Frontiers in immunology
Identification of Crohn's disease-associated molecular signatures by integrative bioinformatics analysis and experimental validation
Par Li P, Guo S, Hu H et 5 autres.
Contenu en anglais
Résumé
Background
Crohn's disease (CD) is a chronic inflammatory bowel disease with rising global incidence. Its multifactorial etiology involves genetic, environmental, microbial, and immunological factors. Current diagnostic methods are invasive and costly, underscoring the need for improved biomarkers and non-invasive tools for early detection and monitoring.
Methods
Using bioinformatics and machine learning, we analyzed datasets from the Gene Expression Omnibus (GEO) to identify diagnostic biomarkers and immune infiltration patterns. Multi-omics integration and immune profiling were employed. Differential gene expression, Weighted Gene Co-expression Network Analysis (WGCNA), and Mendelian randomization (MR) identified causal genes. Machine learning (12 algorithms, 113 combinations) developed diagnostic models, validated via Receiver Operating Characteristic (ROC) analysis. Protein-protein interaction network, immune infiltration, and gene enrichment analysis identified biological pathways or functions of key genes. Single-cell sequencing validated key findings. Key genes were experimentally validated through immunohistochemistry (IHC), quantitative polymerase chain reaction (qPCR) and Western blot (WB).