scholarly journals The SMART App: an interactive web application for comprehensive DNA methylation analysis and visualization

2019 ◽  
Vol 12 (1) ◽  
Author(s):  
Yin Li ◽  
Di Ge ◽  
Chunlai Lu
Keyword(s):  
Data Mining ◽  
Dna Methylation ◽  
Web Application ◽  
Web Applications ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Methylation Analysis ◽  
Cancer Genome Atlas ◽  
User Friendly ◽  
Dna Methylation Analysis

Abstract Background Data mining of The Cancer Genome Atlas (TCGA) data has significantly facilitated cancer genome research and provided unprecedented opportunities for cancer researchers. However, existing web applications for DNA methylation analysis does not adequately address the need of experimental biologists, and many additional functions are often required. Results To facilitate DNA methylation analysis, we present the SMART (Shiny Methylation Analysis Resource Tool) App, a user-friendly and easy-to-use web application for comprehensively analyzing the DNA methylation data of TCGA project. The SMART App integrates multi-omics and clinical data with DNA methylation and provides key interactive and customized functions including CpG visualization, pan-cancer methylation profile, differential methylation analysis, correlation analysis and survival analysis for users to analyze the DNA methylation in diverse cancer types in a multi-dimensional manner. Conclusion The SMART App serves as a new approach for users, especially wet-bench scientists with no programming background, to analyze the scientific big data and facilitate data mining. The SMART App is available at http://www.bioinfo-zs.com/smartapp.

scholarly journals FXR silencing in human colon cancer by DNA methylation and KRAS signaling

2014 ◽  
Vol 306 (1) ◽  
pp. G48-G58 ◽  
Author(s):  
Ann M. Bailey ◽  
Le Zhan ◽  
Dipen Maru ◽  
Imad Shureiqi ◽  
Curtis R. Pickering ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Colon Cancer ◽  
Epithelial To Mesenchymal Transition ◽  
Human Colon ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Human Colon Cancer ◽  
Mesenchymal Transition ◽  
Cancer Genome Atlas ◽  
Genome Atlas

Farnesoid X receptor (FXR) is a bile acid nuclear receptor described through mouse knockout studies as a tumor suppressor for the development of colon adenocarcinomas. This study investigates the regulation of FXR in the development of human colon cancer. We used immunohistochemistry of FXR in normal tissue ( n = 238), polyps ( n = 32), and adenocarcinomas, staged I–IV ( n = 43, 39, 68, and 9), of the colon; RT-quantitative PCR, reverse-phase protein array, and Western blot analysis in 15 colon cancer cell lines; NR1H4 promoter methylation and mRNA expression in colon cancer samples from The Cancer Genome Atlas; DNA methyltransferase inhibition; methyl-DNA immunoprecipitation (MeDIP); bisulfite sequencing; and V-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog (KRAS) knockdown assessment to investigate FXR regulation in colon cancer development. Immunohistochemistry and quantitative RT-PCR revealed that expression and function of FXR was reduced in precancerous lesions and silenced in a majority of stage I-IV tumors. FXR expression negatively correlated with phosphatidylinositol-4, 5-bisphosphate 3 kinase signaling and the epithelial-to-mesenchymal transition. The NR1H4 promoter is methylated in ∼12% colon cancer The Cancer Genome Atlas samples, and methylation patterns segregate with tumor subtypes. Inhibition of DNA methylation and KRAS silencing both increased FXR expression. FXR expression is decreased early in human colon cancer progression, and both DNA methylation and KRAS signaling may be contributing factors to FXR silencing. FXR potentially suppresses epithelial-to-mesenchymal transition and other oncogenic signaling cascades, and restoration of FXR activity, by blocking silencing mechanisms or increasing residual FXR activity, represents promising therapeutic options for the treatment of colon cancer.

scholarly journals DNA Methylation–Based Classifier for Accurate Molecular Diagnosis of Bone Sarcomas

2017 ◽  
pp. 1-11 ◽  
Author(s):  
S. Peter Wu ◽  
Benjamin T. Cooper ◽  
Fang Bu ◽  
Christopher J. Bowman ◽  
J. Keith Killian ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Synovial Sarcoma ◽  
Ewing Sarcoma ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Clinical Samples ◽  
Cpg Sites ◽  
A Genome ◽  
Cancer Genome Atlas ◽  
Genome Atlas

Purpose Pediatric sarcomas provide a unique diagnostic challenge. There is considerable morphologic overlap between entities, increasing the importance of molecular studies in the diagnosis, treatment, and identification of therapeutic targets. We developed and validated a genome-wide DNA methylation–based classifier to differentiate between osteosarcoma, Ewing sarcoma, and synovial sarcoma. Methods DNA methylation status of 482,421 CpG sites in 10 Ewing sarcoma, 11 synovial sarcoma, and 15 osteosarcoma samples were determined using the Illumina Infinium HumanMethylation450 array. We developed a random forest classifier trained from the 400 most differentially methylated CpG sites within the training set of 36 sarcoma samples. This classifier was validated on data drawn from The Cancer Genome Atlas synovial sarcoma, TARGET-Osteosarcoma, and a recently published series of Ewing sarcoma. Results Methylation profiling revealed three distinct patterns, each enriched with a single sarcoma subtype. Within the validation cohorts, all samples from The Cancer Genome Atlas were accurately classified as synovial sarcoma (10 of 10; sensitivity and specificity, 100%), all but one sample from TARGET-Osteosarcoma were classified as osteosarcoma (85 of 86; sensitivity, 98%; specificity, 100%), and 14 of 15 Ewing sarcoma samples were classified correctly (sensitivity, 93%; specificity, 100%). The single misclassified osteosarcoma sample demonstrated high EWSR1 and ETV1 expression on RNA sequencing, although no fusion was found on manual curation of the transcript sequence. Two additional clinical samples that were difficult to classify by morphology and molecular methods were classified as osteosarcoma; one had been suspected of being a synovial sarcoma and the other of being Ewing sarcoma on initial diagnosis. Conclusion Osteosarcoma, synovial sarcoma, and Ewing sarcoma have distinct epigenetic profiles. Our validated methylation-based classifier can be used to provide diagnostic assistance when histologic and standard techniques are inconclusive.

scholarly journals Exploring Drivers of Gene Expression in The Cancer Genome Atlas

2017 ◽  
Author(s):  
Andrea Rau ◽  
Michael Flister ◽  
Hallgeir Rui ◽  
Paul L. Auer
Keyword(s):  
Gene Expression ◽  
Germ Line ◽  
Molecular Data ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Web Based ◽  
Statistical Framework ◽  
Cancer Genome Atlas ◽  
User Friendly ◽  
Genome Atlas

The Cancer Genome Atlas (TCGA) has greatly advanced cancer research by generating, curating, and publicly releasing deeply measured molecular data from thousands of tumor samples. In particular, gene expression measures, both within and across cancer types, have been used to determine the genes and proteins that are active in tumor cells. To more thoroughly investigate the behavior of gene expression in TCGA tumor samples, we introduce a statistical framework for partitioning the variation in gene expression due to a variety of molecular variables including somatic mutations, transcription factors (TFs), microRNAs, copy number alternations, methylation, and germ-line genetic variation. As proof-of-principle, we identify and validate specific TFs that influence the expression of PTPN14 in breast cancer cells. We provide a freely available, user-friendly, browseable interactive web-based application for exploring the results of our transcriptome-wide analyses across 17 different cancers in TCGA at http://ls-shiny-prod.uwm.edu/edge_in_tcga.

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