Serum NMR metabolomics to differentiate haematologic malignancies
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Wojciech Wojtowicz1, Angelika Chachaj2, Andrzej Olczak3, Adam Ząbek1, Elżbieta Piątkowska4, Justyna Rybka5, Aleksandra Butrym6,7, Monika Biedroń5, Grzegorz Mazur6, Tomasz Wróbel5, Andrzej Szuba2 and Piotr Młynarz1
1Wroclaw University of Technology, Department of Bioorganic Chemistry, Wroclaw, Poland
2Wroclaw Medical University, Department of Angiology, Wroclaw, Poland
3Opole University of Technology, Faculty of Electrical Engineering, Automatic Control and Informatics, Opole, Poland
4Wrocław Research Centre EIT+, Wroclaw, Poland
5Department of Haematology, Blood Neoplasms, and Bone Marrow Transplantation, Wroclaw Medical University, Wroclaw, Poland
6Department of Internal Medicine, Wroclaw Medical University, Wroclaw, Poland
7Department of Physiology, Wroclaw Medical University, Wroclaw, Poland
Piotr Młynarz, email: email@example.com
Keywords: metabolomics; haematological malignancies; nHL; AML; CLL
Received: January 24, 2018 Accepted: April 07, 2018 Published: May 11, 2018
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 1H 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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