Endometrial Cancer (Case 36)

Author(s):  
Randolph Heinzel ◽  
David Holtz
2011 ◽  
Vol 14 (4) ◽  
pp. 328-332 ◽  
Author(s):  
Tracy A. O'Mara ◽  
Kaltin Ferguson ◽  
Paul Fahey ◽  
Louise Marquart ◽  
Hannah P. Yang ◽  
...  

Several single nucleotide polymorphisms (SNPs) in candidate genes of DNA repair and hormone pathways have been reported to be associated with endometrial cancer risk. We sought to confirm these associations in two endometrial cancer case-control sample sets and used additional data from an existing genome-wide association study to prioritize an additional SNP for further study. Five SNPs from the CHEK2, MGMT, SULT1E1 and SULT1A1 genes, genotyped in a total of 1597 cases and 1507 controls from two case-control studies, the Australian National Endometrial Cancer Study and the Polish Endometrial Cancer Study, were assessed for association with endometrial cancer risk using logistic regression analysis. Imputed data was drawn for CHEK2 rs8135424 for 666 cases from the Study of Epidemiology and Risk factors in Cancer Heredity study and 5190 controls from the Wellcome Trust Case Control Consortium. We observed no association between SNPs in the MGMT, SULT1E1 and SULT1A1 genes and endometrial cancer risk. The A allele of the rs8135424 CHEK2 SNP was associated with decreased risk of endometrial cancer (adjusted per-allele OR 0.83; 95%CI 0.70-0.98; p = .03) however this finding was opposite to that previously published. Imputed data for CHEK2 rs8135424 supported the direction of effect reported in this study (OR 0.85; 95% CI 0.65–1.10). Previously reported endometrial cancer risk associations with SNPs from in genes involved in estrogen metabolism and DNA repair were not replicated in our larger study population. This study highlights the need for replication of candidate gene SNP studies using large sample groups, to confirm risk associations and better prioritize downstream studies to assess the causal relationship between genetic variants and cancer risk. Our findings suggest that the CHEK2 SNP rs8135424 be prioritized for further study as a genetic factor associated with risk of endometrial cancer.


2009 ◽  
Vol 16 (5) ◽  
pp. 630-633 ◽  
Author(s):  
Massimiliano Fambrini ◽  
Gianni Bargelli ◽  
Elena Peruzzi ◽  
Anna Maria Buccoliero ◽  
Annalisa Pieralli ◽  
...  

Author(s):  
Sandra Radović ◽  
Ana Meyra Potkonjak ◽  
Zorica Knezović ◽  
Marija Jukić ◽  
Katarina Kličan ◽  
...  

2018 ◽  
Vol 119 (4) ◽  
pp. 243-247
Author(s):  
Osman Nuri Dilek ◽  
Emine Özlem Gür ◽  
Turan Acar ◽  
Serpil Aydoğmuş

Author(s):  
Tao Wang

The importance of the gene × gene (G × G) and gene × environment (G × E) interaction has been widely recognized. It is statistically challenging to account for interactions in the analysis of genome-wide association data. In this chapter, we introduce a gene-based method for modeling G × G and G × E interactions under the regression framework. We evaluate the type 1 error rate and power of this new method by simulations. We apply this method to the endometrial cancer case-control dataset.


2008 ◽  
Vol 111 (3) ◽  
pp. 583-588 ◽  
Author(s):  
J. Albareda ◽  
M. Herrera ◽  
A. Lopez Salva ◽  
J. Garcia Donas ◽  
R. Gonzalez

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