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.
%0 Journal Article
%1 Uhlen:2017:Science:28818916
%A Uhlen, M
%A Zhang, C
%A Lee, S
%A Sjöstedt, E
%A Fagerberg, L
%A Bidkhori, G
%A Benfeitas, R
%A Arif, M
%A Liu, Z
%A Edfors, F
%A Sanli, K
%A von Feilitzen, K
%A Oksvold, P
%A Lundberg, E
%A Hober, S
%A Nilsson, P
%A Mattsson, J
%A Schwenk, J M
%A Brunnström, H
%A Glimelius, B
%A Sjöblom, T
%A Edqvist, P H
%A Djureinovic, D
%A Micke, P
%A Lindskog, C
%A Mardinoglu, A
%A Ponten, F
%D 2017
%J Science
%K SHOULDREAD cancer-research database fulltext software
%N 6352
%R 10.1126/science.aan2507
%T A pathology atlas of the human cancer transcriptome
%U https://www.ncbi.nlm.nih.gov/pubmed/28818916
%V 357
%X 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.
@article{Uhlen:2017:Science:28818916,
abstract = {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.},
added-at = {2017-09-10T22:45:42.000+0200},
author = {Uhlen, M and Zhang, C and Lee, S and Sj{\"o}stedt, E and Fagerberg, L and Bidkhori, G and Benfeitas, R and Arif, M and Liu, Z and Edfors, F and Sanli, K and von Feilitzen, K and Oksvold, P and Lundberg, E and Hober, S and Nilsson, P and Mattsson, J and Schwenk, J M and Brunnstr{\"o}m, H and Glimelius, B and Sj{\"o}blom, T and Edqvist, P H and Djureinovic, D and Micke, P and Lindskog, C and Mardinoglu, A and Ponten, F},
biburl = {https://www.bibsonomy.org/bibtex/2684beeaf38cc82e2ab745b9cc0c5fd46/marcsaric},
doi = {10.1126/science.aan2507},
interhash = {f4e70e36336c7f53198bc0765dbc6444},
intrahash = {684beeaf38cc82e2ab745b9cc0c5fd46},
journal = {Science},
keywords = {SHOULDREAD cancer-research database fulltext software},
month = aug,
number = 6352,
pmid = {28818916},
timestamp = {2017-09-10T22:45:42.000+0200},
title = {A pathology atlas of the human cancer transcriptome},
url = {https://www.ncbi.nlm.nih.gov/pubmed/28818916},
volume = 357,
year = 2017
}