ArticleImmunoTargets and therapy2024
Identification of CXC Chemokine Receptor 2 (CXCR2) as a Novel Eosinophils-Independent Diagnostic Biomarker of Pediatric Eosinophilic Esophagitis by Integrated Bioinformatic and Machine-Learning Analysis.
Article in ImmunoTargets and therapy, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Integrative Transcriptomic and Machine Learning Analysis Identifies JAG1 and NR2F2 in Keloid.Clinical, cosmetic and investigational dermatology · 2026Article
- Application of artificial intelligence in eosinophilic esophagitis.Frontiers in immunology · 2025Review
- Identifying epithelial-mesenchymal transition-related genes as prognostic biomarkers and therapeutic targets of hepatocellular carcinoma by integrated analysis of single-cell and bulk-RNA sequencing data.Translational cancer research · 2024Article
- Exploring BSCL2 and associated genes in Alzheimer's disease by integrative analysis of bioinformatics, sn-RNAseq and machine learning approach.Journal of Alzheimer's disease reportsArticle
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4 authors.
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Abstract
Background: Eosinophilic esophagitis (EoE) is a complex allergic condition frequently accompanied by various atopic comorbidities in children, which significantly affects their life qualities. Therefore, this study aimed to evaluate pivotal molecular markers that may facilitate the diagnosis of EoE in pediatric patients. Methods: Three available EoE-associated gene expression datasets in children: GSE184182, GSE 197702, GSE55794, along with GSE173895 were downloaded from the GEO database. Differentially expressed genes (DEGs) identified by "limma" were intersected with key module genes identified by weighted gene co-expression network analysis (WGCNA), and the shared genes went through functional enrichment analysis. The protein-protein interaction (PPI) network and the machine learning algorithms: least absolute shrinkage and selection operator (LASSO), random forest (RF), and XGBoost were used to reveal candidate diagnostic markers for EoE. The receiver operating characteristic (ROC) curve showed the efficacy of differential diagnosis of this marker, along with online databases predicting its molecular regulatory network. Finally, we performed gene set enrichment analysis (GSEA) and assessed immune cell infiltration of EoE/control samples by using the CIBERSORT algorithm. The correlations between the key diagnostic biomarker and immune cells were also investigated. Results: The intersection of 936 DEGs and 1446 key module genes in EoE generated 567 genes, which were primarily enriched in immune regulation. Following the construction of the PPI network and filtration by machine learning, CXCR2 served as a potential diagnostic biomarker of pediatric EoE with a perfect diagnostic efficacy (AUC = ~1.00) in regional tissue/peripheral whole blood samples. Multiple infiltrated immune cells were observed to participate in disrupting the homeostasis of esophageal epithelium to varying degrees. Conclusion: The immune-correlated CXCR2 gene was proved to be a promising diagnostic indicator for EoE, and dysregulated regulatory T cells (Tregs)/neutrophils might play a crucial role in the pathogenesis of EoE in children.
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