Construction of a Feature Gene and Machine Prediction Model for Inflammatory Bowel Disease Based on Multi - Chip Joint Analysis

Background Inflammatory bowel disease (IBD) is a chronic non - specific inflammatory disorder triggered by immune responses and genetic factors. Currently, there is no cure for IBD, and its etiology remains unclear. As a result, early detection and diagnosis of IBD pose significant challenges. Therefore, investigating biomarkers in peripheral blood is of utmost importance, as it can assist doctors in the early identification and management of IBD. Methods We employed the multi - chip joint analysis approach to thoroughly explore the database. Based on methods such as artificial neural networks (ANN), machine learning techniques, and the SHAP model, we developed a diagnostic model for IBD. To select genetic features, we utilized three machine learning algorithms: the Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine (SVM), and Random Forest (RF) to screen for differentially expressed genes. Additionally, we conducted an in - depth analysis of the enriched molecular pathways of these differentially expressed genes through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Moreover, we used the SHAP model to interpret the results of the machine learning process. Finally, we examined the relationship between differentially expressed genes and immune cells. Results Through machine learning, we identified four crucial biomarkers for IBD, namely LOC389023, DUOX2, LCN2, and DEFA6. The SHAP model was used to elucidate the contribution of differentially expressed genes in the diagnostic model. These genes are primarily associated with immune system modulation and microbial alterations. GO and KEGG pathway enrichment analyses indicated that the differentially expressed genes demonstrated excellent performance in molecular pathways such as the Antimicrobial and IL − 17 signaling pathways. By performing correlation and differential analyses between differentially expressed genes and immune cells, we found that M1 macrophages exhibited stable differential changes across all four differentially expressed genes. M2 macrophages, resting mast cells, neutrophils, and activated CD4 memory T cells all showed significant differences among three of the differentially expressed genes. Conclusion We have identified differentially expressed genes (LOC389023, DUOX2, LCN2, and DEFA6) with significant immune - related effects in IBD. Our findings suggest that machine learning algorithms outperform ANN in the diagnosis of IBD. This research provides a theoretical foundation for the clinical diagnosis, targeted therapy, and prognosis evaluation of IBD.