Large-Scale Evaluation of Genetic Variants in Candidate Genes for Colorectal Cancer Risk in the Nurses' Health Study and the Health Professionals' Follow-up Study
Program in Molecular and Genetic Epidemiology, Department of Epidemiology, Harvard School of Public Health, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA. Cancer Epidemiology Biomarkers & Prevention
(Impact Factor: 4.13).
02/2008; 17(2):311-9. DOI: 10.1158/1055-9965.EPI-07-0195
Advances in genomics offer new strategies for assessing the association of common genetic variations at multiple loci and risk of many diseases, including colorectal cancer. Low-penetrance alleles of genes in many biological pathways, such as DNA repair, metabolism, inflammation, cell cycle, apoptosis, and Wnt signaling, may influence the risk of nonfamilial colorectal cancer. To identify susceptibility genes for colorectal cancer, we designed a large-scale case-control association study nested within the Nurses' Health Study (190 cases and 190 controls) and the Health Professionals' Follow-up Study (168 cases and 168 controls). We used a custom GoldenGate (Illumina) oligonucleotide pool assay including 1,536 single nucleotide polymorphisms (SNP) selected in candidate genes from cancer-related pathways, which have been sequenced and genotyped in the SNP500Cancer project; 1,412 of the 1,536 (92%) of the SNPs were genotyped successfully within 388 genes. SNPs in high linkage disequilibrium (r(2) >/= 0.90) with another assayed SNP were excluded from further analyses. As expected by chance (and not significant compared with a corrected Bonferroni P = 0.00004), in the additive model, 11 of 1,253 (0.9%) SNPs had a P(trend) < 0.01 and 38 of 1,253 (3.0%) SNPs had a P(trend) >/= 0.01 and P(trend) < 0.05. Of note, the MGMT Lys(178)Arg (rs2308237) SNP, in linkage disequilibrium with the previously reported MGMT Ile(143)Val SNP, had an inverse association with colorectal cancer risk (MGMT Lys(178)Arg: odds ratio, 0.52; 95% confidence interval, 0.35-0.78; unadjusted P(trend) = 0.0003 for the additive model; gene-based test global P = 0.00003). The SNP500Cancer database and the Illumina GoldenGate Assay allowed us to test a larger number of SNPs than previously possible. We identified several SNPs worthy of investigation in larger studies.
Available from: Mohammadreza Nassiri
- "They reported that PMS2 is a key gene in the MMR pathway. The PMS2-24G>C SNP (rs6463524) on exon7 was associated with an increased risk of CRC (Hazra et al., 2008). "
[Show abstract] [Hide abstract]
Colorectal cancer is the third most common cancer in both men and women in the world and the second leading cause of cancer-related deaths. The incidence of colorectal cancer has increased in Iran in the past three decades and is now considered as a serious problem for our society. This cancer has two types hereditary and non-hereditary, 80% of cases being the latter. Considering that the relationship between SNPs with diseases is a concern, many researchers believed that they offer valuable markers for identifying genes responsible for susceptibility to common diseases. In some cases, they are direct causes of human disease. One SNP can increase risk of cancer, but when considering the rate of overlap and frequency of DNA repair pathways, it might be expected that SNP alone cannot affect the final result of cancer, although several SNPs together can exert a significant influence. Therefore identification of these SNPs is very important. The most important loci which include mutations are: MLH1, MSH2, PMS2, APC, MUTYH, SMAD7, STK11, XRCC3, DNMT1, MTHFR, Exo1, XRCC1 and VDR. Presence of SNPs in these genes decreases or increases risk of colorectal cancer.
Materials and methods:
In this article we reviewed the Genes and SNPs associated with non-hereditary and hereditary of colorectal cancer that recently were reported from candidate gene y, meta-analysis and GWAS studies.
As with other cancers, colorectal cancer is associated with SNPs in gene loci. Generally, by exploring SNPs, it is feasible to predict the risk of developing colorectal cancer and thus establishing proper preventive measures.
SNPs of genes associated with colorectal cancer can be used as a marker SNP panel as a potential tool for improving cancer diagnosis and treatment planning.
Asian Pacific journal of cancer prevention: APJCP 10/2013; 14(10):5609-14. DOI:10.7314/APJCP.2013.14.10.5609 · 2.51 Impact Factor
Available from: PubMed Central
- "A large number of molecular epidemiology studies have been carried out to assess the roles of the MGMT polymorphisms in various types of cancer, including lung cancer, head and neck cancer, and colorectal cancer [9,10,11,12,13,14,15,16,17,18,19,20,21]. The MGMTLeu84Phe substitution is the most widely studied polymorphism in MGMT due to a (C->T) transition at nt.262 (MGMT Leu84Phe, rs12917). "
[Show abstract] [Hide abstract]
ABSTRACT: O(6)-methylguanine-DNA methyltransferase is one of the few proteins to directly remove alkylating agents in the human DNA direct reversal repair pathway. A large number of case-control studies have been conducted to explore the association between MGMT Leu84Phe polymorphism and cancer risk. However, the results were not consistent.
We carried out a meta-analysis of 44 case-control studies to clarify the association between the Leu84Phe polymorphism and cancer risk.
Overall, significant association of the T allele with cancer susceptibility was verified with meta-analysis under a recessive genetic model (P<0.001, OR=1.30, 95%CI 1.24-1.50) and TT versus CC comparison (P=0.001, OR=1.29, 95% CI 1.12-1.50). In subgroup analysis, a significant increased risk was found for lung cancer (TT versus CC, P=0.027, OR=1.67, 95% CI 1.06-2.63; recessive genetic model, P=0.32, OR=1.64, 95% CI 1.04-2.58), whereas risk of colorectal cancer was significantly low under a dominant genetic model (P=0.019, OR=0.84, 95% CI 0.72-0.97). Additionally, a significant association between TT genetic model and total cancer risk was found in the Caucasian population (TT versus CC, P=0.014, OR=1.29, 95% CI 1.05-1.59; recessive genetic model, P=0.009, OR=1.31, 95% CI 1.07-1.61), but not in the Asian population. An increased risk for lung cancer was also verified in the Caucasian population (TT versus CC, P=0.035, OR=1.62, 95% CI 1.04-2.53; recessive genetic model, P=0.048, OR=1.57, 95% CI 1.01-2.45).
These results suggest that MGMT Leu84Phe polymorphism might contribute to the susceptibility of certain cancers.
PLoS ONE 09/2013; 8(9):e75367. DOI:10.1371/journal.pone.0075367 · 3.23 Impact Factor
Available from: Vernon Shane Pankratz
[Show abstract] [Hide abstract]
ABSTRACT: Polymorphisms in genes critical to cell cycle control are outstanding candidates for association with ovarian cancer risk; numerous genes have been interrogated by multiple research groups using differing tagging single-nucleotide polymorphism (SNP) sets. To maximize information gleaned from existing genotype data, we conducted a combined analysis of five independent studies of invasive epithelial ovarian cancer. Up to 2,120 cases and 3,382 controls were genotyped in the course of two collaborations at a variety of SNPs in 11 cell cycle genes (CDKN2C, CDKN1A, CCND3, CCND1, CCND2, CDKN1B, CDK2, CDK4, RB1, CDKN2D, and CCNE1) and one gene region (CDKN2A-CDKN2B). Because of the semi-overlapping nature of the 123 assayed tagging SNPs, we performed multiple imputation based on fastPHASE using data from White non-Hispanic study participants and participants in the international HapMap Consortium and National Institute of Environmental Health Sciences SNPs Program. Logistic regression assuming a log-additive model was done on combined and imputed data. We observed strengthened signals in imputation-based analyses at several SNPs, particularly CDKN2A-CDKN2B rs3731239; CCND1 rs602652, rs3212879, rs649392, and rs3212891; CDK2 rs2069391, rs2069414, and rs17528736; and CCNE1 rs3218036. These results exemplify the utility of imputation in candidate gene studies and lend evidence to a role of cell cycle genes in ovarian cancer etiology, suggest a reduced set of SNPs to target in additional cases and controls.
Cancer Epidemiology Biomarkers & Prevention 04/2009; 18(3):935-44. DOI:10.1158/1055-9965.EPI-08-0860 · 4.13 Impact Factor
Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed. The impact factor represents a rough estimation of the journal's impact factor and does not reflect the actual current impact factor. Publisher conditions are provided by RoMEO. Differing provisions from the publisher's actual policy or licence agreement may be applicable.