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- Sainz, J., et al.
(författare)
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Association of genetic polymorphisms in ESR2, HSD17B1, ABCB1, and SHBG genes with colorectal cancer risk
- 2011
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Ingår i: Endocrine-Related Cancer. - 1479-6821. ; 18:2, s. 265-276
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Tidskriftsartikel (refereegranskat)abstract
- The incidence rates and relative risks for colorectal cancer (CRC) are higher in men than in women. Sex steroids may play a role in this gender-associated difference in CRC risk. This study was conducted to explore the relationship of single nucleotide polymorphisms (SNPs) in steroid hormone signaling (ESR1, ESR2, PGR, NR1I2, and SHBG), phase I-and II-metabolizing enzyme (COMT, HSD17B1, CYP1A1, CYP17A1, CYP1A2, CYP1B1, CYP2C9, CYP3A4, CYP2C19, and GSTP1), and hormone transporter (ABCB1) genes with the risk of CRC in German women and men, separately. From the population-based DACHS study (South Germany), 47 putatively functional SNPs were genotyped in 1798 CRC cases (746 women and 1052 men) and 1810 controls (732 women and 1078 men). Significant allele dose-response associations were observed with ESR2_rs1255998, ESR2_rs928554, HSD17B1_rs605059, and ABCB1_rs2229109 in women (P trend=0.004, 0.05, 0.03, and 0.05 respectively) and with ABCB1_rs1045642, ABCB1_rs9282564, and SHBG_rs6259 in men (P trend=0.01, 0.03, and 0.02 respectively). The ESR2_rs1255998_G allele showed the most significant association with risk for CRC in women, with a per-allele odds ratio (OR) of 0.68 (95% confidence interval (CI) 0.52-0.88). This finding was replicated in an independent study from North Germany including 1076 female CRC cases and 1151 controls (OR=0.84, 95% CI 0.71-1.04), yielding a per-allele OR of 0.80 (95% CI 0.69-0.93, P trend=0.003) in the pooled sample. These findings implicate a role of ESR2 in the risk for developing CRC in women and suggest that HSD17B1, ABCB1, and SHBG genes may contribute to sex steroid-mediated effects on CRC development. Endocrine-Related Cancer (2011) 18 265-276
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- Dunlop, Malcolm G, et al.
(författare)
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Cumulative impact of 10 common genetic variants on colorectal cancer risk in 42,333 individuals from eight populations
- 2012
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Ingår i: Gut. - Stockholm : Karolinska Institutet, Dept of Molecular Medicine and Surgery. - 1468-3288 .- 0017-5749.
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Tidskriftsartikel (refereegranskat)abstract
- OBJECTIVE: Colorectal cancer (CRC) has a substantial heritable component. Common genetic variation has been shown to contribute to CRC risk. A study was conducted in a large multi-population study to assess the feasibility of CRC risk prediction using common genetic variant data combined with other risk factors. A risk prediction model was built and applied to the Scottish population using available data. DESIGN: Nine populations of European descent were studied to develop and validate CRC risk prediction models. Binary logistic regression was used to assess the combined effect of age, gender, family history (FH) and genotypes at 10 susceptibility loci that individually only modestly influence CRC risk. Risk models were generated from case-control data incorporating genotypes alone (n=39 266) and in combination with gender, age and FH (n=11 324). Model discriminatory performance was assessed using 10-fold internal cross-validation and externally using 4187 independent samples. The 10-year absolute risk was estimated by modelling genotype and FH with age- and gender-specific population risks. RESULTS: The median number of risk alleles was greater in cases than controls (10 vs 9, p<2.2×10(-16)), confirmed in external validation sets (Sweden p=1.2×10(-6), Finland p=2×10(-5)). The mean per-allele increase in risk was 9% (OR 1.09; 95% CI 1.05 to 1.13). Discriminative performance was poor across the risk spectrum (area under curve for genotypes alone 0.57; area under curve for genotype/age/gender/FH 0.59). However, modelling genotype data, FH, age and gender with Scottish population data shows the practicalities of identifying a subgroup with >5% predicted 10-year absolute risk. CONCLUSION: Genotype data provide additional information that complements age, gender and FH as risk factors, but individualised genetic risk prediction is not currently feasible. Nonetheless, the modelling exercise suggests public health potential since it is possible to stratify the population into CRC risk categories, thereby informing targeted prevention and surveillance.
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