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- Amirian, E. Susan, et al.
(författare)
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Aspirin, NSAIDs, and Glioma Risk : Original Data from the Glioma International Case-Control Study and a Meta-analysis
- 2019
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Ingår i: Cancer Epidemiology, Biomarkers and Prevention. - : American Association for Cancer Research (AACR). - 1055-9965 .- 1538-7755. ; 28:3, s. 555-562
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Tidskriftsartikel (refereegranskat)abstract
- Background: There have been few studies of sufficient size to address the relationship between glioma risk and the use of aspirin or NSAIDs, and results have been conflicting. The purpose of this study was to examine the associations between glioma and aspirin/NSAID use, and to aggregate these findings with prior published studies using meta-analysis.Methods: The Glioma International Case-Control Study (GICC) consists of 4,533 glioma cases and 4,171 controls recruited from 2010 to 2013. Interviews were conducted using a standardized questionnaire to obtain information on aspirin/NSAID use. We examined history of regular use for ≥6 months and duration-response. Restricted maximum likelihood meta-regression models were used to aggregate site-specific estimates, and to combine GICC estimates with previously published studies.Results: A history of daily aspirin use for ≥6 months was associated with a 38% lower glioma risk, compared with not having a history of daily use [adjusted meta-OR = 0.62; 95% confidence interval (CI), 0.54–0.70]. There was a significant duration-response trend (P = 1.67 × 10−17), with lower ORs for increasing duration of aspirin use. Duration-response trends were not observed for NSAID use. In the meta-analysis aggregating GICC data with five previous studies, there was a marginally significant association between use of aspirin and glioma (mOR = 0.84; 95% CI, 0.70–1.02), but no association for NSAID use.Conclusions: Our study suggests that aspirin may be associated with a reduced risk of glioma.Impact: These results imply that aspirin use may be associated with decreased glioma risk. Further research examining the association between aspirin use and glioma risk is warranted.
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| 3. |
- Yang, Yaohua, et al.
(författare)
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Genetic Data from Nearly 63,000 Women of European Descent Predicts DNA Methylation Biomarkers and Epithelial Ovarian Cancer Risk
- 2019
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Ingår i: Cancer Research. - : AMER ASSOC CANCER RESEARCH. - 0008-5472 .- 1538-7445. ; 79:3, s. 505-517
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Tidskriftsartikel (refereegranskat)abstract
- DNA methylation is instrumental for gene regulation. Global changes in the epigenetic landscape have been recognized as a hallmark of cancer. However, the role of DNA methylation in epithelial ovarian cancer (EOC) remains unclear. In this study, high-density genetic and DNA methylation data in white blood cells from the Framingham Heart Study (N = 1,595) were used to build genetic models to predict DNA methylation levels. These prediction models were then applied to the summary statistics of a genome-wide association study (GWAS) of ovarian cancer including 22,406 EOC cases and 40,941 controls to investigate genetically predicted DNA methylation levels in association with EOC risk. Among 62,938 CpG sites investigated, genetically predicted methylation levels at 89 CpG were significantly associated with EOC risk at a Bonferroni-corrected threshold of P < 7.94 x 10(-7). Of them, 87 were located at GWAS-identified EOC susceptibility regions and two resided in a genomic region not previously reported to be associated with EOC risk. Integrative analyses of genetic, methylation, and gene expression data identified consistent directions of associations across 12 CpG, five genes, and EOC risk, suggesting that methylation at these 12 CpG may influence EOC risk by regulating expression of these five genes, namely MAPT, HOXB3, ABHD8, ARHGAP27, and SKAP1. We identified novel DNA methylation markers associated with EOC risk and propose that methylation at multiple CpG may affect EOC risk via regulation of gene expression. Significance: Identification of novel DNA methylation markers associated with EOC risk suggests that methylation at multiple CpG may affect EOC risk through regulation of gene expression.
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