Dataset Information

Association between APE1 Single Nucleotide Polymorphism (rs1760944) and Cancer Risk: a Meta-Analysis Based on 6,419 Cancer Cases and 6,781 Case-free Controls.

ABSTRACT: Apurinic/apyrimidinic endonuclease 1 (APE1) is an essential enzyme in the base excision repair pathway. Epidemiological studies have suggested associations between APE1 rs1760944 polymorphism and cancer risk. This study was aimed to evaluate the relationship between APE1 rs1760944 polymorphism and cancer risk. We searched Pubmed, ISI Web of Knowledge, Embase, Chinese National Knowledge Infrastructure (CNKI) databases until September 2013 to identify eligible studies. Odds ratios (ORs) and 95 % confidence intervals (CIs) were used to estimate the strength of the associations. 12 studies from 11 articles on APE1 rs1760944 genotypes and cancer risk were identified, including a total of 6,419 cancer cases and 6,781 case-free controls. Overall, APE1 rs1760944 polymorphism was significantly associated with the decreased risk of cancer in any genetic models (G vs. T: OR = 0.86, 95% CI = 0.82-0.90; homozygote comparison: OR = 0.74, 95% CI = 0.67-0.82; heterozygote comparison: OR =0.88, 95%CI = 0.81-0.95; dominant model TG+GG vs. TT: OR = 0.82, 95% CI = 0.76-0.89; recessive model GG vs. TT+TG: OR = 0.81, 95%CI = 0.75-0.88). In the stratified analysis by populations, the effect was remain in studies of Asian population (homozygote comparison: OR = 0.71, 95%CI = 0.63-0.79; heterozygote comparison: OR = 0.86, 95 %CI = 0.79- 0.94; dominant model: OR = 0.80, 95% CI = 0.74 -0.87 and recessive model: OR = 0.78, 95%CI = 0.71-0.86). Moreover, a significantly decreased risk was found in lung cancer studies (homozygote comparison: OR = 0.68, 95% CI = 0.59-0.79; heterozygote comparison: OR = 0.86, 95%CI = 0.77- 0.98; dominant model: OR = 0.80, 95%CI = 0.72-0.90 and recessive model: OR= 0.77, 95% CI= 0.68-0.87). These findings support that APE1 rs1760944 polymorphism has a possible protective effect on cancer susceptibility particularly among Asians. Further studies based on different ethnicity and various cancer types are warranted to verify our findings.

SUBMITTER: Dai ZJ

PROVIDER: S-EPMC3963083 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Association between APE1 Single Nucleotide Polymorphism (rs1760944) and Cancer Risk: a Meta-Analysis Based on 6,419 Cancer Cases and 6,781 Case-free Controls.

Dai Zhi-Jun ZJ Wang Xi-Jing XJ Kang An-Jing AJ Ma Xiao-Bin XB Min Wei-Li WL Lin Shuai S Zhao Yang Y Yang Peng-Tao PT Wang Meng M Kang Hua-Feng HF

Journal of Cancer 20140313 3

Apurinic/apyrimidinic endonuclease 1 (APE1) is an essential enzyme in the base excision repair pathway. Epidemiological studies have suggested associations between APE1 rs1760944 polymorphism and cancer risk. This study was aimed to evaluate the relationship between APE1 rs1760944 polymorphism and cancer risk. We searched Pubmed, ISI Web of Knowledge, Embase, Chinese National Knowledge Infrastructure (CNKI) databases until September 2013 to identify eligible studies. Odds ratios (ORs) and 95 % c ...[more]

PMID: 24665350

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Project description:BACKGROUND: This paper introduces and applies a genome wide predictive study to learn a model that predicts whether a new subject will develop breast cancer or not, based on her SNP profile. RESULTS: We first genotyped 696 female subjects (348 breast cancer cases and 348 apparently healthy controls), predominantly of Caucasian origin from Alberta, Canada using Affymetrix Human SNP 6.0 arrays. Then, we applied EIGENSTRAT population stratification correction method to remove 73 subjects not belonging to the Caucasian population. Then, we filtered any SNP that had any missing calls, whose genotype frequency was deviated from Hardy-Weinberg equilibrium, or whose minor allele frequency was less than 5%. Finally, we applied a combination of MeanDiff feature selection method and KNN learning method to this filtered dataset to produce a breast cancer prediction model. LOOCV accuracy of this classifier is 59.55%. Random permutation tests show that this result is significantly better than the baseline accuracy of 51.52%. Sensitivity analysis shows that the classifier is fairly robust to the number of MeanDiff-selected SNPs. External validation on the CGEMS breast cancer dataset, the only other publicly available breast cancer dataset, shows that this combination of MeanDiff and KNN leads to a LOOCV accuracy of 60.25%, which is significantly better than its baseline of 50.06%. We then considered a dozen different combinations of feature selection and learning method, but found that none of these combinations produces a better predictive model than our model. We also considered various biological feature selection methods like selecting SNPs reported in recent genome wide association studies to be associated with breast cancer, selecting SNPs in genes associated with KEGG cancer pathways, or selecting SNPs associated with breast cancer in the F-SNP database to produce predictive models, but again found that none of these models achieved accuracy better than baseline. CONCLUSIONS: We anticipate producing more accurate breast cancer prediction models by recruiting more study subjects, providing more accurate labelling of phenotypes (to accommodate the heterogeneity of breast cancer), measuring other genomic alterations such as point mutations and copy number variations, and incorporating non-genetic information about subjects such as environmental and lifestyle factors.

Dataset Information

Association between APE1 Single Nucleotide Polymorphism (rs1760944) and Cancer Risk: a Meta-Analysis Based on 6,419 Cancer Cases and 6,781 Case-free Controls.

Publications

Association between APE1 Single Nucleotide Polymorphism (rs1760944) and Cancer Risk: a Meta-Analysis Based on 6,419 Cancer Cases and 6,781 Case-free Controls.

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