Current studies have explored the correlation between the single nucleotide polymorphisms (SNPs) of pregnane X receptor (PXR) and cancer risk. However, the findings were conflicting. Hence, we performed a comprehensive review and meta-analysis for these researches to determine the effect of PXR polymorphisms on the risk of cancer. Eligible publications were collected based on a series of rigorous inclusion and exclusion criteria. In consequence, a total of eight case–control studies (from seven citations) covering 11143 cases and 12170 controls were involved in a meta-analysis of ten prevalent PXR SNPs (rs10504191 G/A, rs3814058 C/T, rs6785049 A/G, rs1464603 A/G, rs1523127 A/C, rs2276706 G/A, rs2276707 C/T, rs3732360 C/T, rs3814055 C/T, rs3814057 A/C). The correlations between PXR SNPs and cancer risk were estimated by odds ratios (ORs) with their 95% confidence intervals (95%CIs). The findings demonstrated that rs3814058 polymorphism (CT compared with CC: pooled OR = 1.280, P=6.36E-05; TT compared with CC: pooled OR = 1.663, P=2.40E-04; dominant model: pooled OR = 1.382, P=2.58E-08; recessive model: pooled OR = 1.422, P=0.002; T compared with C: pooled OR = 1.292, P=6.35E-05) and rs3814057 polymorphism (AC compared with AA: pooled OR = 1.170, P=0.036; dominant model: pooled OR = 1.162, P=0.037) were associated with the risk of overall cancer. In stratified analyses, rs3814058 polymorphism was revealed to increase the cancer risk in lung cancer subgroup. In summary, this meta-analysis indicates that the rs3814057 and rs3814058 polymorphisms of PXR gene play crucial roles in the pathogenesis of cancer and may be novel biomarkers for cancer-forewarning in overall population or in some particular subgroups.

Introduction

The pregnane X receptor (PXR), also referred to as nuclear receptor subfamily 1 group I member 2 (NR1I2) and steroid and xenobiotic receptor (SXR), is a ligand-dependent transcription factor belonging to the orphan nuclear receptors superfamily [1–3] and plays an essential role in adaptive defense system against endogenous metabolites and toxic xenobiotics [4]. The discovery of the PXR supplied novel perspectives on the molecular basis of the drug resistance in cancer cells [5]. What is more, PXR also participates in regulating the proliferation of either cancer or non-cancer cells. In cancer cells, it can control cell growth in various cancer tissues such as ovarian, prostate, colon, endometrial, breast, and so on [6–10]. Strong associations have been revealed between PXR and the proliferation of cancer [1,4,11].

The PXR gene is located on chromosome 3q12-13.33, spanning 35 kb with ten exons and nine introns. Its coding protein contains a ligand-dependent transactivation function 2, a ligand-binding domain, a hinge region, and a DNA-binding domain [12]. Numerous single nucleotide polymorphisms (SNPs) have been observed in PXR gene and the putatively functional SNPs may influence its expression or function. Currently, accumulating studies have yet investigated the associations between SNPs of PXR and the cancer susceptibility, however, the findings were conflicting. For instance, the rs3814057 polymorphism was related to an elevated cancer risk in our meta-analysis, while it showed no association in Christina Justenhoven’s study [13]. Additionally, no systematic review containing all tested SNPs of PXR has been published yet.

We aim to fill this blank by performing a systematic review and meta-analysis of the available evidence, explore the correlation of PXR SNPs with cancer susceptibility, and provide clues for researchers to design future studies and screen novel functional genetic biomarkers for cancer prediction.

Materials and methods

Retrieval strategy

A comprehensive literature search was performed independently by two investigators (J.W. and Z.L.) to find all publications regarding the correlation between the PXR polymorphisms and cancer risk. We retrieved the PubMed and Web of Science database by using the following query terms: ‘(PXR or pregnane X receptor or NR1I2 or nuclear receptor 1I2 or nuclear receptor subfamily 1 group I member 2 or or SXR or steroid X receptor or ‘steroid and xenobiotic receptor’) and (polymorphism or SNP or variant or variation) and (cancer or tumor or carcinoma or neoplasm)’, up to 16 November 2017.

Inclusion and exclusion criteria

The following inclusion criteria were adopted to identify all eligible publications: (i) a case–control-designed study; (ii) about the association between PXR SNPs and cancer risk. The main exclusion criteria were: (i) duplicate studies; (ii) unrelated to cancer or PXR SNPs; (iii) not sufficient data.

Data extraction

Data extraction was independently completed by two of the investigators (J.W. and Z.L.). Items obtained from each eligible publication included: first author, year of publication (unpublished showed study year), country of origin, cancer type, SNP locus, sample size, genotype counts in cases and controls, Hardy–Weinberg equilibrium (HWE) in controls, source of control groups, genotyping method, and adjusted factors. If an article contained multiple study populations or sources, data were extracted respectively. If data were unreported in eligible articles, we spared no effort to contact the corresponding authors.

Methodology quality assessment

The quality evaluation of the selected studies was scored by two reviewers (J.W. and H.D.) independently, according to a study on assigning quality scores which was mentioned in a previous meta-analysis [14]. A third investigator (X.F.) would be involved when disagreement existed. Six items were evaluated: (i) representativeness of the cases; (ii) source of the controls; (iii) ascertainment of relevant cancers; (iv) sample size; (v) quality control of the genotyping methods; (vi) HWE. The scores for quality assessment ranged from 0 to 10 and studies with less than 5 score were not involved in the subsequent analysis.

Trial sequential analysis

The results of meta-analysis can be misled by random errors (play of chance) or systematic errors (bias) due to sparse data and/or repeated significance testing. Therefore, a trial sequential analysis tool (TSA from Copenhagen Trial Unit, Center for Clinical Intervention Research, Denmark, 2011) was conducted in our meta-analysis to gain more reliable findings [15]. In brief, TSA evaluates the required information size by setting type-I error of 5%, type-II error of 20%, and statistical test power of 80%, and then plots a two-sided graph, where TSA monitoring boundaries (red lines) were built [16]. If the TSA monitoring boundaries were crossed with Z-curve (blue lines) before reaching the required information size, robust conclusion might have been identified and further studies are unnecessary [16]. Otherwise, more trials are still in demand.

False-positive report probability

We evaluated the significant findings by computing false-positive report probability (FPRP), which was based on observed P-value, statistical power of test, and prior probability [17]. To identify a significant association as ‘noteworthy’, prior probabilities of 0.25, 0.01, 0.001, 0.0001 were assigned and 0.2 was set as FPRP cut-off value [18].

Statistical analysis

All the statistical analyses in the present study were performed by STATA software, version 11.0 (STATA Corp., College Station, TX, U.S.A.). All tests were two-sided and P-value <0.05 was considered as a statistical significance level unless we highlighted once more. The dominant genetic model was defined as homozygote + heterozygote variant compared with homozygote wild, while the recessive genetic model was defined as homozygote variant compared with homozygote + heterozygote wild. The HWE for the genotype distributions of PXR SNPs in controls was calculated by chi-square test, and P-values <0.05 was considered as significant disequilibrium. The intensity of the relations between the PXR SNPs and cancer risk was evaluated by pooled odds ratios (ORs) and 95% confidence intervals (95%CIs), calculated by fixed effect model [19] when the between-study heterogeneity was absent, otherwise random effect models [20]. The between-study heterogeneity was calculated by Cochran’s Q-test (significance at I2> 50%). Begg’s test, a funnel plot analysis and Egger’s linear regression analysis were conducted to calculate the publication bias. P-value <0.10 was considered as statistically significant in both Begg’s or Egger’s test. What is more, sensitivity analyses were performed to inspect whether the summary findings were robust after excluding one or two outlying studies.

Results

Characteristics of the eligible studies

According to the selection process showed in Figure 1, total 102 publications were collected through database searching. Ninety-five records were excluded after reading titles and abstracts (38 were functional studies; 11 were reviews; 2 were not case–control studies; 7 were not related to PXR SNPs; 14 were not correlated with cancer; 23 were not associated with cancer risks). Hence, total eight case–control studies (from seven citations) covering 11143 cases and 12170 controls were involved in our meta-analyses, which met the inclusion criteria and the quality assessment. Moreover, the genotype distributions of all records were in agreement with HWE (PHWE>0.05). The characteristics of these included articles were shown in Table 1 and the distributions of PXR SNPs genotype frequency were reported in Table 2.

The flow chart of identification for studies included in the meta-analysis

Figure 1
The flow chart of identification for studies included in the meta-analysis
Figure 1
The flow chart of identification for studies included in the meta-analysis
Table 1
The main features of enrolled studies
Ref. No. Year Country Ethnicity Sample size Source of controls Genotyping method Adjusted factors Quality score Citation 
    Case Control      
2008 China/Malay/Indian Asian 62 300 PB Applied Biosystems 3730 DNA Analyzer NM [5
2010 Germany Asian 2984 5318 PB MALDI-TOF MS Age, study region, family history of breast cancer, and BMI 10 [21
2011 Germany Caucasian 1020 1014 PB MALDI-TOF MS Age, menopausal status, family history of breast cancer, body mass index, and smoking 8.5 [13
2011 Germany Caucasian 678 669 PB KASPar assays Age, sex, body mass index, and physical activity in METs 6.5 [22
2014 China Asian 1056 1056 HB TaqMan Age and gender [4
2014 China Asian 503 623 HB TaqMan Age and gender [4
2014 Mexican Mixed 99 144 HB TaqMan Age and marital status 6.5 [23
2015 China Asian 1033 1147 HB MALDI-TOF MS Age, sex, BMI, and family history of cancer [24
Ref. No. Year Country Ethnicity Sample size Source of controls Genotyping method Adjusted factors Quality score Citation 
    Case Control      
2008 China/Malay/Indian Asian 62 300 PB Applied Biosystems 3730 DNA Analyzer NM [5
2010 Germany Asian 2984 5318 PB MALDI-TOF MS Age, study region, family history of breast cancer, and BMI 10 [21
2011 Germany Caucasian 1020 1014 PB MALDI-TOF MS Age, menopausal status, family history of breast cancer, body mass index, and smoking 8.5 [13
2011 Germany Caucasian 678 669 PB KASPar assays Age, sex, body mass index, and physical activity in METs 6.5 [22
2014 China Asian 1056 1056 HB TaqMan Age and gender [4
2014 China Asian 503 623 HB TaqMan Age and gender [4
2014 Mexican Mixed 99 144 HB TaqMan Age and marital status 6.5 [23
2015 China Asian 1033 1147 HB MALDI-TOF MS Age, sex, BMI, and family history of cancer [24

Abbreviations: HB, hospital based; KASPar assay, KBioscience’s competitive allele-specific PCR amplification; MALDI-TOF MS, matrix-assisted laser desorption/ionization time-of-flight mass spectrometry using the Sequenom MassARRAY platform and iPLEX GOLD methodology; NM, not mentioned; PB, population based.

Table 2
Genotype frequency distributions of PXR SNPs in included studies
Ref. No. Year Cancer type SNPs1 Sample size Case Control PHWE Included in meta-analysis 
    Case Control Homozygote wild Heterozygote Homozygote variant Homozygote wild Heterozygote Homozygote variant   
2008 Breast cancer rs3814055 (C/T) 62 300 36 23 176 106 18 0.702 Yes 
  Breast cancer rs1523127 (A/C) 62 300 36 24 170 107 23 0.289 Yes 
  Breast cancer rs2276706 (G/A) 62 300 37 23 176 105 19 0.533 Yes 
  Breast cancer rs3732358 (G/A) 62 300 62 295 0.884 No3 
  Breast cancer rs3732359 (A/G) 62 300 11 28 23 101 125 74 0.006 No2,3 
  Breast cancer rs3732360 (C/T) 62 300 11 28 23 102 124 74 0.004 No2,3 
  Breast cancer rs6438550 (A/G) 62 300 50 12 216 76 0.674 No3 
  Breast cancer rs3814057 (A/C) 59 300 18 27 14 125 127 48 0.105 Yes 
  Breast cancer rs3814058 (C/T) 59 300 18 27 14 125 127 48 0.105 Yes 
2010 Breast cancer rs6785049 (A/G) 2984 5318 1176 1382 426 2036 2476 806 0.238 Yes 
  Breast cancer rs10504191 (G/A) 2982 5315 2216 713 53 3942 1260 113 0.297 Yes 
2011 Colorectal cancer rs1523127 (A/C) 663 669 258 317 88 245 326 98 0.534 Yes 
  Colorectal cancer rs2276706 (G/A) 674 675 267 324 83 251 329 95 0.438 Yes 
  Colorectal cancer rs1464603 (A/G) 676 678 307 291 78 303 310 65 0.263 Yes 
  Colorectal cancer rs6785049 (A/G) 678 677 264 313 101 260 323 94 0.692 Yes 
  Colorectal cancer rs2276707 (C/T) 653 647 439 190 24 446 180 21 0.588 Yes 
  Colorectal cancer rs10504191 (G/A) 673 677 518 143 12 499 161 17 0.356 Yes 
  Colorectal cancer rs3814057 (A/C) 665 657 440 201 24 458 177 22 0.341 Yes 
2011 Breast cancer rs3814055 (C/T) 1020 1014 383 487 150 384 497 133 0.159 Yes 
  Breast cancer rs1523127 (A/C) 1020 1013 386 479 155 390 483 140 0.623 Yes 
  Breast cancer rs2276706 (G/A) 1020 1014 388 482 150 400 485 129 0.336 Yes 
  Breast cancer rs1464603 (A/G) 1019 1013 484 446 89 467 451 95 0.352 Yes 
  Breast cancer rs6785049 (A/G) 1020 1012 421 471 128 391 486 135 0.406 Yes 
  Breast cancer rs2276707 (C/T) 1018 1013 682 310 26 690 292 31 0.987 Yes 
  Breast cancer rs10504191 (G/A) 1020 1013 767 235 18 754 239 20 0.835 Yes 
  Breast cancer rs3814057 (A/C) 1020 1009 687 308 25 703 277 29 0.786 Yes 
2014 Lung cancer rs3814055 (C/T) 1056 1056 693 328 35 706 316 34 0.851 Yes 
  Lung cancer rs3732360 (C/T) 1056 1056 347 520 189 346 533 177 0.242 Yes 
  Lung cancer rs3814058 (C/T) 1056 1056 315 505 236 365 491 200 0.128 Yes 
2014 Lung cancer rs3814058 (C/T) 503 623 122 254 127 185 303 135 0.600 Yes 
2014 Prostate cancer rs2472677 (T/C) 99 144 40 43 16 50 72 22 0.637 Noc 
  Prostate cancer rs7643645 (G/A) 99 144 21 45 33 26 75 43 0.499 Noc 
2015 Colorectal cancer rs3732360 (C/T) 1033 1147 362 519 152 434 560 153 0.189 Yes 
  Colorectal cancer rs3814058 (C/T) 1033 1147 282 511 240 421 561 165 0.318 Yes 
Ref. No. Year Cancer type SNPs1 Sample size Case Control PHWE Included in meta-analysis 
    Case Control Homozygote wild Heterozygote Homozygote variant Homozygote wild Heterozygote Homozygote variant   
2008 Breast cancer rs3814055 (C/T) 62 300 36 23 176 106 18 0.702 Yes 
  Breast cancer rs1523127 (A/C) 62 300 36 24 170 107 23 0.289 Yes 
  Breast cancer rs2276706 (G/A) 62 300 37 23 176 105 19 0.533 Yes 
  Breast cancer rs3732358 (G/A) 62 300 62 295 0.884 No3 
  Breast cancer rs3732359 (A/G) 62 300 11 28 23 101 125 74 0.006 No2,3 
  Breast cancer rs3732360 (C/T) 62 300 11 28 23 102 124 74 0.004 No2,3 
  Breast cancer rs6438550 (A/G) 62 300 50 12 216 76 0.674 No3 
  Breast cancer rs3814057 (A/C) 59 300 18 27 14 125 127 48 0.105 Yes 
  Breast cancer rs3814058 (C/T) 59 300 18 27 14 125 127 48 0.105 Yes 
2010 Breast cancer rs6785049 (A/G) 2984 5318 1176 1382 426 2036 2476 806 0.238 Yes 
  Breast cancer rs10504191 (G/A) 2982 5315 2216 713 53 3942 1260 113 0.297 Yes 
2011 Colorectal cancer rs1523127 (A/C) 663 669 258 317 88 245 326 98 0.534 Yes 
  Colorectal cancer rs2276706 (G/A) 674 675 267 324 83 251 329 95 0.438 Yes 
  Colorectal cancer rs1464603 (A/G) 676 678 307 291 78 303 310 65 0.263 Yes 
  Colorectal cancer rs6785049 (A/G) 678 677 264 313 101 260 323 94 0.692 Yes 
  Colorectal cancer rs2276707 (C/T) 653 647 439 190 24 446 180 21 0.588 Yes 
  Colorectal cancer rs10504191 (G/A) 673 677 518 143 12 499 161 17 0.356 Yes 
  Colorectal cancer rs3814057 (A/C) 665 657 440 201 24 458 177 22 0.341 Yes 
2011 Breast cancer rs3814055 (C/T) 1020 1014 383 487 150 384 497 133 0.159 Yes 
  Breast cancer rs1523127 (A/C) 1020 1013 386 479 155 390 483 140 0.623 Yes 
  Breast cancer rs2276706 (G/A) 1020 1014 388 482 150 400 485 129 0.336 Yes 
  Breast cancer rs1464603 (A/G) 1019 1013 484 446 89 467 451 95 0.352 Yes 
  Breast cancer rs6785049 (A/G) 1020 1012 421 471 128 391 486 135 0.406 Yes 
  Breast cancer rs2276707 (C/T) 1018 1013 682 310 26 690 292 31 0.987 Yes 
  Breast cancer rs10504191 (G/A) 1020 1013 767 235 18 754 239 20 0.835 Yes 
  Breast cancer rs3814057 (A/C) 1020 1009 687 308 25 703 277 29 0.786 Yes 
2014 Lung cancer rs3814055 (C/T) 1056 1056 693 328 35 706 316 34 0.851 Yes 
  Lung cancer rs3732360 (C/T) 1056 1056 347 520 189 346 533 177 0.242 Yes 
  Lung cancer rs3814058 (C/T) 1056 1056 315 505 236 365 491 200 0.128 Yes 
2014 Lung cancer rs3814058 (C/T) 503 623 122 254 127 185 303 135 0.600 Yes 
2014 Prostate cancer rs2472677 (T/C) 99 144 40 43 16 50 72 22 0.637 Noc 
  Prostate cancer rs7643645 (G/A) 99 144 21 45 33 26 75 43 0.499 Noc 
2015 Colorectal cancer rs3732360 (C/T) 1033 1147 362 519 152 434 560 153 0.189 Yes 
  Colorectal cancer rs3814058 (C/T) 1033 1147 282 511 240 421 561 165 0.318 Yes 

Abbreviation: PHWE, the P-value for HWE in control groups. The results are in bold if P<0.05.

1, The ancestral alleles were referenced in the NCBI database.

2, Excluded due to the SNP not being in accordance with HWE.

3, Excluded due to the limited number for this locus.

In general, obtained from eight eligible case–control studies, ten SNPs were involved in our final analysis including: rs10504191 G/A, rs3814058 C/T, rs6785049 A/G, rs1464603 A/G, rs1523127 A/C, rs2276706 G/A, rs2276707 C/T, rs3732360 C/T, rs3814055 C/T, rs3814057 A/C. Of these ten SNPs, the most prevalent one was rs3814058 with four articles encompassing 2651 cases and 3123 controls in Asian population. For rs10504191, rs6785049, rs1523127, rs2276706, rs3814055, and rs3814057 polymorphisms, three case–control studies were enrolled. Other polymorphisms were only investigated in two case–control studies.

Quantitative data synthesis of ten PXR SNPs

We analyzed the associations between each PXR SNP and cancer risk, based on the whole population or two subgroup population stratified by ethnicity or cancer type, respectively. The stratified analyses were performed due to the existence of between-study heterogeneity. In whole population analyses, two (rs3814058 and rs3814057) of the ten SNPs were illustrated to be associated with cancer risk, while others did not show remarkable relations. Moreover, in subgroup analyses, seven SNPs (rs10504191, rs3814058, rs6785049, rs1523127, rs2276706, rs3814055 and rs3814057) were analyzed in ‘cancer type’ subgroup and four SNPs (rs1523127, rs2276706, rs3814055, and rs3814057) were analyzed in ‘ethnicity’ subgroup. However, only rs3814058 showed its association in lung cancer subgroup.

The PXR rs3814058 C/T polymorphism

For rs3814058 C/T, its heterozygote genotype, homozygote variant genotype, dominant, recessive, and allelic models were all correlated with an elevated risk of cancer in Asian population (CT compared with CC: pooled OR = 1.280, 95%CI = 1.134–1.445, P=6.36E-05; TT compared with CC: pooled OR = 1.663, 95%CI = 1.268–2.182, P=2.40E-04 dominant model: pooled OR = 1.382, 95%CI = 1.233–1.549, P=2.58E-08; recessive model: pooled OR = 1.422, 95%CI = 1.132–1.786, P=0.002; T compared with C: pooled OR = 1.292, 95%CI = 1.140–1.465, P=6.35E-05). Moreover, the same effect could also be found in lung cancer subgroup analysis (CT compared with CC: OR = 1.271, 95%CI = 1.036–1.429, P=0.017; TT compared with CC: OR = 1.387, 95%CI = 1.141–1.687, P=0.001; dominant model: OR = 1.267, 95%CI = 1.089–1.473, P=0.002; recessive model: OR = 1.228, 95%CI = 1.038–1.452, P=0.017; T compared with C: OR = 1.186, 95%CI = 1.075–1.308, P=0.001, Table 3).

Table 3
Meta-analysis of the association between PXR polymorphisms and cancer risk
SNPs n Heterozygote compared with homozygote wild Homozygote variant compared with homozygote wild Dominant model Recessive model Allelic model 
  P OR (95%CI) I2 (%) P OR (95%CI) I2 (%) P OR (95%CI) I2 (%) P OR (95%CI) I2 (%) P OR (95%CI) I2 (%) 
rs10504191 (G/A) 0.656 0.980 (0.897–1.071) 0.157 0.820 (0.624–1.079) 0.441 0.967 (0.887–1.053) 0.166 0.825 (0.628–1.083) 0.277 0.958 (0.887–1.035) 
Cancer type                 
Breast cancer 0.97 0.998 (0.909–1.097) 0.259 0.844 (0.629–1.133) 0.757 0.986 (0.900–1.080) 0.259 0.845 (0.630–1.132) 0.549 0.975 (0.899–1.058) 
Colorectal cancer 0.234 0.856 (0.662–1.106) NA 0.313 0.680 (0.321–1.438) NA 0.165 0.839 (0.655–1.075) NA 0.359 0.705 (0.334–1.487) NA 0.129 0.842 (0.674–1.051) NA 
rs3814058 (C/T) 6.36E-05 1.280 (1.134–1.445) 2.40E-041 1.663 (1.268–2.182) 62.5 2.58E-08 1.382 (1.233–1.549) 4.1 0.0021 1.422 (1.132–1.786) 60.3 6.35E-051 1.292 (1.140–1.465) 56.9 
Cancer type                 
Lung cancer 0.017 1.271 (1.036–1.429) 0.001 1.387 (1.141–1.687) 0.002 1.267 (1.089–1.473) 0.017 1.228 (1.038–1.452) 0.001 1.186 (1.075–1.308) 
Breast cancer 0.237 1.476 (0.774–2.815)  0.074 2.025 (0.934–4.391) NA 0.112 1.627 (0.893–2.964) NA 0.154 1.633 (0.832–3.207) NA 0.055 1.476 (0.992–2.197) NA 
Colorectal cancer 0.002 1.360 (1.122–1.649) NA <0.001 2.172 (1.693–2.786) NA <0.001 1.544 (1.287–1.853) NA <0.001 1.801 (1.447–2.243) NA <0.001 1.452 (1.287–1.637) NA 
rs6785049 (A/G) 0.235 0.952 (0.878–1.032) 0.188 0.925 (0.825–1.039) 0.152 0.946 (0.876–1.021) 0.345 0.950 (0.854–1.057) 0.133 0.959 (0.908–1.013) 
Cancer type                 
Breast cancer 0.262 0.952 (0.873–1.038) 0.126 0.908 (0.803–1.027) 0.146 0.941 (0.867–1.021) 0.228 0.932 (0.832–1.045) 0.098 0.952 (0.898–1.009) 
Colorectal cancer 0.692 0.954 (0.757–1.203) NA 0.736 1.058 (0.762–1.470) NA 0.84 0.978 (0.786–1.217) NA 0.596 1.086 (0.801–1.471) NA 0.898 1.010 (0.865–1.180) NA 
rs1464603 (A/G) 0.418 0.943 (0.818–1.087) 0.904 1.015 (0.799–1.288) 16.8 0.51 0.956 (0.835–1.094) 0.698 1.046 (0.833–1.314) 31.9 0.746 0.983 (0.888–1.089) 
rs1523127 (A/C) 0.731 0.975 (0.846–1.125) 0.872 0.983 (0.800–1.209) 30.9 0.73 0.976 (0.853–1.118) 0.99 1.001 (0.827–1.211) 27.9 0.811 0.988 (0.898–1.088) 
Cancer type                 
Breast cancer 0.935 1.008 (0.842–1.206) 0.605 1.072 (0.825–1.393) 40.9 0.82 1.020 (0.860–1.209) 0.561 1.074 (0.844–1.368) 44.9 0.649 1.029 (0.911–1.162) 
Colorectal cancer 0.503 0.923 (0.731–1.166) NA 0.354 0.853 (0.609–1.194) NA 0.388 0.907 (0.727–1.132) NA 0.469 0.892 (0.654–1.216) NA 0.33 0.925 (0.791–1.082) NA 
Ethnicity                 
Caucasian 0.685 0.970 (0.838–1.124) 0.951 1.007 (0.816–1.241) 34.5 0.76 0.979 (0.851–1.125) 0.812 1.024 (0.844–1.242) 19.5 0.925 0.995 (0.902–1.098) 27.8 
Asian 0.843 1.059 (0.599–1.874) 0.241 0.411 (0.093–1.820) NA 0.84 0.944 (0.543–1.643) NA 0.224 0.401 (0.092–1.749) NA 0.495 0.852 (0.538–1.349) NA 
                 
rs2276706 (G/A) 0.859 0.987 (0.857–1.137) 0.888 1.015 (0.823–1.253) 46.7 0.915 0.993 (0.868–1.135) 20.7 0.799 1.026 (0.844–1.246) 41 0.96 1.002 (0.910–1.104) 27.3 
Cancer type                 
Breast cancer 0.777 1.026 (0.858–1.227) 0.286 1.157 (0.885–1.511) 20.8 0.565 1.051 (0.887–1.246) 0.283 1.146 (0.894–1.468) 23.2 0.344 1.061 (0.939–1.199) 
Colorectal cancer 0.512 0.926 (0.735–1.166) NA 0.258 0.821 (0.584–1.155) NA 0.359 0.902 (0.725–1.124) NA 0.34 0.857 (0.625–1.176) NA 0.261 0.914 (0.782–1.069) NA 
Ethnicity                 
Caucasian 0.827 0.984 (0.850–1.138) 0.9721 1.007 (0.696–1.456) 65.2 0.942 0.995 (0.866–1.143) 0.8931 1.022 (0.746–1.399) 58.8 0.9811 0.998 (0.852–1.169) 59.8 
Asian 0.888 1.042 (0.587–1.849) NA 0.366 0.501 (0.112–2.243) NA 0.883 0.959 (0.549–1.674) NA 0.35 0.493 (0.112–2.173) NA 0.623 0.890 (0.558–1.418) NA 
rs2276707 (C/T) 0.356 1.073 (0.924–1.248) 0.896 0.974 (0.655–1.449) 0.405 1.064 (0.920–1.230) 0.813 0.954 (0.643–1.415) 0.518 1.042 (0.919–1.182) 
rs3732360 (C/T) 0.537 1.043 (0.913–1.190) 0.212 1.123 (0.936–1.349) 0.346 1.062 (0.937–1.205) 0.257 1.100 (0.933–1.298) 0.215 1.056 (0.969–1.151) 
rs3814055 (C/T) 0.745 1.022 (0.898–1.163) 0.431 1.098 (0.870–1.387) 0.593 1.034 (0.914–1.171) 0.373 1.105 (0.888–1.375) 0.421 1.040 (0.945–1.145) 
Ethnicity                 
Asian 0.535 1.058 (0.886–1.263) 0.951 1.014 (0.647–1.590) 0.551 1.054 (0.887–1.251) 0.982 0.995 (0.637–1.554) 0.615 1.038 (0.897–1.203) 
Caucasian 0.854 0.982 (0.813–1.187) NA 0.378 1.131 (0.861–1.486) NA 0.881 1.014 (0.847–1.213) NA 0.301 1.142 (0.888–1.469) NA 0.531 1.041 (0.918–1.182) NA 
Cancer type                 
Breast cancer 0.91 0.990 (0.827–1.184) 0.428 1.114 (0.853–1.453) 0.866 1.015 (0.856–1.204) 0.348 1.125 (0.880–1.439) 0.557 1.037 (0.918–1.172) 
Lung cancer 0.558 1.057 (0.877–1.274) NA 0.847 1.049 (0.647–1.701) NA 0.55 1.057 (0.882–1.265) NA 0.903 1.030 (0.638–1.665) NA 0.579 1.045 (0.895–1.220) NA 
rs3814057 (A/C) 0.036 1.170 (1.010–1.355) 0.457 1.145 (0.802–1.634) 32.6 0.037 1.162 (1.009–1.339) 0.656 1.082 (0.766–1.527) 9.1 0.053 1.127 (0.999–1.271) 
Ethnicity                 
Caucasian 0.061 1.155 (0.993–1.343) 0.961 0.990 (0.663–1.478) 0.081 1.138 (0.984–1.317) 0.795 0.948 (0.637–1.412) 0.152 1.097 (0.966–1.245) 
Asian 0.237 1.476 (0.774–2.815) NA 0.074 2.025 (0.934–4.391) NA 0.112 1.627 (0.893–2.964) NA 0.154 1.633 (0.832–3.207) NA 0.055 1.476 (0.992–2.197) NA 
Cancer type                 
Breast cancer 0.11 1.163 (0.966–1.399) 0.5561 1.275 (0.567–2.865) 66.3 0.117 1.154 (0.965–1.379) 28.5 0.6871 1.141 (0.602–2.160) 54.5 0.251 1.191 (0.884–1.605) 53.6 
Colorectal cancer 0.173 1.182 (0.929–1.504) NA 0.674 1.136 (0.627–2.055) NA 0.167 1.177 (0.934–1.483) NA 0.796 1.081 (0.600–1.947) NA 0.201 1.139 (0.933–1.391) NA 
SNPs n Heterozygote compared with homozygote wild Homozygote variant compared with homozygote wild Dominant model Recessive model Allelic model 
  P OR (95%CI) I2 (%) P OR (95%CI) I2 (%) P OR (95%CI) I2 (%) P OR (95%CI) I2 (%) P OR (95%CI) I2 (%) 
rs10504191 (G/A) 0.656 0.980 (0.897–1.071) 0.157 0.820 (0.624–1.079) 0.441 0.967 (0.887–1.053) 0.166 0.825 (0.628–1.083) 0.277 0.958 (0.887–1.035) 
Cancer type                 
Breast cancer 0.97 0.998 (0.909–1.097) 0.259 0.844 (0.629–1.133) 0.757 0.986 (0.900–1.080) 0.259 0.845 (0.630–1.132) 0.549 0.975 (0.899–1.058) 
Colorectal cancer 0.234 0.856 (0.662–1.106) NA 0.313 0.680 (0.321–1.438) NA 0.165 0.839 (0.655–1.075) NA 0.359 0.705 (0.334–1.487) NA 0.129 0.842 (0.674–1.051) NA 
rs3814058 (C/T) 6.36E-05 1.280 (1.134–1.445) 2.40E-041 1.663 (1.268–2.182) 62.5 2.58E-08 1.382 (1.233–1.549) 4.1 0.0021 1.422 (1.132–1.786) 60.3 6.35E-051 1.292 (1.140–1.465) 56.9 
Cancer type                 
Lung cancer 0.017 1.271 (1.036–1.429) 0.001 1.387 (1.141–1.687) 0.002 1.267 (1.089–1.473) 0.017 1.228 (1.038–1.452) 0.001 1.186 (1.075–1.308) 
Breast cancer 0.237 1.476 (0.774–2.815)  0.074 2.025 (0.934–4.391) NA 0.112 1.627 (0.893–2.964) NA 0.154 1.633 (0.832–3.207) NA 0.055 1.476 (0.992–2.197) NA 
Colorectal cancer 0.002 1.360 (1.122–1.649) NA <0.001 2.172 (1.693–2.786) NA <0.001 1.544 (1.287–1.853) NA <0.001 1.801 (1.447–2.243) NA <0.001 1.452 (1.287–1.637) NA 
rs6785049 (A/G) 0.235 0.952 (0.878–1.032) 0.188 0.925 (0.825–1.039) 0.152 0.946 (0.876–1.021) 0.345 0.950 (0.854–1.057) 0.133 0.959 (0.908–1.013) 
Cancer type                 
Breast cancer 0.262 0.952 (0.873–1.038) 0.126 0.908 (0.803–1.027) 0.146 0.941 (0.867–1.021) 0.228 0.932 (0.832–1.045) 0.098 0.952 (0.898–1.009) 
Colorectal cancer 0.692 0.954 (0.757–1.203) NA 0.736 1.058 (0.762–1.470) NA 0.84 0.978 (0.786–1.217) NA 0.596 1.086 (0.801–1.471) NA 0.898 1.010 (0.865–1.180) NA 
rs1464603 (A/G) 0.418 0.943 (0.818–1.087) 0.904 1.015 (0.799–1.288) 16.8 0.51 0.956 (0.835–1.094) 0.698 1.046 (0.833–1.314) 31.9 0.746 0.983 (0.888–1.089) 
rs1523127 (A/C) 0.731 0.975 (0.846–1.125) 0.872 0.983 (0.800–1.209) 30.9 0.73 0.976 (0.853–1.118) 0.99 1.001 (0.827–1.211) 27.9 0.811 0.988 (0.898–1.088) 
Cancer type                 
Breast cancer 0.935 1.008 (0.842–1.206) 0.605 1.072 (0.825–1.393) 40.9 0.82 1.020 (0.860–1.209) 0.561 1.074 (0.844–1.368) 44.9 0.649 1.029 (0.911–1.162) 
Colorectal cancer 0.503 0.923 (0.731–1.166) NA 0.354 0.853 (0.609–1.194) NA 0.388 0.907 (0.727–1.132) NA 0.469 0.892 (0.654–1.216) NA 0.33 0.925 (0.791–1.082) NA 
Ethnicity                 
Caucasian 0.685 0.970 (0.838–1.124) 0.951 1.007 (0.816–1.241) 34.5 0.76 0.979 (0.851–1.125) 0.812 1.024 (0.844–1.242) 19.5 0.925 0.995 (0.902–1.098) 27.8 
Asian 0.843 1.059 (0.599–1.874) 0.241 0.411 (0.093–1.820) NA 0.84 0.944 (0.543–1.643) NA 0.224 0.401 (0.092–1.749) NA 0.495 0.852 (0.538–1.349) NA 
                 
rs2276706 (G/A) 0.859 0.987 (0.857–1.137) 0.888 1.015 (0.823–1.253) 46.7 0.915 0.993 (0.868–1.135) 20.7 0.799 1.026 (0.844–1.246) 41 0.96 1.002 (0.910–1.104) 27.3 
Cancer type                 
Breast cancer 0.777 1.026 (0.858–1.227) 0.286 1.157 (0.885–1.511) 20.8 0.565 1.051 (0.887–1.246) 0.283 1.146 (0.894–1.468) 23.2 0.344 1.061 (0.939–1.199) 
Colorectal cancer 0.512 0.926 (0.735–1.166) NA 0.258 0.821 (0.584–1.155) NA 0.359 0.902 (0.725–1.124) NA 0.34 0.857 (0.625–1.176) NA 0.261 0.914 (0.782–1.069) NA 
Ethnicity                 
Caucasian 0.827 0.984 (0.850–1.138) 0.9721 1.007 (0.696–1.456) 65.2 0.942 0.995 (0.866–1.143) 0.8931 1.022 (0.746–1.399) 58.8 0.9811 0.998 (0.852–1.169) 59.8 
Asian 0.888 1.042 (0.587–1.849) NA 0.366 0.501 (0.112–2.243) NA 0.883 0.959 (0.549–1.674) NA 0.35 0.493 (0.112–2.173) NA 0.623 0.890 (0.558–1.418) NA 
rs2276707 (C/T) 0.356 1.073 (0.924–1.248) 0.896 0.974 (0.655–1.449) 0.405 1.064 (0.920–1.230) 0.813 0.954 (0.643–1.415) 0.518 1.042 (0.919–1.182) 
rs3732360 (C/T) 0.537 1.043 (0.913–1.190) 0.212 1.123 (0.936–1.349) 0.346 1.062 (0.937–1.205) 0.257 1.100 (0.933–1.298) 0.215 1.056 (0.969–1.151) 
rs3814055 (C/T) 0.745 1.022 (0.898–1.163) 0.431 1.098 (0.870–1.387) 0.593 1.034 (0.914–1.171) 0.373 1.105 (0.888–1.375) 0.421 1.040 (0.945–1.145) 
Ethnicity                 
Asian 0.535 1.058 (0.886–1.263) 0.951 1.014 (0.647–1.590) 0.551 1.054 (0.887–1.251) 0.982 0.995 (0.637–1.554) 0.615 1.038 (0.897–1.203) 
Caucasian 0.854 0.982 (0.813–1.187) NA 0.378 1.131 (0.861–1.486) NA 0.881 1.014 (0.847–1.213) NA 0.301 1.142 (0.888–1.469) NA 0.531 1.041 (0.918–1.182) NA 
Cancer type                 
Breast cancer 0.91 0.990 (0.827–1.184) 0.428 1.114 (0.853–1.453) 0.866 1.015 (0.856–1.204) 0.348 1.125 (0.880–1.439) 0.557 1.037 (0.918–1.172) 
Lung cancer 0.558 1.057 (0.877–1.274) NA 0.847 1.049 (0.647–1.701) NA 0.55 1.057 (0.882–1.265) NA 0.903 1.030 (0.638–1.665) NA 0.579 1.045 (0.895–1.220) NA 
rs3814057 (A/C) 0.036 1.170 (1.010–1.355) 0.457 1.145 (0.802–1.634) 32.6 0.037 1.162 (1.009–1.339) 0.656 1.082 (0.766–1.527) 9.1 0.053 1.127 (0.999–1.271) 
Ethnicity                 
Caucasian 0.061 1.155 (0.993–1.343) 0.961 0.990 (0.663–1.478) 0.081 1.138 (0.984–1.317) 0.795 0.948 (0.637–1.412) 0.152 1.097 (0.966–1.245) 
Asian 0.237 1.476 (0.774–2.815) NA 0.074 2.025 (0.934–4.391) NA 0.112 1.627 (0.893–2.964) NA 0.154 1.633 (0.832–3.207) NA 0.055 1.476 (0.992–2.197) NA 
Cancer type                 
Breast cancer 0.11 1.163 (0.966–1.399) 0.5561 1.275 (0.567–2.865) 66.3 0.117 1.154 (0.965–1.379) 28.5 0.6871 1.141 (0.602–2.160) 54.5 0.251 1.191 (0.884–1.605) 53.6 
Colorectal cancer 0.173 1.182 (0.929–1.504) NA 0.674 1.136 (0.627–2.055) NA 0.167 1.177 (0.934–1.483) NA 0.796 1.081 (0.600–1.947) NA 0.201 1.139 (0.933–1.391) NA 

1, P was calculated by random model. The results are in bold if P<0.05.

The PXR rs3814057 A/C polymorphism

For rs3814057 A/C, its heterozygote genotype and dominant models were found to be correlated with an increased cancer risk in whole population (AC compared with AA: pooled OR = 1.170, 95%CI = 1.010–1.355, P=0.036; dominant model: pooled OR = 1.162, 95%CI = 1.009–1.339, P=0.037, Table 3). No association of rs3814057 was found in other genetic models or any subgroups analysis (Table 3).

Sensitivity analysis

Sensitivity analyses were performed to investigate the influence of individual study on the pooled findings by calculating the sensitivity before and after excluding each study from the meta-analysis (Supplementary Table S1). For rs3814057, it was no longer significant after the removal of each study individually (Supplementary Table S1).

Publication bias

Begg’s tests and Egger’s tests were used to calculate the potential publication bias. Evaluation of publication bias for all meta-analyses revealed that the publication biases were observed in rs3814055 (the variant genotype and the recessive model) and in rs3814057 (all models), for P<0.1 in Egger’s tests (Table 4). This may be caused by language bias, the insufficiency publications with adverse results and/or the elevated estimates due to a deficient methodological design for small studies [25].

Table 4
The results of Begg’s and Egger’s tests for the publication bias
Comparison type Begg’s test Egger’s test 
 Z value P-value t value P-value 
rs10504191 (G/A)     
Heterozygote compared with homozygote wild −1.570 0.117 −2.130 0.279 
Homozygote variant compared with homozygote wild −0.520 0.602 −0.550 0.682 
Dominant model −1.570 0.117 −1.800 0.323 
Recessive model −0.520 0.602 −0.420 0.749 
Allelic model −1.570 0.117 −1.530 0.368 
rs3814058 (C/T)     
Heterozygote compared with homozygote wild 0.000 1.000 0.570 0.629 
Homozygote variant compared with homozygote wild 0.680 0.497 0.120 0.912 
Dominant model 0.000 1.000 0.300 0.795 
Recessive model 0.680 0.497 0.070 0.949 
Allelic model 0.000 1.000 0.150 0.893 
rs6785049 (A/G)     
Heterozygote compared with homozygote wild −0.520 0.602 −0.860 0.549 
Homozygote variant compared with homozygote wild 0.520 0.602 0.580 0.667 
Dominant model −0.520 0.602 −0.270 0.832 
Recessive model 1.570 0.117 1.020 0.495 
Allelic model 0.520 0.602 0.280 0.829 
rs1464603 (A/G)     
Heterozygote compared with homozygote wild −1.000 0.317 NA NA 
Homozygote variant compared with homozygote wild 1.000 0.317 NA NA 
Dominant model 1.000 0.317 NA NA 
Recessive model 1.000 0.317 NA NA 
Allelic model 1.000 0.317 NA NA 
rs1523127 (A/C)     
Heterozygote compared with homozygote wild −0.520 0.602 0.270 0.830 
Homozygote variant compared with homozygote wild −1.570 0.117 −1.410 0.392 
Dominant model −0.520 0.602 −0.390 0.761 
Recessive model −1.570 0.117 −1.670 0.343 
Allelic model −0.520 0.602 −0.870 0.543 
rs2276706 (G/A)     
Heterozygote compared with homozygote wild −0.520 0.602 0.050 0.967 
Homozygote variant compared with homozygote wild −0.520 0.602 −0.840 0.556 
Dominant model −0.520 0.602 −0.350 0.785 
Recessive model −0.520 0.602 −0.940 0.521 
Allelic model −0.520 0.602 −0.580 0.668 
rs2276707 (C/T)     
Heterozygote compared with homozygote wild −1.000 0.317 NA NA 
Homozygote variant compared with homozygote wild 1.000 0.317 NA NA 
Dominant model 1.000 0.317 NA NA 
Recessive model 1.000 0.317 NA NA 
Allelic model 1.000 0.317 NA NA 
rs3732360 (C/T)     
Heterozygote compared with homozygote wild −1.000 0.317 NA NA 
Homozygote variant compared with homozygote wild 1.000 0.317 NA NA 
Dominant model −1.000 0.317 NA NA 
Recessive model 1.000 0.317 NA NA 
Allelic model −1.000 0.317 NA NA 
rs3814055 (C/T)     
Heterozygote compared with homozygote wild −0.520 0.602 0.230 0.857 
Homozygote variant compared with homozygote wild −1.570 0.117 −25.410 0.025 
Dominant model 0.520 0.602 −0.100 0.939 
Recessive model −1.570 0.117 −9.210 0.069 
Allelic model −0.520 0.602 −2.770 0.220 
rs3814057 (A/C)     
Heterozygote compared with homozygote wild 1.570 0.117 10.860 0.058 
Homozygote variant compared with homozygote wild 1.570 0.117 8.400 0.075 
Dominant model 1.570 0.117 11.800 0.054 
Recessive model 1.570 0.117 52.120 0.012 
Allelic model 1.570 0.117 13.760 0.046 
Comparison type Begg’s test Egger’s test 
 Z value P-value t value P-value 
rs10504191 (G/A)     
Heterozygote compared with homozygote wild −1.570 0.117 −2.130 0.279 
Homozygote variant compared with homozygote wild −0.520 0.602 −0.550 0.682 
Dominant model −1.570 0.117 −1.800 0.323 
Recessive model −0.520 0.602 −0.420 0.749 
Allelic model −1.570 0.117 −1.530 0.368 
rs3814058 (C/T)     
Heterozygote compared with homozygote wild 0.000 1.000 0.570 0.629 
Homozygote variant compared with homozygote wild 0.680 0.497 0.120 0.912 
Dominant model 0.000 1.000 0.300 0.795 
Recessive model 0.680 0.497 0.070 0.949 
Allelic model 0.000 1.000 0.150 0.893 
rs6785049 (A/G)     
Heterozygote compared with homozygote wild −0.520 0.602 −0.860 0.549 
Homozygote variant compared with homozygote wild 0.520 0.602 0.580 0.667 
Dominant model −0.520 0.602 −0.270 0.832 
Recessive model 1.570 0.117 1.020 0.495 
Allelic model 0.520 0.602 0.280 0.829 
rs1464603 (A/G)     
Heterozygote compared with homozygote wild −1.000 0.317 NA NA 
Homozygote variant compared with homozygote wild 1.000 0.317 NA NA 
Dominant model 1.000 0.317 NA NA 
Recessive model 1.000 0.317 NA NA 
Allelic model 1.000 0.317 NA NA 
rs1523127 (A/C)     
Heterozygote compared with homozygote wild −0.520 0.602 0.270 0.830 
Homozygote variant compared with homozygote wild −1.570 0.117 −1.410 0.392 
Dominant model −0.520 0.602 −0.390 0.761 
Recessive model −1.570 0.117 −1.670 0.343 
Allelic model −0.520 0.602 −0.870 0.543 
rs2276706 (G/A)     
Heterozygote compared with homozygote wild −0.520 0.602 0.050 0.967 
Homozygote variant compared with homozygote wild −0.520 0.602 −0.840 0.556 
Dominant model −0.520 0.602 −0.350 0.785 
Recessive model −0.520 0.602 −0.940 0.521 
Allelic model −0.520 0.602 −0.580 0.668 
rs2276707 (C/T)     
Heterozygote compared with homozygote wild −1.000 0.317 NA NA 
Homozygote variant compared with homozygote wild 1.000 0.317 NA NA 
Dominant model 1.000 0.317 NA NA 
Recessive model 1.000 0.317 NA NA 
Allelic model 1.000 0.317 NA NA 
rs3732360 (C/T)     
Heterozygote compared with homozygote wild −1.000 0.317 NA NA 
Homozygote variant compared with homozygote wild 1.000 0.317 NA NA 
Dominant model −1.000 0.317 NA NA 
Recessive model 1.000 0.317 NA NA 
Allelic model −1.000 0.317 NA NA 
rs3814055 (C/T)     
Heterozygote compared with homozygote wild −0.520 0.602 0.230 0.857 
Homozygote variant compared with homozygote wild −1.570 0.117 −25.410 0.025 
Dominant model 0.520 0.602 −0.100 0.939 
Recessive model −1.570 0.117 −9.210 0.069 
Allelic model −0.520 0.602 −2.770 0.220 
rs3814057 (A/C)     
Heterozygote compared with homozygote wild 1.570 0.117 10.860 0.058 
Homozygote variant compared with homozygote wild 1.570 0.117 8.400 0.075 
Dominant model 1.570 0.117 11.800 0.054 
Recessive model 1.570 0.117 52.120 0.012 
Allelic model 1.570 0.117 13.760 0.046 

Abbreviation: NA, not available. The results are in bold if P<0.1.

TSA and FPRP analyses

To prevent random errors and intensify the reliability of our conclusions, we conducted TSA. Regarding the rs3814058 SNP, its TSA analysis elucidated that the cumulative evidence for rs3814058 SNP is adequate and no further trials are needed to reinforce our conclusions (Figure 2). For other SNPs, however, TSA analysis showed that there was no sufficient cumulative evidence to strengthen the robustness of our findings (figures were not shown).

The required information size to demonstrate the relevance of PXR rs3814058 SNP with cancer risk

Figure 2
The required information size to demonstrate the relevance of PXR rs3814058 SNP with cancer risk

The blue line is the cumulative Z-curve. The red inward-sloping line represents the trial sequential monitoring boundaries.

Figure 2
The required information size to demonstrate the relevance of PXR rs3814058 SNP with cancer risk

The blue line is the cumulative Z-curve. The red inward-sloping line represents the trial sequential monitoring boundaries.

Finally, we computed the FPRP values for significant findings. With the assumption of prior probability 0.1, the FPRP values (for all genotype models in overall cancer analysis and the heterozygote genotype, homozygote variant genotype and dominant models in lung cancer subgroup analysis) of rs3814058 SNP were <0.20, implying that these significant correlations were noteworthy (Table 5). On the contrary, none of the FPRP values of rs3814057 SNP were <0.20 (Table 5).

Table 5
FPRP values for correlations between genotype frequency of PXR and cancer risk
Genotype OR (95%CI) P-value Statistical power1 Prior probability3 
    0.25 0.1 0.01 0.001 0.0001 
rs3814058 (C/T)         
  CT compared with CC 1.280 (1.134–1.445) 6.36E-05 0.599 0.000 0.001 0.010 0.096 0.515 
  TT compared with CC 1.674 (1.262–2.219) 3.45E-04 1.000 0.001 0.003 0.033 0.256 0.775 
  CT + TT compared with CC 1.382 (1.233–1.549) 2.58E-08 0.319 0.000 0.000 0.000 0.000 0.001 
  TT compared with CT + CC 1.422 (1.132–1.786) 0.002 0.974 0.006 0.018 0.169 0.672 0.954 
  T compared with C 1.292 (1.140–1.465) 6.35E-05 0.657 0.000 0.001 0.009 0.088 0.491 
Subgroup (lung cancer)         
  CT compared with CC 1.271 (1.036–1.429) 0.017 0.802 0.060 0.160 0.677 0.955 0.995 
  TT compared with CC 1.387 (1.141–1.687) 0.001 0.480 0.006 0.018 0.171 0.676 0.954 
  CT + TT compared with CC 1.267 (1.089–1.473) 0.002 0.223 0.026 0.075 0.470 0.900 0.989 
  TT compared with CT + CC 1.228 (1.038–1.452) 0.017 0.173 0.228 0.470 0.907 0.990 0.999 
  T compared with C 1.186 (1.075–1.308) 0.001 0.0002 0.968 0.989 0.999 1.000 1.000 
rs3814057 (A/C)         
  AC compared with AA 1.170 (1.010–1.355) 0.036 0.297 0.267 0.522 0.923 0.992 0.999 
  AC + CC compared with AA 1.162 (1.009–1.339) 0.037 0.300 0.270 0.526 0.924 0.992 0.999 
Genotype OR (95%CI) P-value Statistical power1 Prior probability3 
    0.25 0.1 0.01 0.001 0.0001 
rs3814058 (C/T)         
  CT compared with CC 1.280 (1.134–1.445) 6.36E-05 0.599 0.000 0.001 0.010 0.096 0.515 
  TT compared with CC 1.674 (1.262–2.219) 3.45E-04 1.000 0.001 0.003 0.033 0.256 0.775 
  CT + TT compared with CC 1.382 (1.233–1.549) 2.58E-08 0.319 0.000 0.000 0.000 0.000 0.001 
  TT compared with CT + CC 1.422 (1.132–1.786) 0.002 0.974 0.006 0.018 0.169 0.672 0.954 
  T compared with C 1.292 (1.140–1.465) 6.35E-05 0.657 0.000 0.001 0.009 0.088 0.491 
Subgroup (lung cancer)         
  CT compared with CC 1.271 (1.036–1.429) 0.017 0.802 0.060 0.160 0.677 0.955 0.995 
  TT compared with CC 1.387 (1.141–1.687) 0.001 0.480 0.006 0.018 0.171 0.676 0.954 
  CT + TT compared with CC 1.267 (1.089–1.473) 0.002 0.223 0.026 0.075 0.470 0.900 0.989 
  TT compared with CT + CC 1.228 (1.038–1.452) 0.017 0.173 0.228 0.470 0.907 0.990 0.999 
  T compared with C 1.186 (1.075–1.308) 0.001 0.0002 0.968 0.989 0.999 1.000 1.000 
rs3814057 (A/C)         
  AC compared with AA 1.170 (1.010–1.355) 0.036 0.297 0.267 0.522 0.923 0.992 0.999 
  AC + CC compared with AA 1.162 (1.009–1.339) 0.037 0.300 0.270 0.526 0.924 0.992 0.999 

1, Statistical power was computed using the sample size of case and control, OR and P-values.

2, When the statistical power<0.0001, we regarded it as 0.0001.

3, The FPRP are in bold if the values are <0.2.

Discussion

Through numerous mechanisms, PXR have been revealed to regulate cell proliferation in a plenty of cancers, including colon, liver, breast, prostate, ovarian, and so on [26]. It is widely accepted that the polymorphisms of PXR might be correlated to the predisposition to cancer by influencing its expression and/or its function. In the present study, we gathered all related case–control studies and available data, presenting the first systematic review and meta-analysis for the association between ten prevalently studied SNPs in PXR and the susceptibility to overall cancer. Of these ten SNPs, two (rs3814058 C/T and rs3814057 A/C) were demonstrated to be associated with an elevated risk of cancer. No correlations were identified amongst other SNPs.

Our study have generalized the current status of the studies on cancer associated SNPs in PXR. In order to reinforce our conclusions, we performed the TSA and FPRP analysis, which could minimize the errors and guide future researchers to decide whether to continue focussing on this topic. What is more, we provided clues for researchers to figure out the complicated mechanisms of cancer development and screen novel functional genetic biomarkers for cancer prediction.

For rs3814058 C/T polymorphism, our study elucidated that it was statistically associated with overall cancer risk in every genotype model and it could also reach the significance in lung cancer subgroup and the significant associations were confirmed by TSA and FPRP. The meta-analysis of rs3814058 covered four case–control studies and three of them reported the same findings with us. Edwin Sandanaraj’s research on breast cancer, however, holds a different attitude. To explain the discrepancy, we observed that the expression of PXR was depressed or lost in CRC and lung cancer, however elevated in breast cancer. [3,26–28]. Most likely, this tissue specificity can explain the unconformity of the results and more stratification analysis of cancer type ought to be done for rs3814058 polymorphism. Located in the 3′-UTR region of PXR, the C to T transition of rs3814058 obtained a novel miRNA (hsa-miR-129-5p) binding site which was identified by bioinformatics analysis, leading to a depression of PXR expression level in CRC and lung cancer [4,24]. This could reasonably explain the association between the rs3814058 polymorphism and the increment of cancer susceptibility. Therefore, further researchers should pay more attention to the role of rs3814058 on cancerogenesis.

Regarding rs3814057 A/C polymorphism, our results conflicted with other involved studies to some extent. We revealed that the heterozygote genotype and the dominant models of rs3814057 could elevate the risk of overall cancer, which provided a feasible biomarker for cancer prediction. The meta-analysis of rs3814057 involved three case–control studies. None of them were reported to be associated with cancer risk. Based on the TSA, we noticed that the cumulative evidence of rs3814057 was not adequate enough to obtain a reliable conclusion. Likewise, rs3814057 polymorphism was located in 3′-UTR region of PXR, putatively binding to several miRNAs, which was speculated by bioinformatics website ‘https://snpinfo.niehs.nih.gov/’. Thus, the rs3814057 polymorphism might influence the expression of PXR gene and boost the tumor progression. The unfortunate reality is that no studies have focussed on the mechanisms of rs3814057 polymorphism thus far. As a consequence, association studies and mechanism studies concentrated on rs3814057 are extremely needed to further confirm its role on cancer prediction.

Limitations in our study must be recognized. First, articles in English rather than in other languages were selected, which might result in publication bias. Second, studies of PXR polymorphisms on cancer susceptibility field remains emerging, so that the relevant investigations are limited. Last but not least, though PXR gene can influence the development of a variety of cancers, its mechanisms in different cancers have been proved to be distinct [26]. Hence, the tissue specificity must be well recognized in the future studies and meta-analyses of PXR polymorphisms focussed on only one cancer are in demand.

In conclusion, we systematically reviewed the association between PXR polymorphisms and risk of overall cancer. All available data was obtained to conduct a meta-analysis for ten prevalent SNPs. Two of them (rs3814058 C/T and rs3814057 A/C) were elucidated to be correlated with cancer risk in the whole population or some subgroups. Our study generalized the current status of the studies on cancer associated SNPs in PXR gene, providing novel clues for further investigators to identify more biomarkers with cancer-forewarning function.

Author contribution

M.S. conceived and designed the study. J.W. and Z.L. were responsible for the data extraction, TSA, and FPRP analysis. J.W., H.D., and X.F. were responsible for the quality assessment. J.W. and M.S. wrote the manuscript, and M.S. revised the manuscript.

Competing interests

The authors declare that there are no competing interests associated with the manuscript.

Funding

The authors declare that there are no sources of funding to be acknowledged.

Abbreviations

     
  • FPRP

    false-positive report probability

  •  
  • HWE

    Hardy–Weinberg equilibrium

  •  
  • NR1I2

    nuclear receptor subfamily 1 group I member 2

  •  
  • OR

    odds ratio

  •  
  • PXR

    pregnane X receptor

  •  
  • SNP

    single nucleotide polymorphism

  •  
  • SXR

    steroid and xenobiotic receptor

  •  
  • TSA

    trial sequential analysis

  •  
  • 95%CI

    95% confidence interval

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Supplementary data