Defining Putative Glycan Cancer Biomarkers By Ms

Mechref, Yehia; Hu, Yunli; Garcia, Aldo; Zhou, Shiyue; Desantos-Garcia, Janie L.; Hussein, Ahmed

doi:10.4155/bio.12.246

Cited by 56 publications

(54 citation statements)

References 59 publications

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“…Moreover, the association between aberrant glycosylation and disease development has promoted a number of research activities directed to provide reliable diagnostic or prognostic information [31]. MS glycomics methods have been successfully employed to compare the glycomic profiles of different samples collected from healthy individuals and ovarian cancer patients [32]. Particularly, Abbott et al studied the differences in the transcripts of a restricted set of glycosyltransferases involved in N-linked glycosylation in ovarian cancer tissues and normal ovarian tissues, identifying candidate biomarkers with cancer-specific glycosylation, such as periostin and thrombospondin [33].…”

Section: Proteomics and Post-translational Modificationsmentioning

confidence: 99%

Ovarian Cancer: Can Proteomics Give New Insights for Therapy and Diagnosis?

Toss

Matteis

Rossi

et al. 2013

IJMS

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The study of the ovarian proteomic profile represents a new frontier in ovarian cancer research, since this approach is able to enlighten the wide variety of post-translational events (such as glycosylation and phosphorylation). Due to the possibility of analyzing thousands of proteins, which could be simultaneously altered, comparative proteomics represent a promising model of possible biomarker discovery for ovarian cancer detection and monitoring. Moreover, defining signaling pathways in ovarian cancer cells through proteomic analysis offers the opportunity to design novel drugs and to optimize the use of molecularly targeted agents against crucial and biologically active pathways. Proteomic techniques provide more information about different histological types of ovarian cancer, cell growth and progression, genes related to tumor microenvironment and specific molecular targets predictive of response to chemotherapy than sequencing or microarrays. Estimates of specificity with proteomics are less consistent, but suggest a new role for combinations of biomarkers in early ovarian cancer diagnosis, such as the OVA1 test. Finally, the definition of the proteomic profiles in ovarian cancer would be accurate and effective in identifying which pathways are differentially altered, defining the most effective therapeutic regimen and eventually improving health outcomes.

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Section: Proteomics and Post-translational Modificationsmentioning

confidence: 99%

Ovarian Cancer: Can Proteomics Give New Insights for Therapy and Diagnosis?

Toss

Matteis

Rossi

et al. 2013

IJMS

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“…More and more studies show that the alterations of glycosylation and the levels of glycosyltransferases activities derived from the malignant transformation are relevant to human malignancies [9]. Glycomic tools have been extensively used in the last decade for discovering biomarkers [10][11][12]. This research therefore aims to develop improved N-glycan biomarkers that would be useful in the diagnosis of patients at an increased risk of PC.…”

Section: Introductionmentioning

confidence: 98%

Validation of N-glycan markers that improve the performance of CA19-9 in pancreatic cancer

Zhao

Zhou

et al. 2015

Clin Exp Med

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Pancreatic cancer (PC) has a high mortality rate because it is usually diagnosed late. Glycosylation of proteins is known to change in tumor cells during the development of PC. The objectives of this study were to identify and validate the diagnostic value of novel biomarkers based on N-glycomic profiling for PC. In total, 217 individuals including subjects with PC, pancreatitis, and healthy controls were divided randomly into a training group (n = 164) and validation groups (n = 53). Serum N-glycomic profiling was analyzed by DSA-FACE. The diagnostic model was constructed based on N-glycan markers with logistic stepwise regression. The diagnostic performance of the model was assessed further in validation cohort. The level of total core fucose residues was increased significantly in PC. Two diagnostic models designated GlycoPCtest and PCmodel (combining GlycoPCtest and CA19-9) were constructed to differentiate PC from normal. The area under the receiver operating characteristic curve (AUC) of PCmodel was higher than that of CA19-9 (0.925 vs. 0.878). The diagnostic models based on N-glycans are new, valuable, noninvasive alternatives for identifying PC. The diagnostic efficacy is improved by combined GlycoPCtest and CA19-9 for the discrimination of patients with PC from healthy controls.

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“…Additionally, glycomic changes have been associated with transition from non-lethal non-cancerous diseases to oncogenic equivalents 11–12 . High-throughput and highly-sensitive liquid-chromatography mass-spectrometry (LC-MS) platforms are routinely used to detect and monitor glycomic differences between healthy and diseased individuals 13–14 . Within neuroblastoma, monoclonal antibodies targeting GD2 digangliosides are routinely used as treatment in high-risk disease 15 .…”

Section: Introductionmentioning

confidence: 99%

N-Linked Glycan Profiling in Neuroblastoma Cell Lines

Mayampurath²,

Khan³

et al. 2015

J. Proteome Res.

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Although MYCN amplification has been associated with aggressive neuroblastoma, the molecular mechanisms that differentiate low-risk, MYCN-non amplified neuroblastoma from high-risk, MYCN-amplified disease are largely unknown. Genomic and proteomic studies have been limited in discerning differences in signaling pathways that account for this heterogeneity. N-linked glycosylation is a common protein modification resulting from the attachment of sugars to protein residues and important in cell signaling and immune response. Aberrant N-linked glycosylation has been routinely linked to various cancers. In particular, glycomic markers have often proved useful in distinguishing cancers from precancerous conditions. Here, we perform a systematic comparison of N-linked glycomic variation between MYCN-non-amplified SY5Y and MYCN-amplified NLF cell lines with the aim of identifying changes in sugar abundance linked to high-risk neuroblastoma. Through a combination of liquid chromatography-mass spectrometry and bioinformatics analysis, we identified 16 glycans that show a statistically significant change in abundance between NLF and SY5Y samples. Closer examination revealed the preference for larger (in terms of total monosaccharide count) and more sialylated glycan structures in the MYCN-amplified samples in comparison to smaller, non-sialylated glycans that are more dominant in the MYCN-non-amplified samples. These results offer clues for deriving marker candidates for accurate neuroblastoma risk diagnosis.

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Defining Putative Glycan Cancer Biomarkers By Ms

Cited by 56 publications

References 59 publications

Ovarian Cancer: Can Proteomics Give New Insights for Therapy and Diagnosis?

Ovarian Cancer: Can Proteomics Give New Insights for Therapy and Diagnosis?

Validation of N-glycan markers that improve the performance of CA19-9 in pancreatic cancer

N-Linked Glycan Profiling in Neuroblastoma Cell Lines

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