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      ALS blood expression profiling identifies new biomarkers, patient subgroups, and evidence for neutrophilia and hypoxia

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          Abstract

          Background

          Amyotrophic lateral sclerosis (ALS) is a debilitating disease with few treatment options. Progress towards new therapies requires validated disease biomarkers, but there is no consensus on which fluid-based measures are most informative.

          Methods

          This study analyzed microarray data derived from blood samples of patients with ALS ( n = 396), ALS mimic diseases ( n = 75), and healthy controls ( n = 645). Goals were to provide in-depth analysis of differentially expressed genes (DEGs), characterize patient-to-patient heterogeneity, and identify candidate biomarkers.

          Results

          We identified 752 ALS-increased and 764 ALS-decreased DEGs (FDR < 0.10 with > 10% expression change). Gene expression shifts in ALS blood broadly resembled acute high altitude stress responses. ALS-increased DEGs had high exosome expression, were neutrophil-specific, associated with translation, and overlapped significantly with genes near ALS susceptibility loci (e.g., IFRD1, TBK1, CREB5). ALS-decreased DEGs, in contrast, had low exosome expression, were erythroid lineage-specific, and associated with anemia and blood disorders. Genes encoding neurofilament proteins ( NEFH, NEFL) had poor diagnostic accuracy (50–53%). However, support vector machines distinguished ALS patients from ALS mimics and controls with 87% accuracy (sensitivity: 86%, specificity: 87%). Expression profiles were heterogeneous among patients and we identified two subgroups: (i) patients with higher expression of IL6R and myeloid lineage-specific genes and (ii) patients with higher expression of IL23A and lymphoid-specific genes. The gene encoding copper chaperone for superoxide dismutase ( CCS) was most strongly associated with survival (HR = 0.77; P = 1.84e−05) and other survival-associated genes were linked to mitochondrial respiration. We identify a 61 gene signature that significantly improves survival prediction when added to Cox proportional hazard models with baseline clinical data (i.e., age at onset, site of onset and sex). Predicted median survival differed 2-fold between patients with favorable and risk-associated gene expression signatures.

          Conclusions

          Peripheral blood analysis informs our understanding of ALS disease mechanisms and genetic association signals. Our findings are consistent with low-grade neutrophilia and hypoxia as ALS phenotypes, with heterogeneity among patients partly driven by differences in myeloid and lymphoid cell abundance. Biomarkers identified in this study require further validation but may provide new tools for research and clinical practice.

          Electronic supplementary material

          The online version of this article (10.1186/s12967-019-1909-0) contains supplementary material, which is available to authorized users.

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          Most cited references98

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            ExoCarta: A Web-Based Compendium of Exosomal Cargo.

            Exosomes are membranous vesicles that are released by a variety of cells into the extracellular microenvironment and are implicated in intercellular communication. As exosomes contain RNA, proteins and lipids, there is a significant interest in characterizing the molecular cargo of exosomes. Here, we describe ExoCarta (http://www.exocarta.org), a manually curated Web-based compendium of exosomal proteins, RNAs and lipids. Since its inception, the database has been highly accessed (>54,000 visitors from 135 countries). The current version of ExoCarta hosts 41,860 proteins, >7540 RNA and 1116 lipid molecules from more than 286 exosomal studies annotated with International Society for Extracellular Vesicles minimal experimental requirements for definition of extracellular vesicles. Besides, ExoCarta features dynamic protein-protein interaction networks and biological pathways of exosomal proteins. Users can download most often identified exosomal proteins based on the number of studies. The downloaded files can further be imported directly into FunRich (http://www.funrich.org) tool for additional functional enrichment and interaction network analysis.
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              Survival model predictive accuracy and ROC curves.

              The predictive accuracy of a survival model can be summarized using extensions of the proportion of variation explained by the model, or R2, commonly used for continuous response models, or using extensions of sensitivity and specificity, which are commonly used for binary response models. In this article we propose new time-dependent accuracy summaries based on time-specific versions of sensitivity and specificity calculated over risk sets. We connect the accuracy summaries to a previously proposed global concordance measure, which is a variant of Kendall's tau. In addition, we show how standard Cox regression output can be used to obtain estimates of time-dependent sensitivity and specificity, and time-dependent receiver operating characteristic (ROC) curves. Semiparametric estimation methods appropriate for both proportional and nonproportional hazards data are introduced, evaluated in simulations, and illustrated using two familiar survival data sets.
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                Author and article information

                Contributors
                ws277814@ohio.edu
                ck178807@ohio.edu
                list@ohio.edu
                berrymad@ohio.edu
                kopchick@ohio.edu
                Journal
                J Transl Med
                J Transl Med
                Journal of Translational Medicine
                BioMed Central (London )
                1479-5876
                22 May 2019
                22 May 2019
                2019
                : 17
                : 170
                Affiliations
                [1 ]ISNI 0000 0001 0668 7841, GRID grid.20627.31, Heritage College of Osteopathic Medicine, , Ohio University, ; Athens, OH 45701 USA
                [2 ]ISNI 0000 0004 0447 0798, GRID grid.414987.7, Department of Internal Medicine, , The Jewish Hospital, ; Cincinnati, OH 45236 USA
                [3 ]ISNI 0000 0001 0668 7841, GRID grid.20627.31, Department of Environmental and Plant Biology, , Ohio University, ; Athens, OH 45701 USA
                [4 ]ISNI 0000 0001 0668 7841, GRID grid.20627.31, Edison Biotechnology Institute, , Ohio University, ; Athens, OH 45701 USA
                [5 ]ISNI 0000 0001 0668 7841, GRID grid.20627.31, The Diabetes Institute, , Ohio University, ; Athens, OH 45701 USA
                Author information
                http://orcid.org/0000-0001-8504-6363
                Article
                1909
                10.1186/s12967-019-1909-0
                6530130
                31118040
                85ecefce-e1cc-40f0-8e69-41e8c90cba7e
                © The Author(s) 2019

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 10 January 2019
                : 7 May 2019
                Categories
                Research
                Custom metadata
                © The Author(s) 2019

                Medicine
                amyotrophic lateral sclerosis,biomarker,gwas,hypoxia,machine learning,microarray,neutrophil,ribosome,translation

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