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Predicting pediatric Crohn's disease based on six mRNA-constructed risk signature using comprehensive bioinformatic
Yuanyuan Zhan1, Quan Jin2, Tagwa Yousif Elsayed Yousif3
1Department of Plastic and Cosmetic Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Wuhan 430030, China.
Insights
Researchers developed a new non-invasive method to diagnose pediatric Crohn
Area of Science:
- Gastroenterology and Molecular Diagnostics
Background:
- Pediatric Crohn's disease (CD) diagnosis is challenging due to its chronic and recurrent nature.
- Current diagnostic methods for pediatric CD lack non-invasive and timely approaches.
Purpose of the Study:
- To develop a novel, non-invasive predictive risk signature for diagnosing pediatric Crohn's disease.
- To identify key molecular markers for early detection of pediatric CD.
Main Methods:
- Bioinformatics analysis of six messenger RNAs (mRNAs): APCDD1, MAP3K5, LCT1, SMS1, and TMEM184B.
- Validation using statistical methods including support vector machine learning and weighted gene co-expression network analysis.
- Pathological assessment via Hematoxylin-eosin staining and immunohistochemistry.
Main Results:
- A predictive risk signature comprising six mRNAs was constructed for pediatric CD.
- Adenomatosis polyposis downregulated 1 (APCDD1) showed high expression in pediatric CD tissues.
- The signature demonstrated good predictive efficacy, confirmed by decision curve and AUC analyses.
Conclusions:
- The developed predictive risk signature serves as a non-invasive supplementary tool for pediatric CD diagnosis.
- The findings highlight the role of identified immune and cytokine signaling pathways in pediatric CD pathogenesis.
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.
