A pathology atlas of the human cancer transcriptome
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A pathology atlas of the human cancer transcriptome. / Uhlen, Mathias; Zhang, Cheng Jiao; Lee, Sunjae; Sjöstedt, Evelina; Fagerberg, Linn; Bidkhori, Gholamreza; Benfeitas, Rui; Arif, Muhammad; Liu, Zhengtao; Edfors, Fredrik; Sanli, Kemal; von Feilitzen, Kalle; Oksvold, Per; Lundberg, Emma; Hober, Sophia; Nilsson, Peter; Mattsson, Johanna Sm; Schwenk, Jochen M.; Brunnström, Hans; Glimelius, Bengt; Sjöblom, Tobias; Edqvist, Per-Henrik; Djureinovic, Dijana; Micke, Patrick; Lindskog, Cecilia; Mardinoglu, Adil; Ponten, Fredrik.
In: Science, Vol. 357, No. 6352, eaan2507, 18.08.2017.Research output: Contribution to journal › Article
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T1 - A pathology atlas of the human cancer transcriptome
AU - Uhlen, Mathias
AU - Zhang, Cheng Jiao
AU - Lee, Sunjae
AU - Sjöstedt, Evelina
AU - Fagerberg, Linn
AU - Bidkhori, Gholamreza
AU - Benfeitas, Rui
AU - Arif, Muhammad
AU - Liu, Zhengtao
AU - Edfors, Fredrik
AU - Sanli, Kemal
AU - von Feilitzen, Kalle
AU - Oksvold, Per
AU - Lundberg, Emma
AU - Hober, Sophia
AU - Nilsson, Peter
AU - Mattsson, Johanna Sm
AU - Schwenk, Jochen M.
AU - Brunnström, Hans
AU - Glimelius, Bengt
AU - Sjöblom, Tobias
AU - Edqvist, Per-Henrik
AU - Djureinovic, Dijana
AU - Micke, Patrick
AU - Lindskog, Cecilia
AU - Mardinoglu, Adil
AU - Ponten, Fredrik
PY - 2017/8/18
Y1 - 2017/8/18
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85028362951&partnerID=8YFLogxK
U2 - 10.1126/science.aan2507
DO - 10.1126/science.aan2507
M3 - Article
C2 - 28818916
AN - SCOPUS:85028362951
VL - 357
JO - Science (New York, N.Y.)
JF - Science (New York, N.Y.)
SN - 1095-9203
IS - 6352
M1 - eaan2507
ER -