The metabolic and biochemical impact of glucose 6-sulfonate (sulfoquinovose), a dietary sugar, on carbohydrate metabolism

Sacoman, Juliana L.; Badish, Lauren; Sharkey, Thomas D.; Hollingsworth, Rawle I.

doi:10.1016/j.carres.2012.09.014

Cited by 13 publications

(13 citation statements)

References 37 publications

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“…ENTPD2 belongs to ENTPD family (Chiu et al, 2017), have reported only ENTPD1 plays an important role in cancer, however, discloses that ENTPD2 is harnessed by cancer cells to escape immune-mediated destruction and the expression level of ENTPD2 was also high in HCC patients, and the high expression of ENTPD2 was associated with direct liver invasion, tumor microsatellite formation and venous invasion, as well as the absence of tumor encapsulation. The pentose phosphate pathway belongs to major carbohydrate pathways, it could produce ribose and NADPH to protect and promote cells proliferation in hypoxic conditions, such characteristic meets the demand of malignant proliferating cells, therefor, the change of the pentose phosphate pathway may be the landmark of cancer (Sacoman et al, 2012). G6PD is the rate-controlling enzyme of pentose phosphate pathway, previous researches have reported G6PD gene is an oncogene and the expression level upregulates in bladder cancer (Ohl et al, 2006), ESCC (Wang et al, 2016), breast cancer (Pu et al, 2015), Sun et al (2014) indicated G6PD-deficient women have reduced breast cancer risk, Zhang et al (2017) indicated overexpression of G6PD increases the risk of colon cancer, Wang et al (2012a) reported G6PD could promote the progression of gastric cancer cells and is associated with poor clinical outcome for patients with gastric cancer.…”

Section: Discussionmentioning

confidence: 99%

Comprehensive analysis reveals a metabolic ten-gene signature in hepatocellular carcinoma

Zhu

et al. 2020

PeerJ

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Background Due to the complicated molecular and cellular heterogeneity in hepatocellular carcinoma (HCC), the morbidity and mortality still remains high level in the world. However, the number of novel metabolic biomarkers and prognostic models could be applied to predict the survival of HCC patients is still small. In this study, we constructed a metabolic gene signature by systematically analyzing the data from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) and International Cancer Genome Consortium (ICGC). Methods Differentially expressed genes (DEGs) between tumors and paired non-tumor samples of 50 patients from TCGA dataset were calculated for subsequent analysis. Univariate cox proportional hazard regression and LASSO analysis were performed to construct a gene signature. The Kaplan–Meier analysis, time-dependent receiver operating characteristic (ROC), Univariate and Multivariate Cox regression analysis, stratification analysis were used to assess the prognostic value of the gene signature. Furthermore, the reliability and validity were validated in four types of testing cohorts. Moreover, the diagnostic capability of the gene signature was investigated to further explore the clinical significance. Finally, Go enrichment analysis and Gene Set Enrichment Analysis (GSEA) have been performed to reveal the different biological processes and signaling pathways which were active in high risk or low risk group. Results Ten prognostic genes were identified and a gene signature were constructed to predict overall survival (OS). The gene signature has demonstrated an excellent ability for predicting survival prognosis. Univariate and Multivariate analysis revealed the gene signature was an independent prognostic factor. Furthermore, stratification analysis indicated the model was a clinically and statistically significant for all subgroups. Moreover, the gene signature demonstrated a high diagnostic capability in differentiating normal tissue and HCC. Finally, several significant biological processes and pathways have been identified to provide new insights into the development of HCC. Conclusion The study have identified ten metabolic prognostic genes and developed a prognostic gene signature to provide more powerful prognostic information and improve the survival prediction for HCC.

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Section: Discussionmentioning

confidence: 99%

Comprehensive analysis reveals a metabolic ten-gene signature in hepatocellular carcinoma

Zhu

et al. 2020

PeerJ

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“…The pentose phosphate pathway belongs to major carbohydrate pathways, it could produce ribose and NADPH to protect and promote cells proliferation in hypoxic conditions, such characteristic meets the demand of malignant proliferating cells, therefor, the change of the pentose phosphate pathway may be the landmark of cancer. (Sacoman et al 2012) G6PD is the rate-controlling enzyme of pentose phosphate pathway, previous researches have reported G6PD gene is an oncogene and the expression level upregulates in bladder cancer, (Ohl et al 2006) ESCC, (Wang et al 2016)breast cancer, (Pu et al 2015 indicated G6PD-deficient women have reduced breast cancer risk, Zhang X et al indicated overexpression of G6PD increases the risk of colon cancer, Wang J et al (Wang et al 2012a) reported G6PD could promote the progression of gastric cancer cells and is associated with poor clinical outcome for patients with gastric cancer. (Zhang et al 2017) Masayoshi Munemoto et al (Munemoto et al 2019) activated G6PD gene to accelerates carcinogenesis and cancer progression.…”

Section: Go Enrichment Analysis and Gene Set Enrichment Analysis (Gsea)mentioning

confidence: 99%

Peer Review #1 of "Comprehensive analysis reveals a metabolic ten-gene signature in hepatocellular carcinoma (v0.1)"

2020

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Background: Due to the complicated molecular and cellular heterogeneity in hepatocellular carcinoma (HCC), the morbidity and mortality still remains high level in the world. However, the number of novel metabolic biomarkers and prognostic models could be applied to predict the survival of HCC patients is still small. In this study, we constructed a metabolic gene signature by systematically analyzing the data from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus(GEO) and International Cancer Genome Consortium (ICGC). Methods: Differentially expressed genes (DEGs) between tumors and paired non-tumor samples of 50 patients from TCGA dataset were calculated for subsequent analysis. Univariate cox proportional hazard regression and LASSO analysis were performed to construct a gene signature. The Kaplan-Meier analysis, Time-dependent receiver operating characteristic (ROC), Univariate and Multivariate Cox regression analysis, stratification analysis were used to assess the prognostic value of the gene signature. Furthermore, The reliability and validity were validated in four types of testing cohort. Moreover, the diagnostic capability of the gene signature was investigated to further explore the clinical significance, Finally, Go enrichment analysis and Gene Set Enrichment Analysis (GSEA) enrichment analysis have been performed to reveal the different biological processes and signaling pathways which were active in high risk or low risk group. Results: Ten prognostic genes were identified and a gene signature were constructed to predict overall survival (OS). The gene signature has demonstrated an excellent ability for predicting survival prognosis , Univariate and Multivariate analysis revealed the gene signature was an independent prognostic factor. Furthermore, stratification analysis indicated the model was a clinically and statistically significant for all subgroups. Moreover, the gene signature demonstrated a high diagnostic capability in differentiating normal tissue and HCC. Finally, several significant biological processes and pathways have been identified to provide a new insights of the development of HCC. Conclusion: The study have identified ten metabolic prognostic genes and developed a

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“…17,19,20 Helferich and Ost used methyl α-D-glucopyranoside as a substrate and introduced a sulfonate directly by substitution with sulfite. 18,21 In early work crystalline potassium 17,22 and sodium salts, 18 were identified. However, subsequent studies did not take advantage of these isolation methods; in some cases there was either no purification of the final SQ isolated, [19][20][21] or it was purified through tedious methods involving interconversion of toxic salts.…”

Section: Synthesis Of Sq (1)mentioning

confidence: 99%

Synthesis of Sulfoquinovose and Sulfoquinovosyl Diacylglycerides, and a Fluorogenic Substrate for Sulfoquinovosidases

Zhang¹,

Mui²,

Arumaperuma³

et al. 2019

Preprint

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The sulfolipid sulfoquinovosyl diacylglycerol (SQDG) and its headgroup, the sulfosugar sulfoquinovose (SQ), are estimated to harbour up to half of all organosulfur in the biosphere. SQ is liberated from SQDG and related glycosides by the action of sulfoquinovosidases (SQases). We report a 10-step synthesis of SQDG that we apply to the preparation of saturated and unsaturated lipoforms. We also report an expeditious synthesis of SQ and (13C6)SQ, and X-ray crystal structures of sodium and potassium salts of SQ. Finally, we report the synthesis of a fluorogenic SQase substrate, methylumbelliferyl a-D-sulfoquinovoside, and examination of its cleavage kinetics by two recombinant SQases.

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The metabolic and biochemical impact of glucose 6-sulfonate (sulfoquinovose), a dietary sugar, on carbohydrate metabolism

Cited by 13 publications

References 37 publications

Comprehensive analysis reveals a metabolic ten-gene signature in hepatocellular carcinoma

Comprehensive analysis reveals a metabolic ten-gene signature in hepatocellular carcinoma

Peer Review #1 of "Comprehensive analysis reveals a metabolic ten-gene signature in hepatocellular carcinoma (v0.1)"

Synthesis of Sulfoquinovose and Sulfoquinovosyl Diacylglycerides, and a Fluorogenic Substrate for Sulfoquinovosidases

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