Abstract
Crohn's disease (CD) is a recurrent, chronic inflammatory condition of the gastrointestinal tract which is a clinical subtype of inflammatory bowel disease for which timely and non-invasive diagnosis in children remains a challenge. A novel predictive risk signature for pediatric CD diagnosis was constructed from bioinformatics analysis of six mRNAs, adenomatosis polyposis downregulated 1 (APCDD1), complement component 1r, mitogen-activated protein kinase kinase kinase kinase 5 (MAP3K5), lysophosphatidylcholine acyltransferase 1, sphingomyelin synthase 1 and transmembrane protein 184B, and validated using samples. Statistical evaluation was performed by support vector machine learning, weighted gene co-expression network analysis, differentially expressed genes and pathological assessment. Hematoxylin-eosin staining and immunohistochemistry results showed that APCDD1 was highly expressed in pediatric CD tissues. Evaluation by decision curve analysis and area under the curve indicated good predictive efficacy. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes and gene set enrichment analysis confirmed the involvement of immune and cytokine signaling pathways. A predictive risk signature for pediatric CD is presented which represents a non-invasive supplementary tool for pediatric CD diagnosis.