Uhlén2017 - TCGA-A2-A3XS-01A - Breast Invasive Carcinoma (female, 64 years)
This is a whole genome metabolism model of a female patient diagnosed at the age of 64 years with Breast Invasive Carcinoma affecting the patient's breast.
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-A2-A3XS-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 MODEL1707111498.
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.
- A pathology atlas of the human cancer transcriptome.
- Uhlen M, Zhang C, Lee S, Sjöstedt E, Fagerberg L, Bidkhori G, Benfeitas R, Arif M, Liu Z, Edfors F, Sanli K, von Feilitzen K, Oksvold P, Lundberg E, Hober S, Nilsson P, Mattsson J, Schwenk JM, Brunnström H, Glimelius B, Sjöblom T, Edqvist PH, Djureinovic D, Micke P, Lindskog C, Mardinoglu A, Ponten F
- Science (New York, N.Y.) , 8/ 2017 , Volume 357 , Issue 6352
- School of Biotechnology, AlbaNova University Center, KTH-Royal Institute of Technology, Stockholm, Sweden.
- 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.
- Model originally submitted by : Adil Mardinoglu
- Submitted: Aug 17, 2017 5:30:53 PM
- Last Modified: Aug 17, 2017 5:30:47 PM
- Submitted on: Aug 17, 2017 5:30:47 PM
- Submitted by: Adil Mardinoglu
- With comment: Current version of Uhlén2017 - TCGA-A2-A3XS-01A - Breast Invasive Carcinoma (female, 64 years)
- Submitted on: Aug 17, 2017 5:30:53 PM
- Submitted by: Adil Mardinoglu
- With comment: Original import of MODEL1707111498.xml.origin