Construction of a neural network diagnostic model and investigation of immune infiltration characteristics for Crohn’s disease - Yang, Yufei; Xu, Lijun; Qiao, Yuqi; Wang, Tianrong; Zheng, Qing (Frontiers in Genetics, 2022)
ABSTRACT Objective: Crohn’s disease (CD), as a chronic recurrent illness, is one type of the inflammatory bowel diseases whose incidence and prevalence rates are gradually increasing. However, there is no universally acceptable criterion for CD diagnosis. The goal of this study was to create a diagnostic prediction model for CD and identify immune cell infiltration features in CD. Methods: In this study, Gene expression microarray datasets were obtained from Gene Expression Omnibus (GEO) database. Then, we identified differentially expressed genes (DEGs) between 178 CD and 38 control. Enrichment analysis of DEGs was also performed to explore the biological role of DEGs. Moreover, the “randomForest” package was applied to select core genes which were employed to create the neural network model. Finally, In the training cohort, we used CIBERSORT to evaluate the immune landscape between CD and normal group. Results: The results of enrichment analysis revealed that these DEGs may get involved in biological processes associated with immunity and inflammatory response. Moreover, the top 3 hub genes in the protein-protein interaction (PPI) network were IL-1β, CCL2, and CXCR2. The diagnostic model presented significant discrimination with the AUC curve is 0.984 [95%CI: 0.971–0.993]. The validation cohort (GSE36807) was utilized to ensure the reliability and applicability of the model. In addition, the immune infiltration analysis indicated the 9 kinds of immune cell types were significant differences between CD and healthy control. Conclusion: In summary, this study offered a novel insight for diagnosis of CD and facilitated to provide potential biomarkers for precise treatment.