| Literature DB >> 28881775 |
Xiao Zhong1, Jiayu Feng1, Ya Xiao1, Pingxian Wang1, Qiming Fan1, Ronghua Wu1, Wengang Hu1, Chibing Huang1.
Abstract
UGT2B15 (uridine diphosphate-glucuronosyltransferase 2B15) catalyzes the conversion of lipophilic C19 steroid androgens such as dihydrotestosterone (DHT) into water-soluble metabolites that can be excreted. Studies of the association between the UGT2B15 gene D85Y polymorphism and prostate cancer have yielded contradictory results. We therefore systematically searched in the PubMed, EMBASE, Science Direct/Elsevier, CNKI, and Cochrane Library databases, and identified six relevant studies with which to perform a meta-analysis of the relation between UGT2B15 D85Y polymorphism and prostate cancer risk. Our meta-analysis revealed a significant association between UGT2B15 D85Y gene polymorphism and prostate cancer in all genetic models (P<0.05). The combined odds ratios and 95% confidence intervals were as follows: additive model, 0.53 and 0.32-0.88; dominant model, 0.51 and 0.33-0.79; recessive model, 0.76 and 0.60-0.96; co-dominant model, 0.55 and 0.35-0.86; and allele model, 0.70 and 0.55-0.89. These results are consistent with the idea that the UGT2B15 D85Y enzyme variant reduces the risk of prostate cancer by efficiently metabolizing dihydrotestosterone (DHT), which is associated with prostate cancer progression.Entities:
Keywords: UGT2B15; prostate cancer; single-nucleotide polymorphism; uridine diphosphate-glucuronosyltransferase 2 family
Year: 2017 PMID: 28881775 PMCID: PMC5581074 DOI: 10.18632/oncotarget.17375
Source DB: PubMed Journal: Oncotarget ISSN: 1949-2553
Figure 1Flow diagram showing selection criteria of eligible studies
Characteristics of the included studies
| Author | Country | Case | Control | Genotyping method | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| n | D | Y | DD | YD | YY | n | D | Y | DD | YD | YY | |||
| Okugi H 2006 | Japan | 102 | 139 | 65 | 50 | 39 | 13 | 117 | 137 | 97 | 33 | 71 | 13 | PCR-RFLP |
| Gsur A 2002 | Australia | 190 | 179 | 201 | 40 | 99 | 51 | 190 | 187 | 193 | 47 | 93 | 50 | PCR-RFLP |
| Grant D J2013 | USA | 100 | 111 | 73 | 32 | 47 | 13 | 297 | 275 | 319 | 68 | 139 | 90 | PCR-RFLP |
| Park J 2004 | USA | 155 | 176 | 134 | 52 | 72 | 31 | 154 | 238 | 170 | 21 | 96 | 37 | PCR-RFLP |
| MacLeod SL 2007 | USA | 64 | 89 | 39 | 26 | 37 | 1 | 64 | 73 | 55 | 12 | 49 | 3 | PCR-RFLP |
| Hajdinja T 2004 | Slovenia | 206 | 195 | 217 | 47 | 101 | 58 | 178 | 148 | 208 | 28 | 92 | 58 | PCR-RFLP |
Abbreviation: PCR-RFLP: polymerase chain reaction-restriction fragment length polymorphism
Figure 2Forest plot showing the meta-analysis outcomes of the additive model
Figure 3Forest plot showing the meta-analysis outcomes of the co-dominant model
Figure 4Forest plot showing the meta-analysis outcomes of the dominant model
Figure 5Forest plot showing the meta-analysis outcomes of the recessive model
Figure 6Forest plot showing the meta-analysis outcomes of the allele model
Figure 7(A) Begg's publication bias and (B) Sensitivity analysis plot of Additive model
Figure 8(A) Begg's publication bias and (B) Sensitivity analysis plot of Co-dominant model
Figure 9(A) Begg's publication bias and (B) Sensitivity analysis plot of Dominant model
Figure 10(A) Begg's publication bias and (B) Sensitivity analysis plot of Recessive model
Figure 11(A) Begg's publication bias and (B) Sensitivity analysis plot of Allele model
Egger's test of publication bias
| Models | Coeff. | Std. Err. | t | P>|t| | [95% Conf. Interval] | |
|---|---|---|---|---|---|---|
| Allele | −3.36 | 2.88 | −1.17 | 0.31 | −13.46 | 5.89 |
| Additive | −1.09 | 1.36 | −0.80 | 0.47 | −4.88 | 2.69 |
| Dominant | −3.79 | 3.48 | −1.09 | 0.34 | −13.46 | 5.89 |
| Recessive | −0.51 | 1.06 | −0.48 | 0.66 | −3.45 | 2.43 |
| Co-dominant | −5.39 | 3.39 | −1.59 | 0.19 | −14.80 | 4.03 |