Uhlén2017 - TCGA-2Y-A9GS-01A - Liver Hepatocellular Carcinoma (male, 59 years)

Model Identifier
MODEL1707110525
Short description

This is a whole genome metabolism model of a male patient diagnosed at the age of 59 years with Liver Hepatocellular Carcinoma affecting the patient's liver.

This model was automatically generated by tINIT (Agren, R., et al. (2014). Identification of anticancer drugs for hepatocellular carcinoma through personalized genome-scale metabolic modeling. Mol Syst Biol; 10(3), 721.) using information coming from the sample TCGA-2Y-A9GS-01A from GDC Portal (Initial release 1.0, accessed via GDC API) and, where relevant, augmented with metabolic pathway information extracted from Human Metabolic Atlas.

This model has been produced by Human Pathology Atlas project ( Uhlen, M., et al.; A pathology atlas of the human cancer transcriptome. Science.) and is currently hosted on BioModels Database and identified by MODEL1707110525.

Other models with the same disease include MODEL1707110526 MODEL1707110527 MODEL1707110528 MODEL1707110529 MODEL1707110530 MODEL1707110531 MODEL1707110532 MODEL1707110533 MODEL1707110534 MODEL1707110535 MODEL1707110536 MODEL1707110537 MODEL1707110538 MODEL1707110539 MODEL1707110540 MODEL1707110541 MODEL1707110542 MODEL1707110543 MODEL1707110688 MODEL1707110791 MODEL1707110975 MODEL1707111000 MODEL1707111001 MODEL1707111002 MODEL1707112651 MODEL1707112652 MODEL1707112653 MODEL1707112654 MODEL1707112655 MODEL1707112656 MODEL1707112657 MODEL1707112658 MODEL1707112659 MODEL1707112660 MODEL1707112661 MODEL1707112662 MODEL1707112663 MODEL1707112664 MODEL1707112665 MODEL1707112666 MODEL1707112667 MODEL1707112668 MODEL1707112669 MODEL1707112670 MODEL1707112671 MODEL1707112672 MODEL1707112673 MODEL1707112674 MODEL1707113217 MODEL1707113323 MODEL1707113324 MODEL1707113325 MODEL1707113326 MODEL1707113327 MODEL1707113328 MODEL1707113329 MODEL1707113330 MODEL1707113331 MODEL1707113332 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To cite BioModels, please use: V Chelliah et al; BioModels: ten-year anniversary. Nucleic Acids Res 2015; 43 (D1): D542-D548.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.
Format
SBML (L2V3)
Related Publication
  • A pathology atlas of the human cancer transcriptome. Click here to expand
  • Mathias Uhlen, Cheng Zhang, Sunjae Lee, Evelina Sjöstedt, Linn Fagerberg, Gholamreza Bidkhori, Rui Benfeitas, Muhammad Arif, Zhengtao Liu, Fredrik Edfors, Kemal Sanli, Kalle von Feilitzen, Per Oksvold, Emma Lundberg, Sophia Hober, Peter Nilsson, Johanna Mattsson, Jochen M Schwenk, Hans Brunnström, Bengt Glimelius, Tobias Sjöblom, Per-Henrik Edqvist, Dijana Djureinovic, Patrick Micke, Cecilia Lindskog, Adil Mardinoglu, Fredrik Ponten
  • Science (New York, N.Y.) , 8/ 2017 , Volume 357 , Issue 6352 , pages: eaan2507 , PubMed ID: 28818916
  • Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm, Sweden. mathias.uhlen@scilifelab.se.
  • Cancer is one of the leading causes of death, and there is great interest in understanding the underlying molecular mechanisms involved in the pathogenesis and progression of individual tumors. We used systems-level approaches to analyze the genome-wide transcriptome of the protein-coding genes of 17 major cancer types with respect to clinical outcome. A general pattern emerged: Shorter patient survival was associated with up-regulation of genes involved in cell growth and with down-regulation of genes involved in cellular differentiation. Using genome-scale metabolic models, we show that cancer patients have widespread metabolic heterogeneity, highlighting the need for precise and personalized medicine for cancer treatment. All data are presented in an interactive open-access database (www.proteinatlas.org/pathology) to allow genome-wide exploration of the impact of individual proteins on clinical outcomes.
Contributors
Submitter of the first revision: Adil Mardinoglu
Submitter of this revision: Adil Mardinoglu

Metadata information

is (1 statement)
BioModels Database MODEL1707110525

hasTaxon (1 statement)
Taxonomy Homo sapiens

hasProperty (3 statements)
Human Disease Ontology hepatocellular carcinoma
Mathematical Modelling Ontology Constraint-based model
PATO male

isDerivedFrom (2 statements)
Genomic Data Commons Data Portal 95b97198-1a1b-4f01-90f3-6c66638371d1
BioModels Database MODEL1402200003

Curation status
Non-curated
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Auto-Generated

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