| Literature DB >> 29552317 |
Chong Guo1, Li Wen2, Ju-Kun Song3, Weng-Jing Zeng4, Chao Dan5, Yu-Ming Niu1,6, Ming Shen7.
Abstract
Previous studies have suggested that interleukin-10 (IL-10) polymorphisms may be associated with an increased risk of developing cervical cancer. However, the published results on this subject matter are controversial. The aim of this study was to conduct a meta-analysis of published reports to more precisely investigate the relationship between IL-10 polymorphisms and cervical cancer risk. Five online databases (PubMed, Embase, Web of SCI, CNKI and Wanfang) were searched, and seventeen articles with sufficient quantitative information were included in our meta-analysis. The odds ratios (ORs) and 95% confidence intervals (CIs) were calculated to assess the association between IL-10 polymorphisms and cervical cancer risk. Publication bias, sensitivity and cumulative analyses were also performed to support our findings. Overall, there was a significant association between the IL-10 -1082A > G polymorphism and cervical cancer risk observed in the total population (G vs. A: OR = 1.60, 95% CI = 1.12-2.29, P = 0.01, I2 = 92.3%; AG vs. AA: OR = 1.34, 95% CI = 1.04-1.74, P = 0.03, I2 = 65.9%; AG + GG vs. AA: OR = 1.58, 95% CI = 1.11-2.25, P = 0.01, I2 = 84.4%), and the same results were obtained in the subgroup analysis. Moreover, the IL-10 -819 T > C polymorphism exhibited a significant, protective effect against cervical cancer. In summary, our meta-analysis suggests that IL-10 polymorphisms may play a variety of roles in regard to cervical cancer risk, especially in Asians.Entities:
Keywords: cervical cancer; interleukin-10; meta-analysis; polymorphism
Year: 2018 PMID: 29552317 PMCID: PMC5844753 DOI: 10.18632/oncotarget.24193
Source DB: PubMed Journal: Oncotarget ISSN: 1949-2553
Figure 1Flow diagram of the study selection process
Characteristics of case-control studies on IL-10 -1082A > G, -819T > C and -592C > A polymorphisms and cervical cancer risk
| First author | Year | Country | Racial descent | Source of controls | Case | Control | Genotype distribution | Genotyping method | NOS | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Case | Control | ||||||||||||||
| AA | AG | GG | AA | AG | GG | ||||||||||
| Stanczuk | 2001 | Zimbabwe | African | HB | 77 | 69 | 45 | 31 | 1 | 58 | 11 | 0 | 0.472 | ARMS-PCR | 6 |
| Roh | 2002 | Korea | Asian | HB | 144 | 179 | 144 | 0 | 0 | 179 | 0 | 0 | NA | PCR-RFLP | 4 |
| Govan | 2003 | South Africa | African | HB | 197 | 182 | 88 | 80 | 29 | 76 | 65 | 41 | <0.01 | ARMS-PCR | 6 |
| Zoodsma | 2005 | Netherlands | Caucasian | PB | 667 | 606 | 154 | 326 | 187 | 130 | 307 | 169 | 0.668 | Taqman | 9 |
| Matsumoto | 2010 | Japanese | Asian | HB | 104 | 173 | 73 | 26 | 5 | 156 | 16 | 1 | 0.412 | ARMS-PCR | 7 |
| Yu | 2011 | China | Asian | HB | 103 | 115 | 90 | 12 | 1 | 98 | 14 | 3 | 0.012 | ARMS-PCR | 6 |
| Wang | 2011 | China | Asian | PB | 186 | 200 | 77 | 85 | 24 | 103 | 76 | 21 | 0.222 | PCR | 7 |
| Barbisan | 2012 | Argentina | Caucasian | HB | 122 | 176 | 50 | 61 | 11 | 79 | 83 | 14 | 0.222 | PCR Pyrosequencing | 6 |
| Singhal | 2015 | India | Asian | HB | 208 | 250 | 32 | 76 | 100 | 100 | 107 | 43 | 0.125 | PCR-RFLP | 7 |
| Zidi | 2015 | Tunisian | Caucasian | HB | 86 | 126 | 33 | 36 | 17 | 51 | 50 | 25 | 0.055 | TaqMan | 6 |
| Torres-Poveda | 2015 | Mexico | Caucasian | HB | 200 | 200 | 121 | 70 | 9 | 110 | 78 | 12 | 0.708 | TaqMan | 8 |
| Zeng | 2015 | China | Asian | HB | 52 | 50 | 5 | 7 | 40 | 24 | 22 | 0 | 0.033 | TaqMan | 5 |
| Bai | 2016 | China | Asian | HB | 165 | 165 | 74 | 75 | 16 | 80 | 72 | 13 | 0.563 | PCR-RFLP | 7 |
| TT | TC | CC | TT | TC | CC | ||||||||||
| Roh | 2002 | Korea | Asian | HB | 144 | 179 | 77 | 56 | 11 | 87 | 77 | 15 | 0.724 | PCR-RFLP | 6 |
| Singh | 2009 | India. | Asian | HB | 150 | 162 | 27 | 67 | 56 | 24 | 61 | 77 | 0.046 | PCR-RFLP. | 6 |
| Singhal | 2015 | India | Asian | HB | 208 | 250 | 61 | 102 | 45 | 61 | 120 | 69 | 0.537 | PCR-RFLP | 7 |
| Zidi | 2015 | Tunisian | Caucasian | HB | 86 | 126 | 9 | 32 | 45 | 4 | 66 | 56 | 0.003 | TaqMan | 5 |
| Torres-Poveda | 2016 | Mexico | Caucasian | HB | 200 | 200 | 54 | 97 | 49 | 34 | 85 | 81 | 0.156 | TaqMan | 8 |
| Bai | 2016 | China | Asian | HB | 165 | 165 | 44 | 75 | 45 | 28 | 73 | 64 | 0.362 | PCR-RFLP | 7 |
| CC | CA | AA | CC | CA | AA | ||||||||||
| Roh | 2002 | Korea | Asian | HB | 144 | 179 | 11 | 56 | 77 | 15 | 77 | 87 | 0.724 | PCR-RFLP | 6 |
| Zoodsma | 2005 | Netherlands | Caucasian | PB | 667 | 606 | 393 | 231 | 30 | 405 | 175 | 26 | 0.206 | Taqman | 9 |
| Ivansson | 2007 | Sweden | Caucasian | PB | 1306 | 288 | 736 | 464 | 82 | 162 | 112 | 14 | 0.334 | Multiplex PCR | 7 |
| Xiong | 2010 | China | Asian | PB | 70 | 108 | 12 | 23 | 35 | 13 | 44 | 51 | 0.467 | PCR-RFLP | 8 |
| Yu | 2011 | China | Asian | HB | 103 | 115 | 7 | 37 | 59 | 19 | 44 | 52 | 0.075 | ARMS-PCR | 7 |
| Shekari | 2012 | India. | Asian | PB | 200 | 200 | 16 | 96 | 88 | 17 | 102 | 81 | 0.054 | PCR-RFLP | 7 |
| Singhal | 2015 | India | Asian | HB | 208 | 250 | 85 | 94 | 29 | 60 | 123 | 67 | 0.810 | PCR-RFLP | 7 |
| Zidi | 2015 | Tunisian | Caucasian | HB | 86 | 126 | 45 | 32 | 9 | 57 | 64 | 5 | 0.012 | TaqMan | 5 |
| Torres-Poveda | 2016 | Mexico | Caucasian | HB | 200 | 200 | 44 | 98 | 58 | 85 | 85 | 30 | 0.255 | TaqMan | 8 |
| Bai | 2016 | China | Asian | HB | 165 | 165 | 20 | 82 | 63 | 15 | 80 | 70 | 0.243 | PCR-RFLP | 7 |
aHWE in control, PB: Population-base control, HB: Hospital-base control and/or healthy base control
Figure 2Statistical analysis of the association between the IL-10 -1082A>G polymorphism and cervical cancer risk in the AG+GG vs. AA model
(A) ORs and 95% CIs; (B) cumulative analysis; (C) sensitivity analysis; (D) publication bias.
Figure 3Statistical analysis of the association between the IL-10 -819T>C polymorphism and cervical cancer risk in the TC+CC vs. TT model
(A) ORs and 95% CIs; (B) cumulative analysis; (C) sensitivity analysis; (D) publication bias.
Figure 4Statistical analysis of the association between the IL-10 -592C>A polymorphism and cervical cancer risk in the CA+AA vs. CC model
(A) ORs and 95% CIs; (B) cumulative analysis; (C) sensitivity analysis; (D) publication bias.
Scale for quality evaluation
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