Oncotarget

Research Papers:

Two common functional catalase gene polymorphisms (rs1001179 and rs794316) and cancer susceptibility: evidence from 14,942 cancer cases and 43,285 controls

Kang Liu, Xinghan Liu, Meng Wang, Xijing Wang, Huafeng Kang, Shuai Lin, Pengtao Yang, Cong Dai, Peng Xu, Shanli Li and Zhijun Dai _

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Oncotarget. 2016; 7:62954-62965. https://doi.org/10.18632/oncotarget.10617

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Abstract

Kang Liu1,*, Xinghan Liu1,*, Meng Wang1,*, Xijing Wang1, Huafeng Kang1, Shuai Lin1, Pengtao Yang1, Cong Dai1, Peng Xu1, Shanli Li1 and Zhijun Dai1

1 Department of Oncology, Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China

* These authors have contributed equally to this work

Correspondence to:

Zhijun Dai, email:

Keywords: catalase, polymorphism, cancer, susceptibility, meta-analysis

Received: May 12, 2016 Accepted: July 01, 2016 Published: July 15, 2016

Abstract

Recent studies have focused on the associations of catalase polymorphisms with various types of cancer, including cervical and prostate cancers. However, the results were inconsistent. To obtain a more reliable conclusion, we evaluated the relationship between the two common catalase gene polymorphisms (rs1001179 and rs794316) and cancer risk by a meta-analysis. Our meta-analysis included 37 published studies involving 14,942 cancer patients and 43,285 cancer-free controls. Odds ratios (ORs) and 95% confidence intervals (CIs) were used to evaluate the cancer risk. The results demonstrated that the rs1001179 polymorphism was associated with an increased cancer risk in the recessive and homozygote models (TT vs. CC: OR = 1.19, P = 0.01; TT vs. CT+CC: OR = 1.19, P <0.001). Furthermore, stratified analyses revealed a significant association between the rs1001179 polymorphism and prostate cancer in all models except the homozygote comparison. An association of the rs794316 polymorphism with cancer risk was detected in two genetic models (TT vs. AA: OR = 1.34, 95% CI = 1.03–1.74, P <0.001; TT vs. AT+AA: OR = 1.39, 95% CI = 1.09–1.77, P = 0.01). Additional well-designed studies with large samples should be performed to validate our results.


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