Diagnosis of Crohn's disease (CD) is often challenging and remains an urgent need for new diagnose options. This study aimed to identify new serological biomarkers and explored the role of these markers in the diagnosis of CD .
Gene expression profiles in CD and normal for tissue and peripheral blood were downloaded from the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) were identified by R software. Gene set enrichment analysis (GSEA), gene ontology(GO) enrichment and kyoto encyclopedia of genes and genomes(KEGG) analysis were performed to inspect the functional annotation of these DEGs. The overlapping DEGs that were up-/down-regulated both in the tissue and blood sample were used to develop diagnose model. ROC curve analysis was used to evaluate the model's performance.
A total of 224 DEGs were identified between CD and normal tissue samples from the datasets GSE126124, GSE95095 and GSE16879. Enrichment analysis indicated that DEGs are mostly enriched in humoral immune response, regulation of inflammatory response, granulocyte migration, cell chemotaxis, cytokine-cytokine receptor interaction, chemokine signaling pathway, IL-17 signaling pathway, NOD-like receptor signaling pathway, TNF signaling pathway, complement coagulation cascades. Six haematologic/tissular-specific expressed genes(PDZK1IP1, STAT1, PI3, VAV1, PTGDS and LAIR2) were identified from the GSE119600 datasets. A proposed model with STAT1, PI3 ,VAV1 and PTGDS achieved 86.3% accuracy, 91.0% sensitivity and 77.1% specificity in the training set. The model also displayed good discriminative power with 96.4% sensitivity and 83.3% specificity in the validation set.
This work identified four genes as potential hematologic biomarkers of CD. The proposed model showed good performance which would be helpful for the early diagnosis of CD.