Association Between C282Y And H63D Mutations Of The HFE Gene ...
Study identification and selection
Eligible studies were identified by searching the databases of PubMed and ISI Web of Knowledge for relevant reports published before May 2009. The search criteria "c282y OR h63d" and "liver cancer OR hepatocellular carcinoma" were used. We also searched reports and dissection databases published in the Chinese Biomedical database (CBM), China National Knowledge Infrastructure (CNKI), and Wan Fang (Chinese) database to collect articles of case-control studies or cohort studies on associations between HFE mutations and susceptibility to HCC before May 2009. The reference lists of the retrieved articles were also reviewed to identify additional articles missed by the above search.
Studies were selected if (1) there were available data for at least one of the C282Y and H63D two mutations with risk of HCC using a case-control or cohort design; (2) HCC cases were diagnosed by histopathological biopsy or by elevated AFP and distinct iconography changes (CT, MRI, and B ultrasonography); (3) control subjects were free of cancer. Controls could be composed of healthy subjects, chronic liver disease (CLD), including chronic hepatitis (CH) and LC. CLD was either histologically proven or diagnosed based on concordant clinical, biological, and morphological criteria. Review articles and articles that did not provide genotype data were excluded.
Data extraction and synthesis
The following information was extracted from each study: first author's surname, year of publication, ethnicity of study population, country where study was conducted, and the number of cases and controls for each C282Y and H63D genotype. When specific results were not reported, we used available tabular data to calculate them.
Statistical methods
To compare the odds ratio (OR) on the same baseline, we used crude OR to conduct the meta-analysis. The effect of association was indicated as crude OR with the corresponding 95% confidence intervals (CIs). Because of relatively small sample sizes of individual studies and low frequency of variant alleles and the practical clinical value, we performed meta-analysis only in two models: dominant model (YY+CY vs. CC or DD+HD vs. HH) and allele contrast (Y vs. C or D vs. H). The pooled OR was estimated using the FE model (DerSimonian & Laird) [22]. The heterogeneity between studies was tested using the Q statistic [23]. If P < 0.10, the heterogeneity was considered statistically significant, and the RE model was then used. Heterogeneity was also quantified using the I2 metric, which is independent of the number of studies in the meta-analysis (I2 < 25% = no heterogeneity; I2 = 25-50% = moderate heterogeneity; I2 > 50% = large or extreme heterogeneity) [24]. The potential small-study bias was tested using the Egger regression test asymmetry [25] and the Begg's test for funnel plot, which is based on Kendall's tau [26]. Sensitivity analysis was performed by omitting one study at a time to assess the influence of individual studies on meta-analysis. The distribution of the genotypes in the control group was tested for Hardy-Weinberg equilibrium using a goodness-of-fit Chi-square test.
All analyses above were conducted using the STATA version 10.0 software (Stata Corp, College Station, Texas). All P-values were two-sided. A p value less than 0.05 was considered statistically significant.
The statistical power was calculated using the PS software [27]. In order to assess the reliability of the positive association, we calculated false positive report probability (FPRP) [28]. An Excel spreadsheet to calculate FPRP is included with the online material http://jncicancerspectrum.oupjournals.org/jnci/content/vol96/issue6. If FPRP < 0.20, we think the association is reliable. Given that the gene mutation was regarded as causal, we used population-attributable risk (PAR) to refer to the proportion of disease risk in a population that can be attributed to the causal effects of the risk allele. PAR can be assessed by using the formula [29].
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