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Title: | Blood transcriptomics to facilitate diagnosis and stratification in pediatric rheumatic diseases - a proof of concept study | Authors: | Ha, My Kieu Bartholomeus, Esther Van Os, Luc Dandelooy, Julie Leysen, Julie Aerts, Olivier Siozopoulou, Vasiliki De Smet, Eline Gielen , Jan Guerti, Khadija De Maeseneer, Michel Herregods, Nele Lechkar, Bouchra Wittoek, Ruth Geens, Elke Claes , Laura Zaqout, Mahmoud Dewals, Wendy Lemay, Annelies Tuerlinckx, David Weynants, David Vanlede, Koen van Berlaer, Gerlant Raes , Marc Verhelst , Helene Boiy, Tine Van Damme, Pierre Jansen, Anna C. Meuwissen , Marije Sabato, Vito Van Camp, Guy Suls, Arvid ten Bosch, Jutte Van der Werff Dehoorne, Joke Joos, Rik Laukens, Kris Meysman, Pieter OGUNJIMI, Benson |
Issue Date: | 2022 | Publisher: | BMC | Source: | Pediatric Rheumatology, 20 (1) (Art N° 91) | Abstract: | Background Transcriptome profiling of blood cells is an efficient tool to study the gene expression signatures of rheumatic diseases. This study aims to improve the early diagnosis of pediatric rheumatic diseases by investigating patients' blood gene expression and applying machine learning on the transcriptome data to develop predictive models. Methods RNA sequencing was performed on whole blood collected from children with rheumatic diseases. Random Forest classification models were developed based on the transcriptome data of 48 rheumatic patients, 46 children with viral infection, and 35 controls to classify different disease groups. The performance of these classifiers was evaluated by leave-one-out cross-validation. Analyses of differentially expressed genes (DEG), gene ontology (GO), and interferon-stimulated gene (ISG) score were also conducted. Results Our first classifier could differentiate pediatric rheumatic patients from controls and infection cases with high area-under-the-curve (AUC) values (AUC = 0.8 +/- 0.1 and 0.7 +/- 0.1, respectively). Three other classifiers could distinguish chronic recurrent multifocal osteomyelitis (CRMO), juvenile idiopathic arthritis (JIA), and interferonopathies (IFN) from control and infection cases with AUC >= 0.8. DEG and GO analyses reveal that the pathophysiology of CRMO, IFN, and JIA involves innate immune responses including myeloid leukocyte and granulocyte activation, neutrophil activation and degranulation. IFN is specifically mediated by antibacterial and antifungal defense responses, CRMO by cellular response to cytokine, and JIA by cellular response to chemical stimulus. IFN patients particularly had the highest mean ISG score among all disease groups. Conclusion Our data show that blood transcriptomics combined with machine learning is a promising diagnostic tool for pediatric rheumatic diseases and may assist physicians in making data-driven and patient-specific decisions in clinical practice. | Notes: | Ha, MK; Ogunjimi, B (corresponding author), Univ Antwerp, Ctr Hlth Econ Res & Modelling Infect Dis CHERMID, Vaccine & Infect Dis Inst, Antwerp, Belgium.; Ha, MK; Ogunjimi, B (corresponding author), Univ Antwerp, Antwerp Unit Data Anal & Computat Immunol & Seque, Antwerp, Belgium.; Ha, MK (corresponding author), Univ Antwerp, Antwerp Ctr Translat Immunol & Virol ACTIV, Vaccine & Infect Dis Inst, Antwerp, Belgium. my.ha@uantwerpen.be; benson.ogunjimi@uantwerpen.be |
Keywords: | Pediatric rheumatic diseases;RNA sequencing;Blood transcriptomics;Classification model | Document URI: | http://hdl.handle.net/1942/38845 | ISSN: | 1546-0096 | e-ISSN: | 1546-0096 | DOI: | 10.1186/s12969-022-00747-x | ISI #: | 000869248400002 | Rights: | The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. 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 in a credit line to the data. | Category: | A1 | Type: | Journal Contribution |
Appears in Collections: | Research publications |
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