Bioanalytical techniques for metabolite profiling in pancreatic cancer
Abstract
Metabolomics is increasingly recognized as an important tool for its ability to resolve complex biological problems while providing useful and complementary information to genomics and proteomics to present a more complete systems biology model. Metabolomics utilizes high-throughput analytical techniques such as nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) to directly analyze the metabolic profiles of small molecules (<1000 >Daltons) in biological samples such as blood, urine and tissue. Metabolite profiling has several applications including disease diagnosis, understanding metabolic pathways and toxicology studies. Although a combination of advanced NMR and MS techniques enable the comparison of metabolic features in complex biological samples, such methods are focused on relatively high concentrated metabolites which are often less specific to a particular pathological condition. A combination of these methods with multivariate statistical methods greatly reduces the complexity of metabolomics data to allow the full exploration of metabolite information. Considering the advantages of NMR and MS in the identification of new and low concentrated biomarkers in complex biological samples, there is a significant interest to enhance the metabolic pool, by increasing the resolution and sensitivity of these methods. This thesis focuses on the use of metabolite profiling in serum through advanced analytical and statistical methods. The first part focuses on the use of NMR and multivariate statistical methods to develop and validate biomarkers for distinguishing healthy volunteers from pancreatic cancer patients. The second part combines NMR, LC-MS and multivariate statistical analysis in the detection and characterization of a metabolic profile in at-risk patients and pancreatic cancer patients through biomarker identification and validation. The final section describes the use chromatogram resolved NMR spectroscopy is to separate and identify metabolites isotopically labeled with 15N ethanolamine in human serum. This technique can be applied in targeted metabolomics.
Degree
Ph.D.
Advisors
Raftery, Purdue University.
Subject Area
Molecular biology|Statistics|Biochemistry|Oncology
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