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      Metabolomic biomarkers of pancreatic cancer: a meta-analysis study

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          Abstract

          Pancreatic cancer (PC) is an aggressive disease with high mortality rates, however, there is no blood test for early detection and diagnosis of this disease. Several research groups have reported on metabolomics based clinical investigations to identify biomarkers of PC, however there is a lack of a centralized metabolite biomarker repository that can be used for meta-analysis and biomarker validation. Furthermore, since the incidence of PC is associated with metabolic syndrome and Type 2 diabetes mellitus (T2DM), there is a need to uncouple these common metabolic dysregulations that may otherwise diminish the clinical utility of metabolomic biosignatures. Here, we attempted to externally replicate proposed metabolite biomarkers of PC reported by several other groups in an independent group of PC subjects. Our study design included a T2DM cohort that was used as a non-cancer control and a separate cohort diagnosed with colorectal cancer (CRC), as a cancer disease control to eliminate possible generic biomarkers of cancer. We used targeted mass spectrometry for quantitation of literature-curated metabolite markers and identified a biomarker panel that discriminates between normal controls (NC) and PC patients with high accuracy. Further evaluation of our model with CRC, however, showed a drop in specificity for the PC biomarker panel. Taken together, our study underscores the need for a more robust study design for cancer biomarker studies so as to maximize the translational value and clinical implementation.

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          Most cited references71

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          MetaboAnalyst: a web server for metabolomic data analysis and interpretation

          Metabolomics is a newly emerging field of ‘omics’ research that is concerned with characterizing large numbers of metabolites using NMR, chromatography and mass spectrometry. It is frequently used in biomarker identification and the metabolic profiling of cells, tissues or organisms. The data processing challenges in metabolomics are quite unique and often require specialized (or expensive) data analysis software and a detailed knowledge of cheminformatics, bioinformatics and statistics. In an effort to simplify metabolomic data analysis while at the same time improving user accessibility, we have developed a freely accessible, easy-to-use web server for metabolomic data analysis called MetaboAnalyst. Fundamentally, MetaboAnalyst is a web-based metabolomic data processing tool not unlike many of today's web-based microarray analysis packages. It accepts a variety of input data (NMR peak lists, binned spectra, MS peak lists, compound/concentration data) in a wide variety of formats. It also offers a number of options for metabolomic data processing, data normalization, multivariate statistical analysis, graphing, metabolite identification and pathway mapping. In particular, MetaboAnalyst supports such techniques as: fold change analysis, t-tests, PCA, PLS-DA, hierarchical clustering and a number of more sophisticated statistical or machine learning methods. It also employs a large library of reference spectra to facilitate compound identification from most kinds of input spectra. MetaboAnalyst guides users through a step-by-step analysis pipeline using a variety of menus, information hyperlinks and check boxes. Upon completion, the server generates a detailed report describing each method used, embedded with graphical and tabular outputs. MetaboAnalyst is capable of handling most kinds of metabolomic data and was designed to perform most of the common kinds of metabolomic data analyses. MetaboAnalyst is accessible at http://www.metaboanalyst.ca
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            Testing significance relative to a fold-change threshold is a TREAT

            Motivation: Statistical methods are used to test for the differential expression of genes in microarray experiments. The most widely used methods successfully test whether the true differential expression is different from zero, but give no assurance that the differences found are large enough to be biologically meaningful. Results: We present a method, t-tests relative to a threshold (TREAT), that allows researchers to test formally the hypothesis (with associated p-values) that the differential expression in a microarray experiment is greater than a given (biologically meaningful) threshold. We have evaluated the method using simulated data, a dataset from a quality control experiment for microarrays and data from a biological experiment investigating histone deacetylase inhibitors. When the magnitude of differential expression is taken into account, TREAT improves upon the false discovery rate of existing methods and identifies more biologically relevant genes. Availability: R code implementing our methods is contributed to the software package limma available at http://www.bioconductor.org. Contact: smyth@wehi.edu.au
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              Pivotal Evaluation of the Accuracy of a Biomarker Used for Classification or Prediction: Standards for Study Design

              Research methods for biomarker evaluation lag behind those for evaluating therapeutic treatments. Although a phased approach to development of biomarkers exists and guidelines are available for reporting study results, a coherent and comprehensive set of guidelines for study design has not been delineated. We describe a nested case–control study design that involves prospective collection of specimens before outcome ascertainment from a study cohort that is relevant to the clinical application. The biomarker is assayed in a blinded fashion on specimens from randomly selected case patients and control subjects in the study cohort. We separately describe aspects of the design that relate to the clinical context, biomarker performance criteria, the biomarker test, and study size. The design can be applied to studies of biomarkers intended for use in disease diagnosis, screening, or prognosis. Common biases that pervade the biomarker research literature would be eliminated if these rigorous standards were followed.
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                Author and article information

                Journal
                Oncotarget
                Oncotarget
                Oncotarget
                ImpactJ
                Oncotarget
                Impact Journals LLC
                1949-2553
                15 September 2017
                18 August 2017
                : 8
                : 40
                : 68899-68915
                Affiliations
                1 Department of Oncology, Georgetown University Medical Center, Washington, DC, United States of America
                2 Department of Biostatistics Bioinformatics and Biomathematics, Georgetown University, Washington, DC, United States of America
                3 Department of Health Sciences, Qatar University, Doha, Qatar
                4 Lombardi Comprehensive Cancer Center, Med-Star Georgetown University Hospital, Washington, DC, United States of America
                5 Department of Neurology, University of California, Irvine, CA, United States of America
                6 Department of Neurological Surgery, University of California, Irvine, CA, United States of America
                7 Department of Biochemistry and Molecular and Cellular Biology, Georgetown University Medical Center, Washington, DC, United States of America
                Author notes
                Correspondence to: Amrita K. Cheema, akc27@ 123456georgetown.edu
                Article
                20324
                10.18632/oncotarget.20324
                5620306
                28978166
                11e8f649-7a67-4b69-88da-d628c2c1153f
                Copyright: © 2017 Mehta et al.

                This article is distributed under the terms of the Creative Commons Attribution License (CC-BY), which permits unrestricted use and redistribution provided that the original author and source are credited.

                History
                : 19 April 2017
                : 4 August 2017
                Categories
                Meta-Analysis

                Oncology & Radiotherapy
                pancreatic cancer,biomarkers,metabolomics
                Oncology & Radiotherapy
                pancreatic cancer, biomarkers, metabolomics

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