Blood transcriptomics to facilitate diagnosis and stratification in pediatric rheumatic diseases - a proof of concept study

My Kieu Ha, Esther Bartholomeus, Luc Van Os, Julie Dandelooy, Julie Leysen, Olivier Aerts, Vasiliki Siozopoulou, Eline De Smet, Jan Gielen, Khadija Guerti, Michel De Maeseneer, Nele Herregods, Bouchra Lechkar, Ruth Wittoek, Elke Geens, Laura Claes, Mahmoud Zaqout, Wendy Dewals, Annelies Lemay, David TuerlinckxDavid Weynants, Koen Vanlede, Gerlant van Berlaer, Marc Raes, Helene Verhelst, Tine Boiy, Pierre Van Damme, Anna C Jansen, Marije Meuwissen, Vito Sabato, Guy Van Camp, Arvid Suls, Jutte Van der Werff Ten Bosch, Joke Dehoorne, Rik Joos, Kris Laukens, Pieter Meysman, Benson Ogunjimi

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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.

Original languageEnglish
Article number91
Number of pages10
JournalPediatric rheumatology online journal
Volume20
Issue number1
DOIs
Publication statusPublished - 17 Oct 2022

Bibliographical note

Funding Information:
We received financial support from the Research Foundation Flanders (1861219N to BO), Al Thrasher Early Career Grant, academic investigator-initiated grants from AbbVie and charity support from “Cycling for the smile of a child”. The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Publisher Copyright:
© 2022, The Author(s).

Copyright:
Copyright 2023 Elsevier B.V., All rights reserved.

Keywords

  • Child
  • Humans
  • Arthritis, Juvenile/diagnosis
  • Cytokines
  • Interferons
  • Osteomyelitis
  • Proof of Concept Study
  • Rheumatic Diseases/diagnosis
  • Transcriptome

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