Systems approach to characterize the metabolism of liver cancer stem cells expressing CD133

Hur, Wonhee; Ryu, Jae Yong; Kim, Hyun Uk; Hong, Sung Woo; Lee, Eun Byul; Lee, Sang Yup; Yoon, Seung Kew

doi:10.1038/srep45557

Cited by 37 publications

(36 citation statements)

References 52 publications

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“…HCC stem cells possess the capacity for self-renewal, differentiation, and responsible for tumor initiation, proliferation and progression. CD44 and CD133 were highly enriched with properties of HCC stem cells [ 36 , 37 ]. Importantly, depletion of B7-H4 led to the decreased CD44 + /CD133 + double positive cell subpopulation, suggesting that the overexpression of B7-H4 in HCC cells might be potentially associated with its stemness properties.…”

Section: Discussionmentioning

confidence: 99%

B7-H4 overexpression is essential for early hepatocellular carcinoma progression and recurrence

Kang

Sun

et al. 2017

Oncotarget

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B7-H4, another member of costimulatory molecule, has been shown to be overexpressed in multiple types of tumors, including hepatocellular carcinoma (HCC). However, the specific biological role of B7-H4 in HCC still needs to be further explored. In this study, we observed that B7-H4 was highly overexpressed in HCC tissues and cells, and its overexpression strongly correlated with patient's TNM stage, overall survival and early recurrence. Downregulation of B7-H4 significantly suppressed cell growth, invasion, and stemness of HCC by inducing apoptosis in the in vitro experiment. In addition, depletion of B7-H4 could help restore CD8+ T anti-tumor immunity by elevating the expression and secretion levels of CD107a, granzyme A, granzyme B, perforin and IFN-γ. In a xenografted mouse model of HCC, stable depletion of B7-H4 resulted in significantly smaller mean tumor volume and less mean tumor weight after 30 days of growth, compared to the control group. Together, our results provide insights into the diverse functions of B7-H4 involved in the pathogenesis, recurrence and anti-tumor immunity of HCC, indicating B7-H4 as a novel and effective approach for future treatment strategies that benefits anticancer therapy.

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Section: Discussionmentioning

confidence: 99%

B7-H4 overexpression is essential for early hepatocellular carcinoma progression and recurrence

Kang

Sun

et al. 2017

Oncotarget

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“…Further, CD133+ cells stemness features are significantly reduced when glycolysis is inhibited [143] . Extensive trascriptome and metabolome analysis of CD133+ HCC cells revealed the key role of MYC in the regulation of glycolytic metabolism in HCC CSCs [144] .…”

Section: Metabolic Reprogrammingmentioning

confidence: 99%

“…When specifically looking at the stem cell compartment, it has been demonstrated that stem-like cells rely on fatty acid oxidation (FAO) for the generation of ATP and NADH [145] [ Figure 2]. Metabolism analysis has revealed that NAD+ concentrations are increased in CD133+ cells, and this is directly correlated with SIRT1-dependent enhanced FAO [144] . In HCC, genome-wide transcriptional profiling and Ingenuity pathway analysis have suggested NANOG to be the connecting point between FAO and stem-like features, because of its simultaneous OXPHOS repression and FAO activation actions [145] .…”

Section: Metabolic Reprogrammingmentioning

confidence: 99%

Stemness features in liver cancer

Correnti¹,

Booijink²,

Maira³

et al. 2018

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Heterogeneity is a cardinal hallmark of cancer, including primary liver cancer (PLC), and occurs at different layers including putative cell-of-origin. Current evidence suggests that within cellular subpopulations in PLC there are stem-like cells, the cancer stem cells (CSCs). The CSC concept has been recently proposed as an explanation of such intra-tumor heterogeneity. According to this model, CSCs are responsible for tumor initiation, recurrence, metastasis as well as drug-resistance. However, although the CSC hypothesis is intriguing and supported by a large number of experimental studies, there are still open questions regarding the origin of putative CSCs. Since chemoresistance and recurrence represent major issues in PLC treatment, the development of new therapeutic strategies is needed, for which a good understanding of tumor behavior and in particular of CSCs biology is an imperative prerequisite. In this review we summarize the regulatory pathways that support CSC features in PLC. Moreover, we highlight the key features of hepatic CSC, in terms of enhanced drug-resistance, increased metastatic potential and metabolic rearrangement. Knowledge of the molecular mechanisms underlying CSC biology may provide novel options for PLC combination therapies.Primary liver cancer (PLC) is one of the most common cancers worldwide and the second leading cause of cancer-related mortality [1,2] . The major forms of PLC comprise hepatocellular carcinoma (HCC) and cholangiocarcinoma (CCA) [1,3-5] . HCC accounts for approximately 90% of all PLCs [1,3] , while CCA is the second most common form and accounts for about 5% of all PLCs [3][4][5] . HCC causes over 600,000 deaths worldwide annually, and its incidence and mortality are increasing at a fast rate [6-10] . On the other hand, CCA is characterized by a very poor prognosis, with a 5-years survival lower than 20%, and its incidence and worldwide mortality are also increasing [5,[11][12][13] . The high mortality rate of CCA may depend on its nonspecific or silent clinical features and the lack of specific markers that make it difficult to diagnose [14][15][16] .Many studies carried out in these last years have attempted to define which type of epithelial cell [hepatocytes, cholangiocytes, hepatic progenitor cells (HPCs) or all three] should be considered as the PLC cell of origin [17] . For a long time, HCC and CCA have been commonly accepted to derive from hepatocytes and cholangiocytes, respectively. Since mature hepatocytes and cholangiocytes have an enormous selfrenewal capacity and longevity, they meet the requirements to be targets for oncogenesis [17][18][19][20][21][22][23] . Detailed analyses of a wide range of PLC tumor types have reported that a rare form of combined HCC-CCA (cHCC-CCA) has intermediate characteristics between HCC and intrahepatic CCA (iCCA), suggesting that they could share the same stem/progenitor cell origin [18][19][20][21][22][23][24] . In this regard, since most PLCs arise on the background of chronic liver disease in the presence of an ex...

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“…In addition to facilitating the analysis and visualization of existing GEMs, RAVEN particularly aimed to assist semi-automated draft model reconstruction, utilizing existing template GEMs and the KEGG database [10]. Since publication, RAVEN has been used in GEMs reconstruction for a wide variety of organisms, ranging from bacteria [11], archaea [12] to human gut microbiome [13], eukaryotic microalgae [14], parasites [15][16][17], and fungi [18], as well as various human tissues [19,20] and generic mammalians models with complex metabolism [21,22]. As such, the RAVEN toolbox has functioned as one of the two major MATLABbased packages for constraint-based metabolic modelling, together with the COBRA Toolbox [23][24][25].…”

Section: Introductionmentioning

confidence: 99%

RAVEN 2.0: a versatile toolbox for metabolic network reconstruction and a case study on Streptomyces coelicolor

Wang

Marcišauskas

Sánchez

et al. 2018

Preprint

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RAVEN is a commonly used MATLAB toolbox for genome-scale metabolic model (GEM) reconstruction, curation and constraint-based modelling and simulation. Here we present RAVEN Toolbox 2.0 with major enhancements, including: (i) de novo reconstruction of GEMs based on the MetaCyc pathway database; (ii) a redesigned KEGG-based reconstruction pipeline; (iii) convergence of reconstructions from various sources; (iv) improved performance, usability, and compatibility with the COBRA Toolbox. Capabilities of RAVEN 2.0 are here illustrated through de novo reconstruction of GEMs for the antibiotic-producing bacterium Streptomyces coelicolor. Comparison of the automated de novo reconstructions with the iMK1208 model, a previously published high-quality S.coelicolor GEM, exemplifies that RAVEN 2.0 can capture most of the manually curated model. The generated de novo reconstruction is subsequently used to curate iMK1208 resulting in Sco4, the most comprehensive GEM of S. coelicolor, with increased coverage of both primary and secondary metabolism. This increased coverage allows the use of Sco4 to predict novel genome editing targets for optimized secondary metabolites production. As such, we demonstrate that RAVEN 2.0 can be used not only for de novo GEM reconstruction, but also for curating existing models based on up-todate databases. Both RAVEN 2.0 and Sco4 are distributed through GitHub to facilitate usage and further development by the community. Author summaryCellular metabolism is a large and complex network. Hence, investigations of metabolic networks are aided by in silico modelling and simulations. Metabolic networks can be derived from whole-genome sequences, through identifying what enzymes are present and connecting these to formalized chemical reactions. To facilitate the reconstruction of genome-scale models of metabolism (GEMs), we have developed RAVEN 2.0. This versatile toolbox can reconstruct GEMs fast, through either metabolic pathway databases KEGG and MetaCyc, or from homology with an existing GEM. We demonstrate RAVEN's functionality through generation of a metabolic model of Streptomyces coelicolor, an antibiotic-producing bacterium. Comparison of this de novo generated GEM with a previously manually curated model demonstrates that RAVEN captures most of the previous model, and we subsequently reconstructed an updated model of S. coelicolor: Sco4. Following, we used Sco4 to predict promising targets for genetic engineering, which can be used to increase antibiotic production.

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Systems approach to characterize the metabolism of liver cancer stem cells expressing CD133

Cited by 37 publications

References 52 publications

B7-H4 overexpression is essential for early hepatocellular carcinoma progression and recurrence

B7-H4 overexpression is essential for early hepatocellular carcinoma progression and recurrence

Stemness features in liver cancer

RAVEN 2.0: a versatile toolbox for metabolic network reconstruction and a case study on Streptomyces coelicolor

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