The Immune Landscape and Molecular Subtypes of Pediatric Crohn's Disease: Results from In Silico Analysis.
Shiyu XiaoWenhui XieYinghui ZhangYan PanLei LeiPublished in: Journal of personalized medicine (2023)
Pediatric Crohn's disease (CD) presents a distinct phenotype from adult-onset disease. A dysregulated immune response is critical in CD pathogenesis; thus, it is clinically important to describe immune cell alterations and to identify a new molecular classification for pediatric CD. To this end, in this study, a RNA-seq derived dataset GSE101794-which contains the expression profiles of 254 treatment-naïve pediatric CD samples, including CIBERSORTx and weighted gene-co-expression network analysis (WGCNA)-were performed to estimate the ratio of immune cells and to identify modules and genes related to specific immune cell infiltration, respectively. Hub genes derived from WGCNA were further employed to create a molecular classification using unsupervised K-means clustering. In the pediatric CD samples, it was found that M2 macrophages, CD4 + memory resting T cells, CD8 + T cells, and resting mast cells were the most prominent immune cells in intestinal tissues. Then, 985 up-regulated genes and 860 down-regulated genes were identified in samples with high immune cell infiltration. Of these differential genes, 10 hub genes (APOA1, CYB5A, XPNPEP2, SLC1A7, SLC4A6, LIPE, G6PC, AGXT2, SLC13A1, and SOAT2) were associated with CD8 + T cell infiltration. Clinically, the higher expression of these 10 hub genes was strongly associated with an earlier age of CD onset and colonic-type CD. Furthermore, based on these key genes, pediatric CD could be classified into three molecular subtypes, displaying a different immune landscape. Altogether, this in silico analysis provides a novel insight into the immune signature of pediatric CD, and a new classification of pediatric CD is presented, which may help us develop more personalized disease management and treatments for pediatric CD.
Keyphrases
- genome wide
- network analysis
- bioinformatics analysis
- genome wide identification
- immune response
- nk cells
- machine learning
- rna seq
- poor prognosis
- single cell
- magnetic resonance
- magnetic resonance imaging
- transcription factor
- computed tomography
- inflammatory response
- toll like receptor
- dendritic cells
- molecular docking
- heart rate variability
- long non coding rna
- replacement therapy
- working memory