The sweet and sour of cancer: glycans as novel therapeutic targets

Fuster, Mark M.; Esko, Jeffrey D.

doi:10.1038/nrc1649

Cited by 1,245 publications

(1,126 citation statements)

References 131 publications

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“…These experiments show that FUT9 knockdown enhances cell migration (Fig 3F), while FUT9 overexpression produces the opposite response (Fig 3G). Interestingly, changes in glycosylation pattern are known to modulate cell migration, cell–cell adhesion, cell signaling, growth, and metastasis (Hakomori, 1996; Fuster & Esko, 2005; de Freitas Junior & Morgado‐Díaz, 2016). As FUT9 belongs to the glycosyltransferase family, to gain mechanistic insight we next investigated how loss of FUT9 may enhance cell migration.…”

Section: Resultsmentioning

confidence: 99%

An integrated computational and experimental study uncovers FUT 9 as a metabolic driver of colorectal cancer

Auslander

Cunningham

Toosi

et al. 2017

Molecular Systems Biology

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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.

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Section: Resultsmentioning

confidence: 99%

An integrated computational and experimental study uncovers FUT 9 as a metabolic driver of colorectal cancer

Auslander

Cunningham

Toosi

et al. 2017

Molecular Systems Biology

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“…Differences in glycomes were found to be related with various cancers (Fuster & Esko, 2005; Adamczyk et al ., 2012). Recently, Kristic et al .…”

Section: Omics and Agingmentioning

confidence: 99%

Integration of ‘omics’ data in aging research: from biomarkers to systems biology

et al. 2015

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SummaryAge is the strongest risk factor for many diseases including neurodegenerative disorders, coronary heart disease, type 2 diabetes and cancer. Due to increasing life expectancy and low birth rates, the incidence of age‐related diseases is increasing in industrialized countries. Therefore, understanding the relationship between diseases and aging and facilitating healthy aging are major goals in medical research. In the last decades, the dimension of biological data has drastically increased with high‐throughput technologies now measuring thousands of (epi) genetic, expression and metabolic variables. The most common and so far successful approach to the analysis of these data is the so‐called reductionist approach. It consists of separately testing each variable for association with the phenotype of interest such as age or age‐related disease. However, a large portion of the observed phenotypic variance remains unexplained and a comprehensive understanding of most complex phenotypes is lacking. Systems biology aims to integrate data from different experiments to gain an understanding of the system as a whole rather than focusing on individual factors. It thus allows deeper insights into the mechanisms of complex traits, which are caused by the joint influence of several, interacting changes in the biological system. In this review, we look at the current progress of applying omics technologies to identify biomarkers of aging. We then survey existing systems biology approaches that allow for an integration of different types of data and highlight the need for further developments in this area to improve epidemiologic investigations.

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“…It has long been known that glycans also influence tumour progression, invasion and metastasis. 1 In this context, changes in cell surface glycosylation influence the way cancer cells interact with the extracellular matrix, neighbouring cells and the epithelium of the target organ, during metastatic invasion. [1][2][3] One of the key players in cancer progression is thought to be the monosaccharide sialic acid, which caps the end of Oand N-linked glycoproteins.…”

Section: Introductionmentioning

confidence: 99%

“…1 In this context, changes in cell surface glycosylation influence the way cancer cells interact with the extracellular matrix, neighbouring cells and the epithelium of the target organ, during metastatic invasion. [1][2][3] One of the key players in cancer progression is thought to be the monosaccharide sialic acid, which caps the end of Oand N-linked glycoproteins. 1 Certain sialyltransferases are overexpressed in colorectal and breast cancers, increasing the total content of sialic acid on the cell surface.…”

Section: Introductionmentioning

confidence: 99%

“…[1][2][3] One of the key players in cancer progression is thought to be the monosaccharide sialic acid, which caps the end of Oand N-linked glycoproteins. 1 Certain sialyltransferases are overexpressed in colorectal and breast cancers, increasing the total content of sialic acid on the cell surface. 4 High levels of sialic acid have been correlated with metastatic disease and poor prognosis.…”

Section: Introductionmentioning

confidence: 99%

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