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An integrated computational and experimental study uncovers FUT9 as a metabolic driver of colorectal cancer

Metabolic alterations play an important role in cancer and yet, few metabolic cancer driver genes are known. Here we perform a combined genomic and metabolic modeling analysis searching for metabolic drivers of colorectal cancer. Our analysis predicts FUT9, which catalyzes the biosynthesis of Ley gl...

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Detalles Bibliográficos
Autores principales: Auslander, Noam, Cunningham, Chelsea E, Toosi, Behzad M, McEwen, Emily J, Yizhak, Keren, Vizeacoumar, Frederick S, Parameswaran, Sreejit, Gonen, Nir, Freywald, Tanya, Bhanumathy, Kalpana K, Freywald, Andrew, Vizeacoumar, Franco J, Ruppin, Eytan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5740504/
https://www.ncbi.nlm.nih.gov/pubmed/29196508
http://dx.doi.org/10.15252/msb.20177739
Descripción
Sumario:Metabolic alterations play an important role in cancer and yet, few metabolic cancer driver genes are known. Here we perform a combined genomic and metabolic modeling analysis searching for metabolic drivers of colorectal cancer. Our analysis predicts FUT9, which catalyzes the biosynthesis of Ley glycolipids, as a driver of advanced‐stage colon cancer. Experimental testing reveals FUT9's complex dual role; while its knockdown enhances proliferation and migration in monolayers, it suppresses colon cancer cells expansion in tumorspheres and inhibits tumor development in a mouse xenograft models. These results suggest that FUT9's inhibition may attenuate tumor‐initiating cells (TICs) that are known to dominate tumorspheres and early tumor growth, but promote bulk tumor cells. In agreement, we find that FUT9 silencing decreases the expression of the colorectal cancer TIC marker CD44 and the level of the OCT4 transcription factor, which is known to support cancer stemness. Beyond its current application, this work presents a novel genomic and metabolic modeling computational approach that can facilitate the systematic discovery of metabolic driver genes in other types of cancer.