by several genetic variants connected to the Vitamin D pathway (Kong et al., 2020). Furthermore, the activity of CYP27B1 has been found to be attenuated among some small cell and non-small cell cancer cell lines (Hansdottir et al., 2008). CYP3A4 and CYP3A5 activities as part of liver machinery P450 protein content in the liver account for the metabolism of more than 50% of all drugs and exogenous carcinogens (Ingelman-Sundberg et al., 2007). CYP3A4 and CYP3A5 affect the activation of benzo [a]pyrene (B [a]P), N9-nitrosonornicotine (NNN), aflatoxin B1, stergmatocystin, alpha-hydroxytamoxifen, and procarcinogens present in tobacco smoke (Islam et al., 2014). Altered levels of these toxins are thought to be involved in lung cancer pathogenesis. Increased VDR expression in lung cancer has been correlated with improved survival (Salama et al., 2022). Genetic variants within the VDR gene might potentially influence the binding of 1,25(OH)2D and subsequently alter vitamin D levels and lung cancer pathogenesis (Haznadar et al., 2018). Nevertheless, despite research into the association between vitamin D gene pathway variants and lung cancer, much is still unknown due to a lack of conclusive findings. So this work extensively investigated the genetic variations in the vitamin D pathway, specifically the polymorphisms of CYP2R1, CYP27B1, CYP24A1, CYP3A4, CYP3A5, GC, and VDR genes, to determine their connection with increased risk of developing lung cancer.

2 Materials and methods

2.1 Search strategy

We conducted a systematic review and meta-analysis in accordance with meta-analysis of observational studies in epidemiology (MOOSE) guidelines (Stroup et al., 2000). The study results were reported following Preferred Reporting Items for Systematic Reviews and Meta-Analyses protocols (PRISMA-2020) (Page et al., 2021). A comprehensive literature search of PubMed, Scopus, the Cochrane Library, and Web of Science (WOS) databases to 1 January 2023 was conducted by two authors independently for all relevant articles on the effects of the CYP2R1, CYP27B1, CYP24A1, CYP3A4, CYP3A5, GC, and VDR polymorphisms on lung cancer risk using the following search strategy: (“lung” or “pulmonary”) and (“cancer,” “tumor,” “neoplasm,” or “carcinoma”) and (“polymorphism,” “polymorphic,” “variation,” “variant,” “mutant,” “mutation,” “SNP,” “genotypic,” “genotype,” “allelic,” or “allele”) and (“CYP24A1,” “cytochrome P450 family 24 subfamily A member 1,” “P450-CC24,” “CYP24,” “CP24,” “23-OHase,” “rs6068816,” “rs4809957,” “vitamin D-binding protein,” “vitamin D binding protein,” “Gc-MAF,” “GC,” “DBP,” “VDBP,” “rs7041,” “rs4588,” “CYP2R1,” “25-hydroxylase,” “cytochrome P450 2R1,” “CYP27B1,” “25 hydroxyvitamin D3 1 alpha hydroxylase,” “25-hydroxycholecalciferol 1-hydroxylase,” “calcidiol 1 monooxygenase,” “calcidiol 1-monooxygenase,” “25-hydroxyvitamin D 1-alpha-hydroxylase,” “25 hydroxyvitamin D 1 alpha hydroxylase,” “25-hydroxyvitamin D2 1-hydroxylase,” “1-hydroxylase, 25-hydroxyvitamin D2,” “25 hydroxyvitamin D2 1 hydroxylase,” “cytochrome P-450 CYP27B1,” “cytochrome P 450 CYP27B1,” “25-hydroxycholecalciferol-1-hydroxylase,” “25 hydroxyvitamin D3-1-alpha hydroxylase,” “25-hydroxyvitamin D3 1 alpha-hydroxylase,” “EC 1.14.15.18,” “CYP3A5,” “cytochrome P450, family 3, subfamily A, polypeptide 5,” “cytochrome P450-PCN3,” “cytochrome P450 3A5,” “CYP3A4,” “cytochrome P450 family 3 subfamily A member 4,” “CYP3A3” “cytochrome P450, family 3, subfamily A, polypeptide 4,” “cholesterol 25-hydroxylase,” “albendazole sulfoxidase,” “cytochrome P450 3A4,” “cytochrome P450 3A3,” “nifedipine oxidase,” “EC 1.14.14.1,” “CYPIIIA4,” “taurochenodeoxycholate 6-alpha-hydroxylase,” “glucocorticoid-inducible P450,” “EC 1.14.14.55,” “P450PCN1,” “CYP3A,” “NF-25,” “VDDR3,” “CP33,” “CP34,” “HLP,” “vitamin D receptor,” “vitamin D3 receptor,” “calcitriol receptor,” “1,25-dihydroxyvitamin D3 receptor,” “VDR,” “FokI,” “BsmI,” “ApaI,” or “TaqI”). References cited in recruited articles were manually searched for additional eligible studies. When multiple publications reported the same or overlapping data, we only recruited the publication with complete data or the largest population.

2.2 Inclusion and exclusion criteria

The following inclusion criteria were used to identify the eligible studies for our meta-analysis: 1) case–control studies evaluated the potential association between the CYP2R1, CYP27B1, CYP24A1, CYP3A4, CYP3A5, GC, or VDR polymorphisms and lung risk; 2) studies with sufficient data to calculate an odds ratio (OR) with 95% confidence interval (95% CI); 3) original full-text articles. Otherwise, studies were excluded if they were 1) review articles, meta-analyses, letters, case reports, or articles with abstract only; 2) articles without controls; 3) non-human studies; 4) had duplicated or republished data.

2.3 Quality assessment and data extraction

Two authors independently reviewed and extracted the following baseline information for all included studies: first author’s name, year of publication, country of origin, geographical distribution, source of control [hospital-based (HB) or population-based (PB)], mean/median age of cases and controls, sample size of cases and control, classification of lung cancer, genotyping method, allelic/genotype frequencies of cases and controls, matching criteria, and quality control of genotyping. Potential conflicts were resolved by discussion with two other authors to reach a final consensus. Each study was given quality scores based on certain criteria (Supplementary Table S1) (Xu et al., 2015).

2.4 Statistical analysis

First, the allelic and genotypic frequencies of eligible studies were extracted and calculated from the selected records. HWE within cancer-free controls was calculated by the chi-squared method with a p-value >0.05 representing equilibrium between subjects (Elsalahaty et al., 2023). Next, the crude odds ratios (ORs) and their 95% confidence intervals (CI) were processed to assess the association of included variants with the risk of lung cancer. Different genetic association models were performed, including the allelic, homozygous, heterozygous, dominant, and recessive models (Elshazli et al., 2018). Heterogeneity testing amongst eligible reports was performed using the Q-statistic test along with the I2 index, with a p-value for the Q-test ≤0.10 and I2 index >50%, which signified strong heterogeneity between studies; hence, the random-effects model was chosen (Higgins et al., 2003; El Awady et al., 2022). Publication bias was evaluated using Egger’s regression method and Begg’s funnel plot to ensure a symmetric fashion; the p-value <0.05 was considered significant. We conducted a sensitivity analysis by excluding any included case–control study at a time and then recalculated the significance of the results to detect whether our outcomes were significantly impacted by the presence of each individual study. The present meta-analysis was performed using Comprehensive Meta-analysis version 3.0 (Brüggemann and Rajguru, 2022).

3 Results

3.1 Characteristics of eligible studies

Depending on the keyword-based searching on databases, we acquired 1,769 records from the electronic databases (Figure 2). We removed 103 records due to duplication. The remaining 1,666 were subjected to title and abstract screening, thus excluding 60 records. The 1,606 records were independently screened by two authors, leading to 15 being selected for their eligibility as well as one record obtained from the references of included articles (Figure 2). The research in the 16 articles was performed in ten countries: China (Kong et al., 2015; Wu et al., 2016; Qu et al., 2019; Xiong et al., 2020; Jia et al., 2020), Germany (Dally et al., 2004; Timofeeva et al., 2009), Turkey (Dogan et al., 2009; Çiçek et al., 2017), Norway (Zienolddiny et al., 2008), Bangladesh (Islam et al., 2014), Tunisia (Kaabachi et al., 2014), Thailand (Maneechay et al., 2015), Poland (Gromowski et al., 2017), United States of America (Haznadar et al., 2018), and Spain (Pineda Lancheros et al., 2022). The 16 case–control studies included 6,206 lung cancer cases along with 7,272 healthy controls. The main characteristics of the included studies are presented in Table 1. Additionally, comprehensive data extraction of genotypic and allelic frequencies, along with quality scores for the 16 studies, is summarized in Supplementary Table S2.

FIGURE 2.

FIGURE 2

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Preferred Reporting Items for Systematic Reviews and Meta-Analyses protocols (PRISMA) flow diagram of the search strategy.

TABLE 1.

Main characteristics of the 16 records included in this meta-analysis.

First author Year Country Studied Genes*variants Geographical distribution Ct. source (HB or PB) a Mean age Sample size Classification of disease Genotyping method
Cases Ct Cases Ct
Heike Dally 2003 Germany CYP3A4 (rs2740574)
CYP3A5 (rs776746)		 Europe HB NA b NA 782 428 Mixed c Capillary PCR/fluorescence
Shanbeh Zienolddiny 2008 Norway CYP3A4 (rs2740574) Europe PB 65 (31–85) 60 (50–83) 250 297 NSCLC d Arrayed primer extension
Maria N.Timofeeva 2009 Germany CYP3A4 (rs2740574)
CYP3A5 (rs776746)		 Europe PB 45.1 ± 4.3 45.0 ± 4.4 638 1,300 Mixed Fluorescence-based melting curve analysis
Dogan 2009 Turkey VDR (ApaI: rs7975232; Taq1: rs731236; BsmI: rs1544410) Europe HB NA NA 137 156 Mixed PCR-RFLP e
Mohammad Safiqul Islam 2013 Bangladesh CYP3A4 (rs2740574)
CYP3A5 (rs776746)		 Asia NA 57.87 ± 10.12 58.14 ± 9.77 106 116 Mixed PCR-RFLP
Kaabachi 2014 Tunisia VDR (ApaI: rs7975232; Fok1: rs2228570; Taq1: rs731236; BsmI: rs1544410) Africa NA 58.51 ± 10.25 52.64 ± 6.36 240 280 Mixed PCR-RFLP
Jinyu Kong 2015 China CYP2R1 (rs10741657)
CYP27B1 (rs3782130)
CYP24A1 (rs6068816)
CYP24A1 (rs4809957)
GC (rs7041)
VDR (rs11574129)		 Asia HB 60 (23–83) 60 (23–83) 603 661 NSCLC TaqMan
Wanwisa Maneechay 2015 Thailand GC (rs7041; rs4588) Asia PB 62.8 ± 11.7 62.5 ± 11.5 113 113 Mixed TaqMan
Xiayu Wu 2016 China CYP27B1 (rs3782130)
CYP24A1 (rs4809960)
GC (rs7041; rs4588)
VDR (ApaI: rs7975232; Fok1: rs2228570; Cdx-2: rs11568820; Taq1: rs731236; BsmI: rs1544410)		 Asia HB 57.4 ± 5.8 59.6 ± 4.7 426 445 NSCLC PCR-RFLP
Gromowski 2017 Poland VDR (ApaI: rs7975232; Fok1: rs2228570; Cdx-2: rs11568820; Taq1: rs731236; BsmI: rs1544410) Europe PB 61 (28–88) 61 (28–88) 840 920 Mixed TaqMan
Hülya Kanbur 2017 Turkey VDR (Fok1: rs2228570; BsmI: rs1544410) Europe NA 60.41 ± 11.42 55.71 ± 8.60 59 55 Mixed TaqMan
Ruoyi Qu 2018 China CYP24A1 (rs6068816) Asia HB 56.87 ± 10.31 57.96 ± 10.65 345 351 Mixed TaqMan
Majda Haznadar 2018 United States CYP24A1 (rs2585439; rs3787555; rs3787557; rs2769237; rs6022993; rs8120563; rs10623012; rs2762940; rs2762933; rs2209314) North America PB 66.3 ± 10.0 67.0 ± 8.9 406 437 NSCLC Allele-specific PCR
Zhuoqi Jia 2020 China CYP3A4 (rs3735451; rs4646440; rs35564277; rs4646437) Asia HB 60.75 ± 9.98 60.40 ± 7.39 507 505 NSCLC Mass spectrometry
Qiantao Xiong 2020 China CYP24A1 (rs6068816; rs4809960; rs2585428; rs6022999) Asia HB 58.13 ± 10.25 56.16 ± 10.85 550 800 Mixed Sanger sequencing
Laura Elena 2022 Spain CYP2R1 (rs10741657; rs3782130)
CYP24A1 (rs6068816; rs4809957)
GC (rs7041)
VDR (ApaI: rs7975232; Fok1: rs2228570; Cdx-2: rs11568820; Taq1: rs731236; BsmI: rs1544410)		 Europe HB 61.1 ± 10.7 64 (52–75) 204 408 NSCLC TaqMan
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a

−ΔΔCT. Source (HB or PB). Control subjects’ source, where HB indicates hospital-based control and PB indicates population-based control.

b

NA: not applicable.

c

Mixed: unidentified sub-classification of lung cancer.

d

NSCLC: non-small-cell lung cancer.

e

PCR-RFLP: polymerase chain reaction–restriction fragment length polymorphism.

3.2 Association of CYP2R1, CYP27B1, CYP24A1, CYP3A4, CYP3A5, GC, and VDR with lung cancer polymorphisms in lung cancer risk

All eligible variants for meta-analysis (≥2 records) were subjected to a five-heredity-model-pooled meta-analysis (Duan et al., 2020) to assess their association with lung cancer. Significant gene variants are depicted in Table 2. Our work addressed statistical significance regarding CYP24A1 (rs4809957) with risk of lung cancer under a homozygous model [AA versus GG, OR = 1.788, 95% CI = 1.172–2.727, p-value = 0.007] (Table 2; Supplementary Figure S1A ). However, it revealed a protective impact under the heterozygous model [OR = 0.751, 95% CI = 0.599–0.942 [p-value = 0.013] (Table 2, Supplementary Figure S1B).

TABLE 2.

Meta-analysis of the association of CYP24A1 (rs4809957), CYP3A4 (rs2740574), VDR (Fok1: rs2228570), VDR (Cdx-2: rs11568820), VDR (Taq1: rs731236), and VDR (BsmI: rs1544410) with lung cancer.

Comparison (model) No. a Sample size Test of association Test of heterogeneity Publication bias
Lung cancer Ct b OR 95% CI d p-value Model e , f Q-test p-value I b (%) p-value (Egger’s)
CYP24A1 (rs4809957)
Allelic 2 1,612 2,112 1.048 0.909–1.208 0.521 F 0.094 0.759 0 NA
Homozygous 429 505 1.788 1.172–2.727 0.007 F 2.308 0.129 56.679 NA
Heterozygous 620 796 0.751 0.599–0.942 0.013 F 0.153 0.695 0 NA
Dominant 806 1,056 0.840 0.673–1.047 0.121 F 0.125 0.723 0 NA
Recessive 806 1,056 1.692 0.755–3.792 0.202 R 7.679 0.006 86.977 NA
CYP3A4 (rs2740574)
Allelic 4 3,504 4,182 1.269 1.053–1.530 0.012 F 1.898 0.594 0 0.57276
Homozygous 2 1,524 1859 1.465 0.903–2.377 0.122 F 0.007 0.933 0 NA
Heterozygous 4 1,705 2042 1.316 1.043–1.661 0.021 F 2.78 0.427 0 0.512
Dominant 1,752 2091 1.322 1.054–1.658 0.016 F 2.711 0.438 0 0.54792
Recessive 2 1,752 2091 1.149 0.741–1.782 0.535 F 0.049 0.825 0 NA
VDR (Fok1: rs2228570)
Allelic 5 3,522 4,204 0.896 0.756–1.062 0.207 R 10.318 0.035 61.232 0.771
Homozygous 974 1,104 0.850 0.610–1.186 0.339 R 8.456 0.076 52.695 0.982
Heterozygous 1,465 1,746 0.858 0.744–0.988 0.034 F 5.237 0.264 23.627 0.522
Dominant 1,761 2,102 0.862 0.755–0.986 0.030 F 7.487 0.112 46.572 0.709
Recessive 1,761 2,102 0.961 0.808–1.42 0.649 F 6.893 0.142 41.967 0.948
VDR (Cdx-2: rs11568820)
Allelic 3 2,904 3,424 0.907 0.8–1.027 0.125 F 3.369 0.185 40.643 0.725
Homozygous 885 987 0.828 0.535–1.281 0.397 F 3.363 0.186 40.535 0.094
Heterozygous 1,375 1,626 0.818 0.683–0.978 0.028 F 0.094 0.954 0 0.409
Dominant 1,452 1,712 0.807 0.676–0.962 0.017 F 0.042 0.979 0 0.835
Recessive 1,452 1,712 0.872 0.438–1.736 0.697 R 5.224 0.073 61.718 0.145
VDR (Taq1: rs731236)
Allelic 5 3,662 4,404 0.89 0.804–0.986 0.025 F 6.022 0.198 33.576 0.103
Homozygous 1,140 1,335 0.776 0.618–0.976 0.030 F 4.924 0.295 18.763 0.767
Heterozygous 1,666 1,945 0.852 0.657–1.105 0.227 R 9.239 0.055 56.706 0.132
Dominant 1,831 2,202 0.891 0.774–1.026 0.11 F 7.713 0.103 48.138 0.103
Recessive 1,831 2,202 0.795 0.643–0.984 0.035 F 4.915 0.296 18.617 0.979
VDR (BsmI: rs1544410)
Allelic 6 3,752 4,512 0.724 0.543–0.964 0.027 R 31.501 <0.001 84.128 0.135
Homozygous 1,158 1,357 0.701 0.488–1.007 0.055 R 25.95 <0.001 80.732 0.106
Heterozygous 1,690 1,940 0.753 0.529–1.07 0.114 R 21.379 0.001 76.613 0.069
Dominant 1,876 2,256 0.701 0.488–1.007 0.055 R 25.95 <0.001 80.732 0.106
Recessive 1,876 2,256 0.684 0.473–0.988 0.043 R 12.384 0.03 59.625 0.512
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a

No.: no. of studies involved.

b

−ΔΔCT.: control subjects.

c

OR: odds ratio.

d

95% CI: 95% confidence interval.

e

R: random-effects model.

f

F: fixed-effects model; bold values indicate significance.

Regarding CYP3A4 (rs2740574), the meta-analysis addressed the risk association with lung cancer in the allelic [OR = 1.269, 95% CI 1.053–1.530, p-value = 0.012], heterozygous [OR = 1.316, 95% CI 1.043–1.661, p-value = 0.021], and dominant models [OR = 1.322, 95% CI 1.054–1.658, p-value = 0.016] (Figures 3A 3B,C).

FIGURE 3.

FIGURE 3

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Association between CYP3A4 (rs2740574) and lung cancer risk under (A) allelic, (B) heterozygous, and (C) dominant models. Association between VDR (Fok1: rs2228570) and decreased lung cancer risk under (D) heterozygous and (E) dominant models. Association between VDR (Cdx-2: rs11568820) and decreased lung cancer risk under (F) heterozygous and (G) dominant models. Association between VDR (Taq1: rs731236) and decreased lung cancer risk under (H) allelic, (I) homozygous, and (J) recessive models. Association between VDR (BsmI: rs1544410) and decreased lung cancer risk under (K) allelic and (L) recessive models.

VDR (Fok1: rs2228570) indicated a protective impact within the heterozygous model [OR = 0.858, 95% CI = 0.744–0.988, p-value = 0.034] (Figure 3D), and similar protective findings were found in the dominant model [OR = 0.862, 95% CI = 0.755–0.986, p-value = 0.030] (Figure 3E). VDR (Cdx-2: rs11568820) was found to be associated with protection from lung cancer under the heterozygous model [OR = 0.818, 95% CI = 0.683–978, p-value = 0.028] (Figure 3F), along with a positive impact of the variant under the dominant model [OR = 0.807, 95% CI = 0.676–0.962, p-value = 0.017] (Figure 3G).

VDR (Taq1: rs731236) exercised a protective role through our pooled analysis among allelic [OR = 0.89, 95% CI 0.804–0.986, p-value = 0.025], homozygous [OR = 0.776, 95% CI 0.618–0.976, p-value = 0.030], and recessive models [OR = 0.795, 95% CI 0.643–0.984, p-value = 0.035] (Figures 3H–J).

VDR (BsmI: rs1544410) reduced lung cancer risk in the allelic model [OR = 0.724, 95% CI, p-value], and a similar impact was noticed in the recessive model [OR = 0.684, 95% CI, p-value = 0.043] (Figure 3K and 3L) (Table 2).

Conversely, this meta-analysis revealed the lack of association of CYP2R1 (rs10741657), CYP27B1 (rs3782130), CYP27B1 (rs10877012), CYP24A1 (rs6068816), CYP24A1 (rs4809960), CYP3A5 (rs776746), GC (rs7041), GC (rs4588), and VDR (ApaI: rs7975232) with lung cancer [p-value >0.05] (Supplementary Table S3).

3.3 Heterogeneity analysis

The random-effects model was employed for the analysis to nullify the heterogeneity among studies under the recessive model for CYP24A1 (rs4809957) and CYP27B1 (rs10877012) (Table 2 and Supplementary Table S3. CYP24A1 (rs6068816), CYP24A1 (rs4809960), and GC (rs7041) were subjected to the random-effects model for all genetic models (Supplementary Table S3), as was VDR (BsmI: rs1544410). CYP3A5 (rs776746) in both homozygous and recessive models was subjected to the random-effects model (Supplementary Table S3). VDR (ApaI: rs7975232) heterogeneity was observed in allelic, homozygous, and recessive models (Supplementary Table S3). Lastly, VDR (Cdx-2: rs11568820) and VDR (Taq1: rs731236) showed significant heterogeneity within the recessive and heterozygous models, respectively (Table 2).

3.4 Sensitivity analysis and publication bias

Sensitivity analysis was subsequently executed to evaluate whether our results were substantially affected by one individual study. Each time, we removed one included study to recalculate the significance of the results, and the obtained results showed that the ORs were not significantly changed. The graphs of Begg’s funnel were symmetrically executed, guaranteeing the absence of publication bias among the pooled studies in our meta-analysis (Supplementary Figure S2). Additionally, Egger’s regression test showed a p-value of >0.05 within included studies under all genetic models, addressing the lack of publication bias across our present study.

3.5 Genetic association models of the one-study variants with risk of lung cancer within included studies among CYP27B1, CYP24A1, CYP3A4, GC, and VDR with lung cancer polymorphisms in lung cancer risk

As depicted in Table 3, we subjected data of one-study variants to five heredity models to assess their association with lung cancer. Of the CYP24A1-included variants, CYP24A1 (rs2585439) indicated significance with increased risk for lung cancer under allelic, homozygous, and recessive models [p-value <0.05]. Interestingly, three variants of CYP24A1 showed an increased susceptibility to lung cancer within all genetic models, including rs2762937, rs2762940 and, rs2209314 [p-value <0.05]. Conversely, CYP24A1 (rs6022993), CYP24A1 (rs8120563), and CYP24A1 (rs6068816) revealed a protective role for lung cancer in all genetic models [p-value <0.05]. CYP24A1 (rs8120563) was correlated with increased lung cancer risk in allelic, heterozygous, and dominant models [p-value <0.05], while CYP24A1 (rs2181874) was associated with lung cancer under the allelic model. Lastly, CYP24A1 (rs6022999) indicated significance with lung cancer susceptibility under allelic and homozygous models (Table 3).

TABLE 3.

Genetic association models of one-study variants with the risk of lung cancer within the included studies.

Study Variant Case (N) Ct. (N) Allelic model Homozygous model Heterozygous model Dominant model Recessive model
OR 95% CI p-value OR 95% CI p-value OR 95% CI p-value OR 95% CI p-value OR 95% CI p-value
Laura (2022) rs4646536 203 406 0.919 0.698–1.209 0.557 0.894 0.465–1.716 0.736 0.896 0.624–1.285 0.55 0.895 0.637–1.259 0.525 0.933 0.493–1.765 0.83
rs703842 203 398 1.041 0.790–1.371 0.777 1.21 0.624–2.344 0.573 0.969 0.675–1.391 0.865 1.006 0.715–1.416 0.973 1.224 0.641–2.338 0.540
Majda (2018) rs2585439 366 398 1.277 1.027–1.587 0.028 2.122 1.292–3.484 0.003 0.966 0.714–1.308 0.824 1.137 0.856–1.511 0.375 2.155 1.337–3.472 0.002
rs3787555 364 391 0.919 0.720–1.172 0.495 0.669 0.355–1.260 0.213 1.037 0.761–1.412 0.819 0.971 0.724–1.303 0.846 0.660 0.354–1.233 0.193
rs3787557 365 390 0.932 0.677–1.282 0.665 1.048 0.334–3.287 0.936 0.902 0.628–1.295 0.577 0.912 0.643–1.296 0.609 1.070 0.342–3.347 0.908
rs2762937 364 382 1.496 1.137–1.968 0.004 3.293 1.341–8.084 0.009 1.389 1.002–1.924 0.048 1.493 1.091–2.044 0.012 2.625 1.075–6.405 0.034
rs6022993 374 400 0.383 0.260–0.566 < 0.001 0.168 0.037–0.766 0.021 0.414 0.269–0.639 <0.001 0.383 0.252–0.582 < 0.001 0.190 0.042–0.864 0.032
rs8120563 358 350 0.628 0.399–0.988 0.044 0.923 0.057–14.83 0.955 0.596 0.370–0.962 0.034 0.603 0.376–0.967 0.036 0.978 0.061–15.691 0.987
rs10623012 381 401 1.190 0.975–1.451 0.086 1.378 0.942–2.016 0.099 0.868 0.618–1.219 0.414 1.032 0.754–1.413 0.843 1.504 1.095–2.066 0.012
rs2762940 381 398 1.687 1.318–2.160 < 0.001 3.113 1.596–6.076 0.001 1.522 1.116–2.077 0.008 1.693 1.261–2.274 < 0.001 2.715 1.402–5.258 0.003
rs2762933 377 399 1.342 1.076–1.673 0.009 2.536 1.475–4.358 0.001 1.033 0.767–1.392 0.831 1.213 0.915–1.608 0.18 2.5 1.478–4.230 0.001
rs2209314 368 397 1.584 1.229–2.042 < 0.001 2.933 1.367–6.292 0.006 1.486 1.088–2.029 0.013 1.607 1.192–2.167 0.002 2.58 1.211–5.497 0.014
Xiayu Wu (2016) rs6068816 426 445 0.550 0.453–0.667 < 0.001 0.245 0.149–0.403 < 0.001 0.323 0.235–0.446 < 0.001 0.31 0.227–0.425 < 0.001 0.536 0.345–0.833 0.006
rs2244719 426 445 0.87 0.678–1.115 0.271 0.675 0.398–1.144 0.144 1.049 0.751–1.456 0.778 0.935 0.694–1.26 0.66 0.668 0.396–1.127 0.130
rs2762939 426 445 0.987 0.815–1.195 0.892 1.046 0.704–1.553 0.825 0.851 0.635–1.141 0.281 0.897 0.681–1.183 0.443 1.146 0.8–1.64 0.458
rs2181874 426 445 1.325 1.031–1.703 0.028 1.509 0.911–2.5 0.110 1.24 0.877–1.754 0.224 1.314 0.971–1.78 0.077 1.446 0.877–2.384 0.149
rs2296241 426 445 0.858 0.711–1.036 0.112 0.709 0.48–1.049 0.085 0.971 0.708–1.331 0.853 0.89 0.658–1.2 0.44 0.723 0.52–1.007 0.055
Qiantao (2020) rs2585428 550 800 1.073 0.920–1.251 0.370 1.145 0.846–1.551 0.379 1.155 0.886–1.505 0.288 1.151 0.898–1.477 0.267 1.043 0.814–1.337 0.739
rs6022999 550 800 1.296 1.093–1.537 0.003 2.014 1.354–2.997 0.001 1.102 0.874–1.388 0.412 1.232 0.991–1.531 0.06 1.934 1.316–2.843 0.001
Zhuoqi (2020) rs3735451 506 502 0.984 0.814–1.189 0.867 1.124 0.721–1.751 0.606 0.876 0.675–1.136 0.317 0.916 0.715–1.172 0.484 1.196 0.781–1.832 0.409
rs4646440 506 503 1.652 1.313–2.078 < 0.001 2.638 1.348–5.163 0.005 1.613 1.22–2.131 0.001 1.712 1.311–2.236 < 0.001 2.292 1.177–4.461 0.015
rs35564277 507 503 0.889 0.628–1.257 0.505 1.297 0.289–5.831 0.734 0.84 0.576–1.227 0.368 0.860 0.595–1.244 0.424 1.325 0.295–5.952 0.713
rs4646437 507 505 0.578 0.457–0.732 < 0.001 0.661 0.321–1.363 0.262 0.483 0.364–0.64 < 0.001 0.498 0.380–0.654 < 0.001 0.815 0.397–1.672 0.577
Majda (2018) rs4237855 368 398 1.229 0.996–1.518 0.055 1.654 1.049–2.607 0.03 1.074 0.792–1.456 0.645 1.184 0.889–1.576 0.247 1.596 1.039–2.451 0.033
rs2853559 362 392 1.044 0.84–1.297 0.7 1.064 0.659–1.72 0.799 1.062 0.784–1.438 0.699 1.062 0.798–1.414 0.679 1.035 0.654–1.638 0.882
rs2239184 363 387 1.213 0.988–1.489 0.065 1.324 0.88–1.99 0.178 1.598 1.154–2.212 0.005 1.511 1.115–2.047 0.008 1.012 0.705–1.452 0.951
rs2107301 354 359 1.060 0.827–1.358 0.646 0.649 0.346–1.215 0.176 1.424 1.033–1.963 0.031 1.251 0.925–1.691 0.146 0.574 0.31–1.065 0.078
rs4760658 365 359 0.956 0.762–1.199 0.695 0.882 0.52–1.497 0.643 0.985 0.728–1.334 0.924 0.966 0.726–1.285 0.810 0.888 0.531–1.484 0.65
rs6580642 374 399 0.788 0.587–1.058 0.113 0.831 0.332–2.081 0.693 0.690 0.489–0.976 0.036 0.724 0.520–1.008 0.056 1.190 0.478–2.963 0.708
rs7967152 375 399 1.182 0.966–1.446 0.104 1.270 0.851–1.895 0.242 1.561 1.132–2.152 0.007 1.467 1.086–1.981 0.012 0.98 0.689–1.396 0.912
rs7974353 374 399 0.863 0.657–1.136 0.294 1.075 0.509–2.27 0.849 0.767 0.549–1.071 0.119 0.803 0.586–1.101 0.173 1.149 0.547–2.414 0.714
rs10875693 366 395 1.334 1.067–1.669 0.012 1.245 0.772–2.007 0.368 1.731 1.269–2.361 0.001 1.602 1.202–2.135 0.001 1.002 0.632–1.59 0.992
rs7974708 371 399 1.273 1.021–1.588 0.032 1.115 0.69–1.8 0.657 1.714 1.261–2.328 0.001 1.556 1.170–2.069 0.002 0.894 0.563–1.419 0.634
rs11574101 370 395 1.074 0.742–1.554 0.707 0.726 0.225–2.063 0.548 1.241 0.801–1.921 0.333 1.152 0.765–1.733 0.498 0.707 0.249–2.006 0.515
rs2853563 382 402 0.957 0.67–1.368 0.811 1.248 0.377–4.129 0.717 0.899 0.6–1.347 0.606 0.926 0.628–1.365 0.699 1.267 0.383–4.186 0.698
rs4760733 362 377 1.176 0.958–1.444 0.12 1.324 0.879–1.994 0.18 1.482 1.057–2.077 0.023 1.431 1.042–1.967 0.027 1.036 0.730–1.472 0.842
rs10783218 368 389 0.967 0.683–1.370 0.851 1.069 0.214–5.337 0.935 1.069 0.725–1.576 0.736 1.069 0.731–1.564 0.731 0.525 0.130–2.113 0.364
Jinyu (2015) rs11574129 603 661 1.037 0.86–1.25 0.703 1.239 0.498–3.085 0.645 1.034 0.825–1.295 0.773 1.040 0.832–1.3 0.729 1.222 0.493–3.027 0.665
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Bold values indicate significance.

Regarding the CY3A4 gene, CYP3A4 (rs4646440) indicated substantial significance with the risk of lung cancer within all genetic models [p-value <0.05]. CYP3A4 (rs4646437) revealed a decreased risk under allelic, heterozygous, dominant, and recessive models [p-value <0.05] (Table 3).

VDR (rs4237855) and VDR (rs2107301) showed a significant risk for lung cancer in homozygous and heterozygous models, respectively. VDR (rs2239184), VDR (rs7967152), and VDR (rs4760733) were correlated with lung cancer susceptibility among heterozygous and dominant models [p-value <0.05] (Table 3). Both VDR (rs10875693) and VDR (rs7974708) showed a risk for lung cancer within allelic, heterozygous, and dominant genetic models [p-value <0.05]. VDR (rs6580642) had a protective impact within the heterozygous model (Table 3).

4 Discussion

Lung cancer is a critical problem for human health and is associated with a high rate of mortality. Therefore, its rapid and personalized management is an urgent priority. The well-established roles of vitamin D and genetic variants in the pathogenesis of several types of cancer, including lung cancer, are multifactorial and involve the alteration of transcription, translation, expression, and protein function (Shaw, 2013; Wang et al., 2017; Elsalahaty et al., 2023). Vitamin D has been attributed a favorable role in pulmonary inflammation, and its alteration was thought to be linked to lung cancer progression by modulating the tumor microenvironment and immune function (Prietl et al., 2013). Furthermore, vitamin D metabolites have been found to possess anticancer potency in preclinical models of lung cancer (Shaurova et al., 2018).

While numerous clinical trials have investigated the association of vitamin D gene variants with lung cancer, their findings were not conclusive and were not comprehensively analyzed through different genetic models. Additionally, the most recent studies have not been included. To the best of our knowledge, this is the first systematic review to extract all available data of vitamin D gene variants and subject them to genetic analyses. Our meta-analysis evidence is based on a sum of 6,206 lung cancer cases together with 7,272 healthy controls included in 16 case–control studies among seven genes of the vitamin D pathway (CYP2R1, CYP27B1, CYP24A1, CYP3A4, CYP3A5, GC, and VDR) to assess their association with lung cancer.

Our analysis showed a significant association of CYP24A1 (rs4809957) with an increased risk of lung cancer in the homozygous model and a protective impact within the heterozygous comparison. However, this analysis is limited because of its dependence on data from two available studies. Altered levels of toxins manipulated through the CYP450 machinery, including CYP3A4 and CYP3A5, are thought to participate in the pathogenesis of lung cancer. Our analysis revealed that CYP3A4 (rs2740574) has a significant association with increased lung cancer risk, while CYP3A5 (rs776746) was not correlated with lung cancer risk.

Vitamin D-binding protein encoded by the GC gene is the main transport protein for 25(OH)D and vitamin D metabolites (Speeckaert et al., 2006; Anic et al., 2014). The potential implementation of GC in diseases is thought to be linked to the altered expression of 25(OH)D, actin scavenging, and immunomodulatory roles in macrophage activation and neutrophil chemotaxis (Chun, 2012). Our analysis could not reject the null hypothesis regarding GC (rs7041) and GC (rs4588), and no correlation was observed with the risk of lung cancer in all genetic models. These findings were consistent with those of a previous meta-analysis (Duan et al., 2020).

VDR is tightly linked to vitamin D levels attributed to the modulation of the risk of lung cancer due to the antiproliferative impact of vitamin D. VDR (Apa1: rs7975232) did not reveal a significant impact on lung cancer susceptibility. The same findings were confirmed in a recent meta-analysis (Li et al., 2019; Duan et al., 2020). However, VDR (Fok1: rs2228570) and VDR (Cdx-2: rs11568820) showed significant protection in heterozygous and dominant models. Similarly, Li et al. (2019) obtained a protective impact of Cdx-2: rs11568820 in heterozygous and dominant models relying on two reports. In addition, VDR (Taq1: rs731236) indicated a significant association with a decreased risk of lung cancer in allelic, homozygous, and recessive models. Lastly, VDR (BsmI: rs1544410) indicated protection against lung cancer in the allelic and recessive models.

In contrast, CYP2R1 (rs10741657), CYP27B (rs3782130), CYP27B1 (rs10877012), and CYP24A1 (rs6068816) were not found to be associated with lung cancer. In agreement with Zhu et al. (2019), they did not show a clear association between CYP27B1 (rs10877012) and cancer risk.

Our comprehensive systematic review and data extraction for all included variants, gathered data for one-study variants that were subjected to genetic models. As a result, CYP24A1 (rs2762937), CYP24A1 (rs2762940), and CYP24A1 (rs2209314) showed a correlation with lung cancer susceptibility. In contrast, CYP24A1 (rs6022993), CYP24A1 (rs8120563), and CYP24A1 (rs6068816) revealed a protective role for lung cancer in all genetic models. Our evidence-based study suggests that CYP3A4 (rs2740574) contributes to the development of lung cancer by modulating vitamin D metabolism. It could therefore represent an independent risk factor for developing lung cancer. Genetic testing for this variant in lung cancer patients may be a useful tool for addressing which patients are characterized by higher aggressive characteristics and poorer outcomes, allowing clinicians to choose more personalized and robust treatments early in the course of the disease when treatment is most likely to be effective.

5 Conclusion

This systematic review and meta-analysis addressed the correlation of the CYP3A4 (rs2740574) variant with lung cancer risk. Conversely, VDR (Fok1: rs2228570) and VDR (Cdx-2: rs11568820, Taq1: rs731236, and BsmI: rs1544410) showed protective effects. However, CYP2R1 (rs10741657), CYP27B1 (rs3782130), CYP27B1 (rs10877012), CYP24A1 (rs6068816), CYP24A1 (rs4809960), CYP3A5 (rs776746), GC (rs7041), GC (rs4588), and VDR (ApaI: rs7975232) did not show any significant association with lung cancer. Further assessment of vitamin D pathway genes with lung cancer is required, especially for CYP3A4, CYP27B1, CYP24A1, GC, and VDR, among different ethnicities, to generalize these associations.

Acknowledgments

The authors wholeheartedly thank Associate Prof. Rami M. Elshazli for his clarification of some ambiguity they encountered in their work.

Funding Statement

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This review was funded by the Abu Dhabi Award for Research Excellence, Department of Education and Knowledge (Grant #: 21S105) to KE-T, and by UAEU Program for Advanced Research (UPAR; Grant #: 12S169) to KE-T.

Data availability statement

The original contributions presented in the study are included in the article/Supplementary Material; further inquiries can be directed to the corresponding author.

Author contributions

ME: conceptualization, data curation, formal analysis, investigation, methodology, project administration, resources, software, supervision, validation, writing–original draft, and writing–review and editing. SA: data curation, formal analysis, investigation, methodology, project administration, validation, visualization, writing–original draft, and writing–review and editing. AB: data curation, formal analysis, investigation, methodology, software, validation, writing–original draft, and writing–review and editing. KE-T: conceptualization, data curation, investigation, methodology, software, writing–review and editing, funding acquisition, resources, and visualization. ME-S: data curation, formal analysis, investigation, methodology, software, and writing–review and editing. EY: conceptualization, data curation, formal analysis, investigation, methodology, project administration, software, writing–original draft, and writing–review and editing.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors, and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fgene.2024.1302527/full#supplementary-material

Table1.docx (530.6KB, docx)

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Supplementary Materials

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Data Availability Statement

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