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TCPA v3.0: An Integrative Platform to Explore the Pan-Cancer Analysis of Functional Proteomic Data.
Chen, Mei-Ju May; Li, Jun; Wang, Yumeng; Akbani, Rehan; Lu, Yiling; Mills, Gordon B; Liang, Han.
Afiliação
  • Chen MM; Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
  • Li J; Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
  • Wang Y; Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
  • Akbani R; Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
  • Lu Y; Department of Systems Biology, University of Texas MD Anderson Cancer Center, Houston, Texas.
  • Mills GB; Department of Systems Biology, University of Texas MD Anderson Cancer Center, Houston, Texas; Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon.
  • Liang H; Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas; Department of Systems Biology, University of Texas MD Anderson Cancer Center, Houston, Texas. Electronic address: hliang1@mdanderson.org.
Mol Cell Proteomics ; 18(8 suppl 1): S15-S25, 2019 08 09.
Article em En | MEDLINE | ID: mdl-31201206
ABSTRACT
Reverse-phase protein arrays represent a powerful functional proteomics approach to characterizing cell signaling pathways and understanding their effects on cancer development. Using this platform, we have characterized ∼8,000 patient samples of 32 cancer types through The Cancer Genome Atlas and built a widely used, open-access bioinformatic resource, The Cancer Proteome Atlas (TCPA). To maximize the utility of TCPA, we have developed a new module called "TCGA Pan-Cancer Analysis," which provides comprehensive protein-centric analyses that integrate protein expression data and other TCGA data across cancer types. We further demonstrate the value of this module by examining the correlations of RPPA proteins with significantly mutated genes, assessing the predictive power of somatic copy-number alterations, DNA methylation, and mRNA on protein expression, inferring the regulatory effects of miRNAs on protein expression, constructing a co-expression network of proteins and pathways, and identifying clinically relevant protein markers. This upgraded TCPA (v3.0) will provide the cancer research community with a more powerful tool for studying functional proteomics and making translational impacts.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Software / Proteoma / Proteínas de Neoplasias / Neoplasias Idioma: En Ano de publicação: 2019 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Software / Proteoma / Proteínas de Neoplasias / Neoplasias Idioma: En Ano de publicação: 2019 Tipo de documento: Article