Related Experiment Video
Updated: Nov 2, 2025
Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
Published on: October 3, 2025
A computational workflow for the detection of candidate diagnostic biomarkers of Kawasaki disease using time-series
Vasileios C Pezoulas1, Costas Papaloukas1,2, Maëva Veyssiere3
1Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, Ioannina GR45110, Greece.
Insights
Researchers identified five novel genes (HLA-DQB1, HLA-DRA, ZBTB48, TNFRSF13C, CASD1) as potential biomarkers for diagnosing Kawasaki disease (KD), a type of systemic autoinflammatory disease (SAID). This discovery improves diagnostic accuracy and aids in understanding KD's underlying mechanisms.
Area of Science:
- Immunology
- Genetics
- Computational Biology
Background:
- Systemic autoinflammatory diseases (SAIDs) lack definitive biomarkers, hindering diagnosis and treatment.
- Kawasaki disease (KD), a type of SAID, presents challenges due to unknown pathogenic mechanisms and genetic mutations.
Purpose of the Study:
- To develop a computational workflow for identifying novel diagnostic biomarkers for Kawasaki disease (KD).
- To discover genes that can differentiate KD patients across different disease phases.
Main Methods:
- Utilized Self-Organizing Maps (SOMs) for clustering gene expression profiles across acute, subacute, and convalescent KD phases.
- Employed false discovery rate (FDR)-based feature selection to identify significantly deviating genes.
- Trained boosting ensemble models (AdaBoost, XGBoost) to evaluate biomarker performance.
Main Results:
- Identified five candidate KD biomarkers: HLA-DQB1, HLA-DRA, ZBTB48, TNFRSF13C, and CASD1.
- These genes demonstrated improved classification accuracy, sensitivity, and specificity compared to known markers in both common and cross-platform datasets.
- Achieved an average increase of 4.40% in accuracy, 5.52% in sensitivity, and 3.57% in specificity in acute and subacute phases.
Conclusions:
- The identified genes represent novel, potential biomarkers for KD diagnosis.
- The computational workflow provides a robust method for biomarker discovery in complex diseases.
- These findings contribute to a better understanding of KD pathogenesis and offer improved diagnostic tools.
Abstract:
Unlike autoimmune diseases, there is no known constitutive and disease-defining biomarker for systemic autoinflammatory diseases (SAIDs). Kawasaki disease (KD) is one of the "undiagnosed" types of SAIDs whose pathogenic mechanism and gene mutation still remain unknown. To address this issue, we have developed a sequential computational workflow which clusters KD patients with similar gene expression profiles across the three different KD phases (Acute, Subacute and Convalescent) and utilizes the resulting clustermap to detect prominent genes that can be used as diagnostic biomarkers for KD. Self-Organizing Maps (SOMs) were employed to cluster patients with similar gene expressions across the three phases through inter-phase and intra-phase clustering. Then, false discovery rate (FDR)-based feature selection was applied to detect genes that significantly deviate across the per-phase clusters. Our results revealed five genes as candidate biomarkers for KD diagnosis, namely, the HLA-DQB1, HLA-DRA, ZBTB48, TNFRSF13C, and CASD1. To our knowledge, these five genes are reported for the first time in the literature. The impact of the discovered genes for KD diagnosis against the known ones was demonstrated by training boosting ensembles (AdaBoost and XGBoost) for KD classification on common platform and cross-platform datasets. The classifiers which were trained on the proposed genes from the common platform data yielded an average increase by 4.40% in accuracy, 5.52% in sensitivity, and 3.57% in specificity than the known genes in the Acute and Subacute phases, followed by a notable increase by 2.30% in accuracy, 2.20% in sensitivity, and 4.70% in specificity in the cross-platform analysis.
