GCOD - GeneChip Oncology Database

Liu, Fenglong; White, Joseph A.; Antonescu, Corina; Gusenleitner, Daniel; Quackenbush, John

doi:10.1186/1471-2105-12-46

Cited by 15 publications

(13 citation statements)

References 15 publications

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“…Over the past decade, a wealth of data on tumors, normal tissues and cell models have been generated using microarrays and analyzed to identify genes differentially expressed in cancer 10 , 11 . These data provide a unique opportunity to study expression patterns of metabolic enzymes in cancer and thereby define the metabolic program of cancer on a genome-wide scale 9 .…”

Section: Introductionmentioning

confidence: 99%

Metabolic enzyme expression highlights a key role for MTHFD2 and the mitochondrial folate pathway in cancer

et al. 2014

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Metabolic remodeling is now widely regarded as a hallmark of cancer, but it is not clear whether individual metabolic strategies are frequently exploited by many tumours. Here we compare messenger RNA profiles of 1,454 metabolic enzymes across 1,981 tumours spanning 19 cancer types to identify enzymes that are consistently differentially expressed. Our meta-analysis recovers established targets of some of the most widely used chemotherapeutics, including dihydrofolate reductase, thymidylate synthase and ribonucleotide reductase, while also spotlighting new enzymes, such as the mitochondrial proline biosynthetic enzyme PYCR1. The highest scoring pathway is mitochondrial one-carbon metabolism and is centred on MTHFD2. MTHFD2 RNA and protein are markedly elevated in many cancers and correlated with poor survival in breast cancer. MTHFD2 is expressed in the developing embryo, but is absent in most healthy adult tissues, even those that are proliferating. Our study highlights the importance of mitochondrial compartmentalization of one-carbon metabolism in cancer and raises important therapeutic hypotheses.

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Section: Introductionmentioning

confidence: 99%

Metabolic enzyme expression highlights a key role for MTHFD2 and the mitochondrial folate pathway in cancer

et al. 2014

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“…Research on the human transcriptome has produced a large amount of expression data at the gene level. Many gene-expression databases have been developed for cancer research, such as Oncomine (public version) ( 1 ), NextBio ( 2 ) and GCOD ( 3 ). However, a given gene is often spliced into multiple transcript isoforms, which in turn are translated into different proteins.…”

Section: Introductionmentioning

confidence: 99%

Cancer RNA-Seq Nexus: a database of phenotype-specific transcriptome profiling in cancer cells

Sun

et al. 2015

Nucleic Acids Res

105

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The genome-wide transcriptome profiling of cancerous and normal tissue samples can provide insights into the molecular mechanisms of cancer initiation and progression. RNA Sequencing (RNA-Seq) is a revolutionary tool that has been used extensively in cancer research. However, no existing RNA-Seq database provides all of the following features: (i) large-scale and comprehensive data archives and analyses, including coding-transcript profiling, long non-coding RNA (lncRNA) profiling and coexpression networks; (ii) phenotype-oriented data organization and searching and (iii) the visualization of expression profiles, differential expression and regulatory networks. We have constructed the first public database that meets these criteria, the Cancer RNA-Seq Nexus (CRN, http://syslab4.nchu.edu.tw/CRN). CRN has a user-friendly web interface designed to facilitate cancer research and personalized medicine. It is an open resource for intuitive data exploration, providing coding-transcript/lncRNA expression profiles to support researchers generating new hypotheses in cancer research and personalized medicine.

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“…Most of them focus only on one data type (e.g. GCOD [50], CaSNP [21] and PrognoScan [51]). canEvolve allows users to query larger number of data types when suitable.…”

Section: Discussionmentioning

confidence: 99%

canEvolve: A Web Portal for Integrative Oncogenomics

et al. 2013

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Background & ObjectiveGenome-wide profiles of tumors obtained using functional genomics platforms are being deposited to the public repositories at an astronomical scale, as a result of focused efforts by individual laboratories and large projects such as the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium. Consequently, there is an urgent need for reliable tools that integrate and interpret these data in light of current knowledge and disseminate results to biomedical researchers in a user-friendly manner. We have built the canEvolve web portal to meet this need.ResultscanEvolve query functionalities are designed to fulfill most frequent analysis needs of cancer researchers with a view to generate novel hypotheses. canEvolve stores gene, microRNA (miRNA) and protein expression profiles, copy number alterations for multiple cancer types, and protein-protein interaction information. canEvolve allows querying of results of primary analysis, integrative analysis and network analysis of oncogenomics data. The querying for primary analysis includes differential gene and miRNA expression as well as changes in gene copy number measured with SNP microarrays. canEvolve provides results of integrative analysis of gene expression profiles with copy number alterations and with miRNA profiles as well as generalized integrative analysis using gene set enrichment analysis. The network analysis capability includes storage and visualization of gene co-expression, inferred gene regulatory networks and protein-protein interaction information. Finally, canEvolve provides correlations between gene expression and clinical outcomes in terms of univariate survival analysis.ConclusionAt present canEvolve provides different types of information extracted from 90 cancer genomics studies comprising of more than 10,000 patients. The presence of multiple data types, novel integrative analysis for identifying regulators of oncogenesis, network analysis and ability to query gene lists/pathways are distinctive features of canEvolve. canEvolve will facilitate integrative and meta-analysis of oncogenomics datasets.AvailabilityThe canEvolve web portal is available at http://www.canevolve.org/.

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GCOD - GeneChip Oncology Database

Cited by 15 publications

References 15 publications

Metabolic enzyme expression highlights a key role for MTHFD2 and the mitochondrial folate pathway in cancer

Metabolic enzyme expression highlights a key role for MTHFD2 and the mitochondrial folate pathway in cancer

Cancer RNA-Seq Nexus: a database of phenotype-specific transcriptome profiling in cancer cells

canEvolve: A Web Portal for Integrative Oncogenomics

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