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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
Published on: October 11, 2018
Identification of potential peripheral blood diagnostic biomarkers for patients with juvenile idiopathic arthritis by
Zhi-Qiang Tu1, Hai-Yan Xue1, Wei Chen1
1Department of Pediatrics, Renji Hospital, School of Medicine, Shanghai Jiaotong University, 145 Shandongzhong Road, Shanghai, 200001, China.
Insights
Juvenile idiopathic arthritis (JIA) diagnosis lacks effective biomarkers. This study developed a predictive model using gene expression and signaling pathways, offering potential diagnostic and therapeutic targets for JIA.
Area of Science:
- Rheumatology
- Pediatric Medicine
- Bioinformatics
Background:
- Juvenile idiopathic arthritis (JIA) is a prevalent childhood rheumatic disease with significant health impacts.
- Current diagnostic methods for JIA often lack effective early-onset biomarkers.
- Identifying reliable biomarkers is crucial for timely diagnosis and management of JIA.
Purpose of the Study:
- To construct a robust classification model for predicting JIA.
- To identify potential gene expression biomarkers and signaling pathways associated with JIA.
- To explore the pathogenic mechanisms underlying JIA through bioinformatics analysis.
Main Methods:
- Downloaded peripheral blood gene expression profile data from the GEO database.
- Analyzed differentially expressed genes (DEGs) using Pearson's correlation and unsupervised clustering.
- Constructed co-expression networks and identified enriched signaling pathways (e.g., B/T cell receptor, ErbB, MAPK).
Main Results:
- Identified eighteen specific correlated DEGs with altered correlations across disease states.
- Developed a classification model utilizing eight significantly enriched signaling pathways.
- Achieved high accuracy in predicting JIA using the developed classification model.
Conclusions:
- The developed classification model demonstrates high accuracy in predicting JIA.
- Specific gene sets and enriched signaling pathways show potential as biomarkers for JIA diagnosis.
- Findings offer insights into JIA pathogenesis and potential therapeutic targets.
Abstract:
Juvenile idiopathic arthritis (JIA) is common childhood rheumatic disease harming children health. However, there is still lack of effective biomarkers for diagnosis JIA at early onset. We aim to construct a classification model to predict JIA disease. The peripheral blood gene expression profile data of JIA were downloaded from GEO database. We compared and analyzed differentially expressed genes (DEGs) between different JIA samples through Pearson's correlation coefficient method and unsupervised clustering analysis. Diagnostic model were constructed based on the deviation pathway through bioinformatics method. Eighteen specific correlated DEGs were obtained, but the correlations altered in different disease states. Although most JIA and control samples were clustered by unsupervised clustering analysis, respectively, a few JIA samples could not be clustered well. Four co-expression networks were next constructed with gene connections dynamically altered under variable conditions. Eight signaling pathways were significantly enriched including B/T cell receptor, ErbB and MAPK signaling pathways. The deviation scores of pathways were calculated. Applying these eight signaling pathways as feature to construct a classification model could predict JIA disease with high accuracies. Our data provide some light into pathogenic mechanism of JIA, the specific gene sets and the related signaling pathways may be potential biomarkers for diagnosis or therapeutic targets of JIA.
