Haematological malignancies are a frequently diagnosed group of neoplasms and a significant cause of cancer deaths. The successful treatment of these diseases relies on early and accurate detection. Specific small molecular compounds released by malignant cells and the simultaneous response by the organism towards the pathological state may serve as diagnostic/prognostic biomarkers or as a tool with relevance for cancer therapy management. To identify the most important metabolites required for differentiation, an H NMR metabolomics approach was applied to selected haematological malignancies. This study utilized 116 methanol serum extract samples from AML (n= 38), nHL (n= 26), CLL (n= 21) and HC (n= 31). Multivariate and univariate data analyses were performed to identify the most abundant changes among the studied groups. Complex and detailed VIP-PLS-DA models were calculated to highlight possible changes in terms of biochemical pathways and discrimination ability. Chemometric model prediction properties were validated by receiver operating characteristic (ROC) curves and statistical analysis. Two sets of eight important metabolites in HC/AML/CLL/nHL comparisons and five in AML/CLL/nHL comparisons were selected to form complex models to represent the most significant changes that occurred.
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http://dx.doi.org/10.18632/oncotarget.25311 | DOI Listing |
Methods Mol Biol
January 2025
Grupo Metabolômica, Universidade Estadual do Norte Fluminense, Campos dos Goytacazes, RJ, Brazil.
Metabolomics is the area of research, which strives to obtain complete metabolic fingerprints, to detect differences between them and to provide hypothesis to explain those differences (Schripsema J, Dagnino D, Handbook of chemical and biological plant analytical methods. Wiley, New York, 2015). However, obtaining complete metabolic fingerprints is not an easy task.
View Article and Find Full Text PDFMethods Mol Biol
January 2025
Biomic Auth, Bioanalysis and Omics Laboratory, Centre for Interdisciplinary Research of Aristotle, University of Thessaloniki, Innovation Area of Thessaloniki, Thermi, Greece.
The gut's symbiome, a hidden metabolic organ, has gained scientific interest for its crucial role in human health. Acting as a biochemical factory, the gut microbiome produces numerous small molecules that significantly impact host metabolism. Metabolic profiling facilitates the exploration of its influence on human health and disease through the symbiotic relationship.
View Article and Find Full Text PDFMethods Mol Biol
January 2025
Center for Environmental Measurement and Modeling, Environmental Protection Agency, Athens, GA, USA.
Metabolic profiling (untargeted metabolomics) aims for a global unbiased analysis of metabolites in a cell or biological system. It remains a highly useful research tool used across various analytical platforms. Incremental improvements across multiple steps in the analytical process may have large consequences for the end quality of the data.
View Article and Find Full Text PDFWorld Allergy Organ J
January 2025
Clinical Metabolomics Core Laboratory, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan.
Background: Childhood rhinitis and asthma are allergic respiratory diseases triggered by common allergens, but they affect different parts of the respiratory system, leading to distinct symptoms. However, a comprehensive multi-biofluid metabolomics-based approach to uncover valuable insights into childhood allergies and allergen sensitization remains unaddressed.
Methods: Seventy-six children, comprising 26 with rhinitis, 26 with asthma, and 24 healthy controls, were enrolled.
Cardiovasc Diabetol
January 2025
Division of Clinical Epidemiology and Aging Research, German Cancer Research Center, Im Neuenheimer Feld 581, 69120, Heidelberg, Germany.
Background: Existing cardiovascular risk prediction models still have room for improvement in patients with type 2 diabetes who represent a high-risk population. This study evaluated whether adding metabolomic biomarkers could enhance the 10-year prediction of major adverse cardiovascular events (MACE) in these patients.
Methods: Data from 10,257 to 1,039 patients with type 2 diabetes from the UK Biobank (UKB) and the German ESTHER cohort, respectively, were used for model derivation, internal and external validation.
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