⚡ Quick Summary
This study identifies CD79A and GADD45A as novel immune-related biomarkers for assessing the severity of respiratory syncytial virus (RSV) infections in children. Utilizing machine learning techniques, the research demonstrates a high predictive accuracy (AUC = 0.950) for these biomarkers, paving the way for improved clinical management of RSV.
🔍 Key Details
- 📊 Datasets Used: GSE77087 (81 RSV-infected children) and GSE188427 (147 RSV-infected children)
- 🧬 Key Biomarkers: CD79A (downregulated in severe cases) and GADD45A (upregulated in severe cases)
- ⚙️ Technology: Machine Learning, specifically Support Vector Machine (SVM)
- 🏆 Performance: AUC of 0.950 for predicting hospitalization
🔑 Key Takeaways
- 📊 CD79A and GADD45A are identified as significant biomarkers for RSV severity.
- 💡 Machine learning models effectively prioritized these biomarkers for clinical relevance.
- 👶 Clinical validation confirmed the association of these biomarkers with disease severity in 92 children.
- 🏥 CD79A suppression and GADD45A elevation correlate with younger age and respiratory distress.
- 🌍 Immune profiling revealed distinct patterns of immune cell infiltration in severe versus mild cases.
- 🔬 Functional enrichment implicated endoplasmic reticulum stress in disease progression.
- 💊 Drug-target predictions suggest potential therapeutic avenues for RSV management.
📚 Background
Respiratory syncytial virus (RSV) is a major cause of severe respiratory infections in children, often leading to hospitalization. Despite its prevalence, effective biomarkers for assessing the severity of RSV infections are limited. This study aims to fill that gap by identifying immune-related biomarkers that can aid in clinical decision-making and improve patient outcomes.
🗒️ Study
The research utilized two publicly available transcriptomic datasets from the Gene Expression Omnibus (GEO) database, focusing on children with confirmed RSV infections. By analyzing gene expression data, the study aimed to identify differentially expressed genes (DEGs) that could serve as biomarkers for disease severity.
📈 Results
The analysis revealed 81 overlapping genes between hospitalized and outpatient RSV-infected children. The machine learning model, particularly the Support Vector Machine (SVM), achieved an impressive AUC of 0.950, highlighting CD79A and GADD45A as key predictors of hospitalization. Clinical validation showed significant changes in these biomarkers, with CD79A being downregulated and GADD45A upregulated in severe cases (p < 0.001).
🌍 Impact and Implications
The identification of CD79A and GADD45A as biomarkers for RSV severity has significant implications for clinical practice. These findings not only enhance our understanding of immune dysregulation in RSV infections but also provide a foundation for personalized management strategies. By integrating these biomarkers into clinical workflows, healthcare providers can better assess disease severity and tailor treatments accordingly.
🔮 Conclusion
This study underscores the potential of CD79A and GADD45A as clinically actionable biomarkers for RSV severity. The integration of machine learning in biomarker identification represents a promising advancement in pediatric infectious disease management. Continued research in this area could lead to improved outcomes for children affected by RSV and similar respiratory infections.
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CD79A and GADD45A as novel immune-related biomarkers for respiratory syncytial virus severity in children: an integrated machine learning analysis and clinical validation.
Abstract
BACKGROUND: Respiratory syncytial virus (RSV) is a leading cause of severe lower respiratory infections in children, yet biomarkers for assessing disease severity remain limited. Herein, we investigated the differential expression biomarkers between RSV infected hospitalized patients, healthy groups and RSV infected outpatients.
METHODS: Two publicly available transcriptomic datasets (GSE77087 and GSE188427) were retrieved from the Gene Expression Omnibus (GEO) database. The GSE77087 dataset comprised peripheral blood samples from 81 children with confirmed RSV infection (61 hospitalized and 20 outpatient) and 23 healthy controls. The GSE188427 dataset included 147 RSV-infected children (113 hospitalized and 34 outpatient) and 51 healthy controls. Genes with |log2 fold change (logFC)| > 0 and false discovery rate (FDR) < 0.05 were considered differentially expressed. Overlapping DEGs between the two datasets were identified using the VennDiagram package. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted on the intersecting DEGs via the clusterProfiler package, with terms deemed significant at FDR < 0.05.The CIBERSORT algorithm was applied to estimate the relative proportions of 22 immune cell types in 228 RSV-infected samples. Potential drug interactions for hug genes were predicted using the Drug-Gene Interaction Database (DGIdb). Competing endogenous RNA (ceRNA) networks were constructed using the SpongeScan database to identify lncRNAs interacting with the target miRNAs. Networks were visualized using Cytoscape (v3.10.1).Finally, Machine Learning-Based Biomarker Selection and hub gene identification and validation.
RESULTS: Differential gene expression analysis revealed 81 overlapping genes between hospitalized and outpatient RSV-infected children. Machine learning models, particularly SVM (area under the curve, AUC = 0.950), prioritized CD79A and GADD45A as key predictors of hospitalization. CD79A was significantly downregulated in severe cases, correlating with impaired B-cell responses and cytotoxic immunity, while GADD45A, upregulated in severe infections, linked to oxidative stress and neutrophil-driven inflammation. Immune cell profiling highlighted distinct infiltration patterns, with severe cases showing elevated naïve B cells and M0 macrophages versus activated NK cells and M1 macrophages in mild cases. Clinical validation in 92 children confirmed CD79A suppression and GADD45A elevation in severe RSV (p < 0.001), aligning with younger age, lower weight, and respiratory distress. Functional enrichment implicated endoplasmic reticulum stress and neutrophil extracellular traps in disease progression. Drug-target predictions and ceRNA networks further revealed therapeutic potential.
CONCLUSION: These findings establish CD79A and GADD45A as clinically actionable biomarkers for RSV severity, offering insights into immune dysregulation and guiding personalized management strategies.
Author: [‘Chen JJ’, ‘Lu ZZ’, ‘Jing YX’, ‘Nong XM’, ‘Qin Y’, ‘Huang JY’, ‘Lin N’, ‘Wei J’]
Journal: Front Immunol
Citation: Chen JJ, et al. CD79A and GADD45A as novel immune-related biomarkers for respiratory syncytial virus severity in children: an integrated machine learning analysis and clinical validation. CD79A and GADD45A as novel immune-related biomarkers for respiratory syncytial virus severity in children: an integrated machine learning analysis and clinical validation. 2025; 16:1609183. doi: 10.3389/fimmu.2025.1609183