Uncovering potential novel biomarkers in Crohn's disease using integrated bioinformatics analysis

Abstract Background: Crohn's disease is a chronic nonspecific intestinal inflammatory disease with unknown etiology. This study aimed to predict potential novel biomarkers of Crohn's disease.Methods: Gene expression datasets of Crohn's disease and normal samples were downloaded from the Gene Expression Omnibus (GEO) database. First, differential gene expression analysis and weighted gene coexpression network analysis (WGCNA) were performed. Common genes (CGs) were obtained by the intersection of differentially expressed genes (DEGs) and optimal modal genes of WGCNA. Subsequently, a protein-protein interaction (PPI) network was established to screen hub genes and then establish a Crohn's disease risk prediction model based on hub genes. A receiver operating characteristic (ROC) curve, and area under the curve (AUC) were used to evaluate the prediction ability of the model. Finally, the mirTarBase database, starBase database, and TargetScan database were used to predict microRNAs (miRNAs) and transcription factors (TFs) that cause Crohn's disease.Results: A total of 74 DEGs were identified. WGCNA showed that the signature gene in the blue module was significantly associated with Crohn's disease (p=4e-6) and obtained 32 CGs. Five hub genes (CDH17, CSF1R, CXCL10, CXCL9, COL3A1) were identified and well predicted Crohn's disease risk (AUC=0.853). Meanwhile, in the miRNA-mRNA regulatory network and TF-mRNA regulatory network, we found that 3 miRNAs (hsa-miR-29a-3p, hsa-miR-29b-3p, hsa-miR-29c-3p) and 2 TFs (TCF4, HINFP) regulate multiple CGs.Conclusions: Five genes (CDH17, CSF1R, CXCL10, CXCL9 and COL3A1), three miRNAs (hsa-miR-29a-3p, hsa-miR-29b-3p, hsa-miR-29c-3p) and two TFs (TCF4, HINFP) may be involved in the pathogenesis of Crohn's disease.