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International Journal of Molecular... Mar 2021Pancreatic ductal adenocarcinoma (PDAC) is known as a highly aggressive malignant disease. Prognosis for patients is notoriously poor, despite improvements in surgical...
Pancreatic ductal adenocarcinoma (PDAC) is known as a highly aggressive malignant disease. Prognosis for patients is notoriously poor, despite improvements in surgical techniques and new (neo)adjuvant chemotherapy regimens. Early detection of PDAC may increase the overall survival. It is furthermore foreseen that precision medicine will provide improved prognostic stratification and prediction of therapeutic response. In this review, omics-based discovery efforts are presented that aim for novel diagnostic and prognostic biomarkers of PDAC. For this purpose, we systematically evaluated the literature published between 1999 and 2020 with a focus on protein- and protein-glycosylation biomarkers in pancreatic cancer patients. Besides genomic and transcriptomic approaches, mass spectrometry (MS)-based proteomics and glycomics of blood- and tissue-derived samples from PDAC patients have yielded new candidates with biomarker potential. However, for reasons discussed in this review, the validation and clinical translation of these candidate markers has not been successful. Consequently, there has been a change of mindset from initial efforts to identify new unimarkers into the current hypothesis that a combination of biomarkers better suits a diagnostic or prognostic panel. With continuing development of current research methods and available techniques combined with careful study designs, new biomarkers could contribute to improved detection, prognosis, and prediction of pancreatic cancer.
Topics: Biomarkers, Tumor; Body Fluids; Carcinoma, Pancreatic Ductal; Clinical Decision-Making; Diagnosis, Differential; Early Detection of Cancer; Glycomics; Glycoproteins; Humans; Mass Spectrometry; Neoplasm Proteins; Neoplastic Syndromes, Hereditary; Pancreatic Neoplasms; Pancreatitis, Chronic; Precancerous Conditions; Precision Medicine; Predictive Value of Tests; Prognosis; Proteomics; Reproducibility of Results; Risk Factors
PubMed: 33800786
DOI: 10.3390/ijms22052655 -
Molecular & Cellular Proteomics : MCP 2021Intact glycopeptide identification has long been known as a key and challenging barrier to the comprehensive and accurate understanding the role of glycosylation in an...
Intact glycopeptide identification has long been known as a key and challenging barrier to the comprehensive and accurate understanding the role of glycosylation in an organism. Intact glycopeptide analysis is a blossoming field that has received increasing attention in recent years. MS-based strategies and relative software tools are major drivers that have greatly facilitated the analysis of intact glycopeptides, particularly intact N-glycopeptides. This article provides a systematic review of the intact glycopeptide-identification process using MS data generated in shotgun proteomic experiments, which typically focus on N-glycopeptide analysis. Particular attention is paid to the software tools that have been recently developed in the last decade for the interpretation and quality control of glycopeptide spectra acquired using different MS strategies. The review also provides information about the characteristics and applications of these software tools, discusses their advantages and disadvantages, and concludes with a discussion of outstanding tools.
Topics: Animals; Glycopeptides; Humans; Mass Spectrometry; Proteomics; Software
PubMed: 33556625
DOI: 10.1074/mcp.R120.002090 -
Biomedicine & Pharmacotherapy =... Nov 2020The glyoxalase system is a ubiquitous enzymatic network which plays important roles in biological life. It consists of glyoxalase 1 (GLO1), glyoxalase 2 (GLO2), and...
The glyoxalase system is a ubiquitous enzymatic network which plays important roles in biological life. It consists of glyoxalase 1 (GLO1), glyoxalase 2 (GLO2), and reduced glutathione (GSH), which perform an essential metabolic function in cells by detoxifying methylglyoxal (MG) and other endogenous harmful metabolites into non-toxic d-lactate. MG and MG-derived advanced glycation endproducts (AGEs) are associated with various diseases, such as diabetes, cardiovascular disease, neurodegenerative disorders and cancer, and GLO1 is a key rate-limiting enzyme in the anti-glycation defense. The abnormal activity and expression of GLO1 in various diseases make this enzyme a promising target for drug design and development. This review focuses on the regulatory mechanism of GLO1 in diverse pathogenic conditions with a thorough discussion of GLO1 regulators since their discovery, including GLO1 activators and inhibitors. The different classes, chemical structure and structure-activity relationship are embraced. Moreover, assays for the discovery of small molecule regulators of the glyoxalase system are also introduced in this article. Compared with spectrophotometer-based assay, microplate-based assay is a more simple, rapid and quantitative high-throughput method. This review will be useful to design novel and potent GLO1 regulators and hopefully provide a convenient reference for researchers.
Topics: Animals; Biological Products; Cardiovascular Diseases; Drug Evaluation, Preclinical; Enzyme Inhibitors; Glycosylation; Humans; Lactoylglutathione Lyase; Neoplasms; Pyruvaldehyde
PubMed: 32858501
DOI: 10.1016/j.biopha.2020.110663 -
Frontiers in Cardiovascular Medicine 2021The hemoglobin glycation index (HGI) has been proposed as a marker to quantify inter-individual variation in hemoglobin glycosylation. However, whether HGI is...
The hemoglobin glycation index (HGI) has been proposed as a marker to quantify inter-individual variation in hemoglobin glycosylation. However, whether HGI is associated with an increased risk of diabetic complications independent of glycated hemoglobin (HbA1c) remains unclear. This meta-analysis aimed to determine the association between HGI and the risk of all cause mortality and composite cardiovascular disease (CVD). PubMed, and EMBASE databases were searched for related studies up to March 31, 2021. Observational studies reported associations between HGI levels and composite CVD and all cause mortality were included for meta-analysis. A random effect model was used to calculate the hazard ratios (HRs) and 95% confidence intervals (CI) for higher HGI. A total of five studies, comprising 22,035 patients with type two diabetes mellitus were included for analysis. The median follow-up duration was 5.0 years. After adjusted for multiple conventional cardiovascular risk factors, an increased level of HGI was associated with a higher risk of composite CVD (per 1 SD increment: HR = 1.14, 95% CI = 1.04-1.26) and all cause mortality (per 1 SD increment: HR = 1.18, 95% CI = 1.05-1.32). However, when further adjusted for HbA1c, the association between HGI and risk of composite CVD (per 1 SD increment of HGI: HR = 1.01, 95% CI = 0.93-1.10) and all cause mortality (per 1 SD increment of HGI: HR = 1.03, 95% CI = 0.96-1.10) became insignificant. High HGI was associated with an increased risk of composite CVD and all cause mortality after adjustment for multiple conventional cardiovascular risk factors. However, the association was mainly mediating by the level of HbA1c.
PubMed: 34124211
DOI: 10.3389/fcvm.2021.690689