DNA methylation array analysis identifies breast cancer associated RPTOR, MGRN1 and RAPSN hypomethylation in peripheral blood DNA

Purpose Epigenetic alterations in tissues targeted for cancer play a causal role in carcinogenesis. Changes in DNA methylation in nontarget tissues, specifically peripheral blood, can also affect risk of malignant disease. We sought to identify specific profiles of DNA methylation in peripheral blood that are associated with bladder cancer risk and therefore serve as an epigenetic marker of disease susceptibility. Methods We performed genome-wide DNA methylation profiling on participants involved in a population-based incident case-control study of bladder cancer. Results In a training set of 112 cases and 118 controls, we identified a panel of 9 CpG loci whose profile of DNA methylation was significantly associated with bladder cancer in a masked, independent testing series of 111 cases and 119 controls (P < .0001). Membership in three of the most methylated classes was associated with a 5.2-fold increased risk of bladder cancer (95% CI, 2.8 to 9.7), and a model that included the methylation classification, participant age, sex, smoking status, and family history of bladder cancer was a significant predictor of bladder cancer (area under the curve, 0.76; 95% CI, 0.70 to 0.82). CpG loci associated with bladder cancer and aging had neighboring sequences enriched for transcription-factor binding sites related to immune modulation and forkhead family members. Conclusion These results indicate that profiles of epigenetic states in blood are associated with risk of bladder cancer and signal the potential utility of epigenetic profiles in peripheral blood as novel markers of susceptibility to this and other malignancies.

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Batch Effects and Pathway Analysis: Two Potential Perils in Cancer Studies Involving DNA Methylation Array Analysis

Cancer Epidemiology Biomarkers & Prevention ◽

10.1158/1055-9965.epi-13-0114 ◽

2013 ◽

Vol 22 (6) ◽

pp. 1052-1060 ◽

Cited By ~ 58

Author(s):

Kristin N. Harper ◽

Brandilyn A. Peters ◽

Mary V. Gamble

Keyword(s):

Dna Methylation ◽

Pathway Analysis ◽

Batch Effects ◽

Array Analysis ◽

Methylation Array ◽

Dna Methylation Array ◽

Cancer Studies

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EWAS Data Hub: a resource of DNA methylation array data and metadata

Nucleic Acids Research ◽

10.1093/nar/gkz840 ◽

2019 ◽

Vol 48 (D1) ◽

pp. D890-D895 ◽

Cited By ~ 6

Author(s):

Zhuang Xiong ◽

Mengwei Li ◽

Fei Yang ◽

Yingke Ma ◽

Jian Sang ◽

...

Keyword(s):

Dna Methylation ◽

Complex Traits ◽

Cell Types ◽

Great Promise ◽

Methylation Array ◽

Array Data ◽

The Past ◽

Comprehensive Collection ◽

Dna Methylation Array ◽

Brain Parts

Abstract Epigenome-Wide Association Study (EWAS) has become an effective strategy to explore epigenetic basis of complex traits. Over the past decade, a large amount of epigenetic data, especially those sourced from DNA methylation array, has been accumulated as the result of numerous EWAS projects. We present EWAS Data Hub (https://bigd.big.ac.cn/ewas/datahub), a resource for collecting and normalizing DNA methylation array data as well as archiving associated metadata. The current release of EWAS Data Hub integrates a comprehensive collection of DNA methylation array data from 75 344 samples and employs an effective normalization method to remove batch effects among different datasets. Accordingly, taking advantages of both massive high-quality DNA methylation data and standardized metadata, EWAS Data Hub provides reference DNA methylation profiles under different contexts, involving 81 tissues/cell types (that contain 25 brain parts and 25 blood cell types), six ancestry categories, and 67 diseases (including 39 cancers). In summary, EWAS Data Hub bears great promise to aid the retrieval and discovery of methylation-based biomarkers for phenotype characterization, clinical treatment and health care.

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MBRS-14. INTEGRATING CLINICAL AND GENOMIC CHARACTERISTICS IN PEDIATRIC MEDULLOBLASTOMA SUBTYPES IN A SINGLE COHORT IN TAIWAN

Neuro-Oncology ◽

10.1093/neuonc/noaa222.531 ◽

2020 ◽

Vol 22 (Supplement_3) ◽

pp. iii400-iii401

Author(s):

Kuo-Sheng Wu ◽

Tai-Tong Wong

Keyword(s):

Dna Methylation ◽

Cluster Analysis ◽

Treatment Strategies ◽

Clinical Results ◽

Tumor Location ◽

Molecular Subgroups ◽

Methylation Array ◽

Metastatic Rate ◽

Pediatric Medulloblastoma ◽

Dna Methylation Array

Abstract BACKGROUND Medulloblastoma (MB) was classified to 4 molecular subgroups: WNT, SHH, group 3 (G3), and group 4 (G4) with the demographic and clinical differences. In 2017, The heterogeneity within MB was proposed, and 12 subtypes with distinct molecular and clinical characteristics. PATIENTS AND METHODS: PATIENTS AND METHODS We retrieved 52 MBs in children to perform RNA-Seq and DNA methylation array. Subtype cluster analysis performed by similarity network fusion (SNF) method. With clinical results and molecular profiles, the characteristics including age, gender, histological variants, tumor location, metastasis status, survival, cytogenetic and genetic aberrations among MB subtypes were identified. RESULTS In this cohort series, 52 childhood MBs were classified into 11 subtypes by SNF cluster analysis. WNT tumors shown no metastasis and 100% survival rate. All WNT tumors located on midline in 4th ventricle. Monosomy 6 presented in WNT α, but not in β subtype. SHH α and β occurred in children, while SHH γ in infant. Among SHH tumors, α subtype showed the worst outcome. G3 γ showed the highest metastatic rate and worst survival associated with MYC amplification. G4 α has the highest metastatic rate, however G4 γ showed the worst survival. CONCLUSION We identified molecular subgroups and subtypes of MBs based on gene expression and DNA methylation profile in children in our cohort series. The results may contribute to the establishment of nation-wide correlated optimal diagnosis and treatment strategies for MBs in infant and children.

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