This is a whole genome metabolism model of a male patient diagnosed at the age of 58 years with Rectum Adenocarcinoma affecting the patient's colon and rectum.
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-AF-2687-01A 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 MODEL1707111946.
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.
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A pathology atlas of the human cancer transcriptome.
- 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.
Submitter of this revision: Adil Mardinoglu
Curator: Adil Mardinoglu
Metadata information
hasTaxon (1 statement)
hasProperty (3 statements)
Mathematical Modelling Ontology Constraint-based model
PATO male
isDerivedFrom (2 statements)
Genomic Data Commons Data Portal f9204a59-6877-4d06-a20e-fd7ab0859ed5
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