Several DNA repair pathways are involved in the maintenance of genetic stability. The hOGG1 gene is involved in base excision repair (BER) of DNA repair pathways. DNA damage generated by different carcinogenic agents or inflammatory process is repaired mostly by BER. Common polymorphisms in DNA repair genes may alter protein function and an individual's capacity to repair damaged DNA. Deficits in repair capacity may lead to genetic instability and carcinogenesis [18, 19]. So allelic variants in the key gene involved in DNA repair process, hOGG1, may confer an increased risk for PCa development.
Since the identification of hOGG1 Ser326Cys polymorphism, a number of studies have investigated the genetic effect of this polymorphism on PCa susceptibility, but the results are inconclusive. As a powerful statistical method, meta-analysis can provide a quantitative approach for pooling the results of different researches on the same topic, and for estimating and explaining their diversity [20, 21]. This led us to undertake the present meta-analysis, which could quantitify all the available data and might help us to distinguish the true from the false, to explore a robust estimate of the effect of this polymorphism on PCa. To the best of our knowledge, it is the first systematic review that has investigated the association of hOGG1 Ser326Cys polymorphism and PCa. In present meta-analysis, no evidence has shown any associations between Ser326Cys polymorphism and PCa susceptibility in overall population.
The results of several studies have suggested that single nucleotide polymorphisms may determine the differences in the risk of PCa between ethnic groups [22, 23]. Ethnic differences in the incidence of PCa are well established. So subanalysis on different ethnicity was performed. Our data suggested that hOGG1 Ser326Cys polymorphism was associated with a statistically significant decrease in PCa risk in mixed population. However, no significant association was detected in Caucasians. This indicates a possible role of ethnic differences in genetic backgrounds and the environment they lived in. Moreover, the discrepancy might be due to chance because studies with small sample sizes may be underpowered to detect a slight effect or may have generated a fluctuated risk estimate. Therefore, the results of this study should be interpreted with caution.
In the present study, statistically significant between-study heterogeneity of genotype effect was detected in all different genetic models when all the eligible studies were pooled into the meta-analysis. After subgroup analyses by ethnicity, significant heterogeneity was only detected in Caucasians. The heterogeneity was effectively removed in mixed subgroup. Significant between-study heterogeneity was detected in overall and Caucasian population and we conducted analyses using random effect models. Then we got a wider confidence interval and a larger P-value which may be distorting the meta-analysis. The data showed that no obvious publication bias existed in our meta-analysis.
Some limitations of this meta-analysis should be acknowledged. First, controls were not uniformly defined, so selection bias may occur and they may not be representative of the general population. Second, the number of cases and controls in the included studies was relatively low. Third, our result was based on unadjusted estimates, while a more precise analysis should be conducted adjusted by other factors like smoking, drinking status and environmental factors. Fourth, in the subgroup analyses by ethnicity, relatively limited study number and incomplete information for mixed ethnicities made it impossible to perform ethnic subgroup analysis of Africans and Asians. Thus, additional studies are warranted to evaluate the effect of this functional polymorphism on PCa risk in different ethnicities, especially in Africans and Asians. In addition, our analysis did not consider the possibility of gene-gene or SNP-SNP interactions or the possibility of linkage disequilibrium between polymorphisms. Therefore, larger and well-designed studies are needed to further evaluate the association between hOGG1 polymorphism and PCa risk.