Abstract
Current available biomarkers lack sensitivity and/or specificity for early detection of cancer. To address this challenge, a robust and complete workflow for metabolic profiling and data mining is described in details. Three independent and complementary analytical techniques for metabolic profiling are applied: hydrophilic interaction liquid chromatography (HILIC–LC), reversed-phase liquid chromatography (RP–LC), and gas chromatography (GC). All three techniques are coupled to a mass spectrometer (MS) in the full scan acquisition mode, and both unsupervised and supervised methods are used for data mining. The univariate and multivariate feature selection are used to determine subsets of potentially discriminative predictors. These predictors are further identified by obtaining accurate masses and isotopic ratios using selected ion monitoring (SIM) and data-dependent MS/MS and/or accurate mass MSn ion tree scans utilizing high resolution MS. A list combining all of the identified potential biomarkers generated from different platforms and algorithms is used for pathway analysis. Such a workflow combining comprehensive metabolic profiling and advanced data mining techniques may provide a powerful approach for metabolic pathway analysis and biomarker discovery in cancer research. Two case studies with previous published data are adapted and included in the context to elucidate the application of the workflow.
Highlights
Pancreatic ductal adenocarcinoma (PDAC) ranks fourth as the cause of cancer-related mortality and second among the gastrointestinal cancers in the USA [74,75,76]
In the preliminary analysis of 20 cancer patients and 20 controls, we found that plasma sampling date was a significant confounding factor
Principal component analysis (PCA) was used for preliminary data mining on whole set of annotated peaks from gas chromatography (GC)-mass spectrometer (MS) and not annotated peaks from Hydrophilic interaction chromatography (HILIC) and Reverse-phase chromatography (RP) liquid chromatography-mass spectrometry (LC-MS) in an unsupervised fashion
Summary
Over the last several decades there has been significant progress in understanding cancer pathology, but cancer is still a devastating disease with high morbidity and mortality if diagnosed too late. Samples are highly complex, biologically variant, and with a large dynamic range of concentrations of active components. Such factors as well as high degree of structural diversity of metabolites present challenges to separation, detection, and data analysis. In order to identify various nontrivial metabolites with different polarity and molecular weight, all of the above-mentioned techniques are needed. Both LC-MS and GC-MS platforms are used in our untargeted metabolomics studies. The general steps include sample preparation, instrumental analysis, data mining, annotation and identification of feature components, secondary metabolite profiling using predictive multiple reaction monitoring (pMRM), and metabolic pathway analysis
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