Related Experiment Video
Updated: May 8, 2026
Use of Capillary Electrophoresis Immunoassay to Search for Potential Biomarkers of Amyotrophic Lateral Sclerosis in Human Platelets
Published on: February 10, 2020
Comprehensive machine learning for identifying platelet-associated diagnostic biomarkers and immune landscape in
1Department of Pediatrics, Shangyu People's Hospital of Shaoxing, Shaoxing University, Shaoxing, China.
Insights
This study identified four platelet biomarkers to create a machine learning model for diagnosing Kawasaki disease (KD). The findings reveal their role in KD pathogenesis, aiding future diagnosis and treatment.
Area of Science:
- Immunology
- Genomics
- Computational Biology
Background:
- Kawasaki disease (KD) is an acute febrile illness in young children.
- Platelets are key in hemostasis and inflammation, contributing to vascular damage in KD.
- Understanding platelet roles is vital for KD diagnosis and treatment.
Purpose of the Study:
- Identify platelet-related diagnostic biomarkers for KD.
- Develop a machine learning-based diagnostic model for KD.
- Characterize the immune features, pathway activities, and regulatory networks of these biomarkers.
Main Methods:
- Integrated transcriptomic data from four KD cohorts.
- Screened platelet-associated differentially expressed genes using differential analysis and WGCNA.
- Utilized machine learning algorithms (LASSO, RF, XGBoost) for biomarker identification and model validation.
- Analyzed immune infiltration, pathway enrichment, molecular subtypes, and regulatory networks.
Main Results:
- Identified four diagnostic biomarkers: CD63, F5, STXBP2, and SERPINA1.
- These biomarkers are involved in KD pathogenesis via dysregulated coagulation, immune hyperactivation, and vascular injury.
- Correlations found with neutrophil infiltration and ribosome pathway suppression.
- Discovered distinct immune-endotypes and potential master regulators (has-miR-155-5p, SMAD5, SAP30, PHF8).
Conclusions:
- A machine learning model using platelet biomarkers can aid KD diagnosis.
- Elucidated the pathogenic roles of these biomarkers in immune dysregulation and vascular injury.
- Provides a foundation for improved KD diagnosis and targeted therapies.
Background:
Kawasaki disease (KD) is an acute, self-limited febrile illness primarily affecting children under 5 years of age. Platelets play a crucial dual role in both hemostasis and inflammatory/immune responses, contributing to vascular damage in diseases such as KD, making their study essential for the diagnosis and treatment of KD. Therefore, this study aimed to identify platelet-related diagnostic biomarkers, construct a machine learning-based diagnostic model, and computationally characterize their associated immune features, pathway activities, and regulatory networks in KD.
Methods:
This study integrated transcriptomic datasets from 4 KD cohorts in the Gene Expression Omnibus database (training set: GSE68004/GSE73461; validation set: GSE100154/GSE63881). Platelet-associated differentially expressed genes were screened through differential analysis and Weighted Gene Co-expression Network Analysis. Four diagnostic biomarkers were identified via cross-validation using least absolute shrinkage and selection operator, random forest, and eXtreme Gradient Boosting algorithms, with model efficacy assessed through Receiver operating characteristic curve analysis. Immune infiltration characteristics were analyzed using single sample Gene Set Enrichment Analysis (GSEA) and CIBERSORT, pathway enrichment was performed via GSEA, molecular subtypes were classified using the non-negative Matrix Factorization algorithm, and miRNA and transcription factor (TF) regulatory networks were predicted through miRNet/NetworkAnalyst.
Results:
We identified 4 platelet-associated biomarkers (CD63, F5, STXBP2, and SERPINA1) with exceptional diagnostic power. These genes orchestrate KD pathogenesis through dysregulated coagulation (F5), immune hyperactivation (CD63/STXBP2), and vascular injury (SERPINA1), further validated by their strong correlations with neutrophil infiltration and ribosome pathway suppression. Molecular subtyping revealed distinct immune-endotypes (e.g., neutrophil-dominant vs. natural killer-cell-enriched clusters), while regulatory network analysis uncovered has-miR-155-5p and TF hubs (SMAD5/SAP30/PHF8) as potential master regulators.
Conclusions:
This study utilized platelet-associated biomarkers to establish a machine learning-based diagnostic model for KD and elucidated their pathogenic roles in immune dysregulation and vascular injury, thereby laying the foundation for improved diagnosis and targeted therapy in KD.
