Identifying Key Genes to the Early Diagnosis of Inflammatory Bowel Disease by Integrating Analysis at the Blood and Tissue Levels

Xin Yu Wang, Dan Zhang

Abstract


Background: Inflammatory bowel disease (IBD) encompasses Crohns disease (CD) and ulcerative colitis (UC), is challenging to diagnose, and frequently relapses, significantly affecting patients quality of life. Despite extensive efforts, the pathogenesis of IBD remains unclear.

Methods: In this study, we integrated bioinformatics analysis and animal disease model to investigate IBD from two dimensions to identify potential diagnostic biomarkers and explore the pathogenesis of distinct conditions at tissue-specific levels.

Results: Firstly, we identified dysferlin (DYSF) and C-X-C motif chemokine ligand 2 (CXCL2) as crucial biomarkers for IBD, with 11 and 13 putative biomarkers for CD and UC, respectively, identified by peripheral blood testing only. CXCL8 and S100 calcium-binding protein A8 (S100A8) were determined to be critical hub genes and validated by real-time polymerase chain reaction (RT-PCR). Secondly, in CD, the differentially expressed genes (DEGs) were mainly associated with immunity based on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, while the metabolism of multiple substances and substance transport activity were dominant in UC. Thirdly, essential genes in the pathological progression of CD and UC were identified through protein-protein interaction networks and molecular complex detection (MCODE) analysis. Finally, pathological examination and quantitative analysis of IBD models confirmed the above results.

Conclusions: Our findings could contribute to understanding the molecular mechanism of IBD, hold clinical significance for early diagnosis and prevention, and provide effective targets for treating IBD.




Gastroenterol Res. 2023;16(6):318-333
doi: https://doi.org/10.14740/gr1683

Keywords


Inflammatory bowel disease; Crohn’s disease; Ulcerative colitis; Differentially expressed genes; Integrated analysis; IBD models

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