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Genetic Data from Nearly 63,000 Women of European Descent Predicts DNA Methylation Biomarkers and Epithelial Ovarian Cancer Risk.

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 × 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.

SUBMITTER: Yang Y

PROVIDER: S-EPMC6359948 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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Genetic Data from Nearly 63,000 Women of European Descent Predicts DNA Methylation Biomarkers and Epithelial Ovarian Cancer Risk.

Yang Yaohua Y Wu Lang L Shu Xiang X Lu Yingchang Y Shu Xiao-Ou XO Cai Qiuyin Q Beeghly-Fadiel Alicia A Li Bingshan B Ye Fei F Berchuck Andrew A Anton-Culver Hoda H Banerjee Susana S Benitez Javier J Bjørge Line L Brenton James D JD Butzow Ralf R Campbell Ian G IG Chang-Claude Jenny J Chen Kexin K Cook Linda S LS Cramer Daniel W DW deFazio Anna A Dennis Joe J Doherty Jennifer A JA Dörk Thilo T Eccles Diana M DM Edwards Digna Velez DV Fasching Peter A PA Fortner Renée T RT Gayther Simon A SA Giles Graham G GG Glasspool Rosalind M RM Goode Ellen L EL Goodman Marc T MT Gronwald Jacek J Harris Holly R HR Heitz Florian F Hildebrandt Michelle A MA Høgdall Estrid E Høgdall Claus K CK Huntsman David G DG Kar Siddhartha P SP Karlan Beth Y BY Kelemen Linda E LE Kiemeney Lambertus A LA Kjaer Susanne K SK Koushik Anita A Lambrechts Diether D Le Nhu D ND Levine Douglas A DA Massuger Leon F LF Matsuo Keitaro K May Taymaa T McNeish Iain A IA Menon Usha U Modugno Francesmary F Monteiro Alvaro N AN Moorman Patricia G PG Moysich Kirsten B KB Ness Roberta B RB Nevanlinna Heli H Olsson Håkan H Onland-Moret N Charlotte NC Park Sue K SK Paul James J Pearce Celeste L CL Pejovic Tanja T Phelan Catherine M CM Pike Malcolm C MC Ramus Susan J SJ Riboli Elio E Rodriguez-Antona Cristina C Romieu Isabelle I Sandler Dale P DP Schildkraut Joellen M JM Setiawan Veronica W VW Shan Kang K Siddiqui Nadeem N Sieh Weiva W Stampfer Meir J MJ Sutphen Rebecca R Swerdlow Anthony J AJ Szafron Lukasz M LM Teo Soo Hwang SH Tworoger Shelley S SS Tyrer Jonathan P JP Webb Penelope M PM Wentzensen Nicolas N White Emily E Willett Walter C WC Wolk Alicja A Woo Yin Ling YL Wu Anna H AH Yan Li L Yannoukakos Drakoulis D Chenevix-Trench Georgia G Sellers Thomas A TA Pharoah Paul D P PDP Zheng Wei W Long Jirong J

Cancer research 20181217 3

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 (<i>N</i> = 1,595) were used to build genetic models to predict DNA methylation levels. These prediction models were then applied to the summary statistics ...[more]

PMID: 30559148

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Project description:BACKGROUND:Observational epidemiological studies have shown that high body mass index (BMI) is associated with a reduced risk of breast cancer in premenopausal women but an increased risk in postmenopausal women. It is unclear whether this association is mediated through shared genetic or environmental factors. METHODS:We applied Mendelian randomization to evaluate the association between BMI and risk of breast cancer occurrence using data from two large breast cancer consortia. We created a weighted BMI genetic score comprising 84 BMI-associated genetic variants to predicted BMI. We evaluated genetically predicted BMI in association with breast cancer risk using individual-level data from the Breast Cancer Association Consortium (BCAC) (cases? = ?46,325, controls? = ?42,482). We further evaluated the association between genetically predicted BMI and breast cancer risk using summary statistics from 16,003 cases and 41,335 controls from the Discovery, Biology, and Risk of Inherited Variants in Breast Cancer (DRIVE) Project. Because most studies measured BMI after cancer diagnosis, we could not conduct a parallel analysis to adequately evaluate the association of measured BMI with breast cancer risk prospectively. RESULTS:In the BCAC data, genetically predicted BMI was found to be inversely associated with breast cancer risk (odds ratio [OR]? = ?0.65 per 5 kg/m2 increase, 95% confidence interval [CI]: 0.56-0.75, p = 3.32?×?10-10). The associations were similar for both premenopausal (OR?? = ??0.44, 95% CI:0.31-0.62, p? = ?9.91?×?10-8) and postmenopausal breast cancer (OR? = ?0.57, 95%?CI: 0.46-0.71, p? = ?1.88?×?10-8). This association was replicated in the data from the DRIVE consortium (OR? = ?0.72, 95%?CI: 0.60-0.84, p?? = ??1.64?×?10-7). Single marker analyses identified 17 of the 84 BMI-associated single nucleotide polymorphisms (SNPs) in association with breast cancer risk at p?<?0.05; for 16 of them, the allele associated with elevated BMI was associated with reduced breast cancer risk. CONCLUSIONS:BMI predicted by genome-wide association studies (GWAS)-identified variants is inversely associated with the risk of both pre- and postmenopausal breast cancer. The reduced risk of postmenopausal breast cancer associated with genetically predicted BMI observed in this study differs from the positive association reported from studies using measured adult BMI. Understanding the reasons for this discrepancy may reveal insights into the complex relationship of genetic determinants of body weight in the etiology of breast cancer.

Project description:MicroRNAs (miRNAs) are small non-coding RNA molecules regulating gene expression with diagnostic potential in different diseases, including epithelial ovarian carcinomas (EOC). As only a few studies have been published on the identification of stable endogenous miRNA in EOC, there is no consensus which miRNAs should be used aiming standardization. Currently, U6-snRNA is widely adopted as a normalization control in RT-qPCR when investigating miRNAs in EOC; despite its variable expression across cancers being reported. Therefore, our goal was to compare different missing data and normalization approaches to investigate their impact on the choice of stable endogenous controls and subsequent survival analysis while performing expression analysis of miRNAs by RT-qPCR in most frequent subtype of EOC: high-grade serous carcinoma (HGSC). 40 miRNAs were included based on their potential as stable endogenous controls or as biomarkers in EOC. Following RNA extraction from formalin-fixed paraffin embedded tissues from 63 HGSC patients, RT-qPCR was performed with a custom panel covering 40 target miRNAs and 8 controls. The raw data was analyzed by applying various strategies regarding choosing stable endogenous controls (geNorm, BestKeeper, NormFinder, the comparative ΔCt method and RefFinder), missing data (single/multiple imputation), and normalization (endogenous miRNA controls, U6-snRNA or global mean). Based on our study, we propose hsa-miR-23a-3p and hsa-miR-193a-5p, but not U6-snRNA as endogenous controls in HGSC patients. Our findings are validated in two external cohorts retrieved from the NCBI Gene Expression Omnibus database. We present that the outcome of stability analysis depends on the histological composition of the cohort, and it might suggest unique pattern of miRNA stability profiles for each subtype of EOC. Moreover, our data demonstrates the challenge of miRNA data analysis by presenting various outcomes from normalization and missing data imputation strategies on survival analysis.

Project description:BackgroundEpithelial ovarian cancer (EOC) is an aggressive disease with poor prognosis. The expression of cytokine-induced apoptosis inhibitor 1 (CIAPIN1) correlates with the malignant progression of several cancers. However, the relationship between the subcellular localization of CIAPIN1 and clinical characteristics in EOC remains unclear.MethodsImmunohistochemistry was performed to detect CIAPIN1 expression in 108 EOC tissues. CIAPIN1 expressions in eight fresh EOC tissues were detected by Western blotting. The relationship between CIAPIN1 subcellular expression and patients' clinicopathological features, including prognosis, was evaluated. Immunohistochemistry and immunofluorescence were employed to assess the CIAPIN1 subcellular localization in the EOC cell lines A2780 and HO8910. In addition, all patients were followed up to assess the prognostic value of CIAPIN1 in patients with EOC.ResultsCIAPIN1 is highly expressed in EOC, but is present at low levels in paired non-cancerous ovarian epithelial tissues. The results of Western blotting were in accordance with the immunohistochemical results. Poor differentiation of the tumors and EOC cell lines correlated with higher levels of CIAPIN1 nuclear expression. CIAPIN1 nuclear expression significantly correlated with the Federation International of Gynecology and Obstetrics (FIGO) stage and histological differentiation (P?=?0.034 and P?<?0.0001, respectively). Moreover, nuclear localization of CIAPIN1 was selected as an unfavorable prognostic factor by both univariate and multivariate analyses ( P?<?0.001). However, no significant correlations were observed between cytoplasmic localization of CIAPIN1 and clinicopathological parameters.ConclusionsCIAPIN1 might play a crucial role in the differentiation of EOC cells. Elevated expression of nuclear CIAPIN1 negatively correlated with the survival of EOC patients, suggesting that nuclear CIAPIN1 might serve as a prognostic biomarker for EOC patients.

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Genetic Data from Nearly 63,000 Women of European Descent Predicts DNA Methylation Biomarkers and Epithelial Ovarian Cancer Risk.

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Genetic Data from Nearly 63,000 Women of European Descent Predicts DNA Methylation Biomarkers and Epithelial Ovarian Cancer Risk.

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