DNA methylation array identifies golli-MBP as a biomarker for the disease severity in childhood atopic dermatitis

Author(s):  
Kuang-Den Chen ◽  
Ying-Hsien Huang ◽  
Mindy Ming-Huey Guo ◽  
Ling-Sai Chang ◽  
Chi-Hsiang Chu ◽  
...  
2019 ◽  
Vol 48 (D1) ◽  
pp. D890-D895 ◽  
Author(s):  
Zhuang Xiong ◽  
Mengwei Li ◽  
Fei Yang ◽  
Yingke Ma ◽  
Jian Sang ◽  
...  

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.


2020 ◽  
Vol 22 (Supplement_3) ◽  
pp. iii400-iii401
Author(s):  
Kuo-Sheng Wu ◽  
Tai-Tong Wong

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.


Author(s):  
Marina Bibikova ◽  
Bret Barnes ◽  
Chan Tsan ◽  
Vincent Ho ◽  
Brandy Klotzle ◽  
...  

Oncotarget ◽  
2016 ◽  
Vol 7 (39) ◽  
pp. 64191-64202 ◽  
Author(s):  
Qiuqiong Tang ◽  
Tim Holland-Letz ◽  
Alla Slynko ◽  
Katarina Cuk ◽  
Frederik Marme ◽  
...  

2013 ◽  
Vol 109 (6) ◽  
pp. 1394-1402 ◽  
Author(s):  
C S Wilhelm-Benartzi ◽  
D C Koestler ◽  
M R Karagas ◽  
J M Flanagan ◽  
B C Christensen ◽  
...  

2021 ◽  
Author(s):  
Jennifer Lu ◽  
Darren Korbie ◽  
Matt Trau

DNA methylation is one of the most commonly studied epigenetic biomarkers, due to its role in disease and development. The Illumina Infinium methylation arrays still remains the most common method to interrogate methylation across the human genome, due to its capabilities of screening over 480, 000 loci simultaneously. As such, initiatives such as The Cancer Genome Atlas (TCGA) have utilized this technology to examine the methylation profile of over 20,000 cancer samples. There is a growing body of methods for pre-processing, normalisation and analysis of array-based DNA methylation data. However, the shape and sampling distribution of probe-wise methylation that could influence the way data should be examined was rarely discussed. Therefore, this article introduces a pipeline that predicts the shape and distribution of normalised methylation patterns prior to selection of the most optimal inferential statistics screen for differential methylation. Additionally, we put forward an alternative pipeline, which employed feature selection, and demonstrate its ability to select for biomarkers with outstanding differences in methylation, which does not require the predetermination of the shape or distribution of the data of interest. Availability: The Distribution test and the feature selection pipelines are available for download at: https://github.com/uqjlu8/DistributionTest Keywords: DNA methylation, Biomarkers, Cancers, Data Distribution, TCGA, 450K


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