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. 2021 Jun 7;19(1):250.
doi: 10.1186/s12967-021-02909-z.

Characterization of terminal-ileal and colonic Crohn's disease in treatment-naïve paediatric patients based on transcriptomic profile using logistic regression

Affiliations

Characterization of terminal-ileal and colonic Crohn's disease in treatment-naïve paediatric patients based on transcriptomic profile using logistic regression

Ilkyu Park et al. J Transl Med. .

Erratum in

Abstract

Background: Inflammatory bowel disease (IBD) is a chronic and idiopathic inflammatory disorder of the gastrointestinal tract and comprises ulcerative colitis (UC) and Crohn's disease (CD). Crohn's disease can affect any part of the gastrointestinal tract, but mainly the terminal ileum and colon. In the present study, we aimed to characterize terminal-ileal CD (ICD) and colonic CD (CCD) at the molecular level, which might enable a more optimized approach for the clinical care and scientific research of CD.

Methods: We analyzed differentially expressed genes in samples from 23 treatment-naïve paediatric patients with CD and 25 non-IBD controls, and compared the data with previously published RNA-Seq data using multi-statistical tests and confidence intervals. We implemented functional profiling and proposed statistical methods for feature selection using a logistic regression model to identify genes that are highly associated in ICD or CCD. We also validated our final candidate genes in independent paediatric and adult cohorts.

Results: We identified 550 genes specifically expressed in patients with CD compared with those in healthy controls (p < 0.05). Among these DEGs, 240 from patients with CCD were mainly involved in mitochondrial dysfunction, whereas 310 from patients with ICD were enriched in the ileum functions such as digestion, absorption, and metabolism. To choose the most effective gene set, we selected the most powerful genes (p-value ≤ 0.05, accuracy ≥ 0.8, and AUC ≥ 0.8) using logistic regression. Consequently, 33 genes were identified as useful for discriminating CD location; the accuracy and AUC were 0.86 and 0.83, respectively. We then validated the 33 genes with data from another independent paediatric cohort (accuracy = 0.93, AUC = 0.92) and adult cohort (accuracy = 0.88, AUC = 0.72).

Conclusions: In summary, we identified DEGs that are specifically expressed in CCD and ICD compared with those in healthy controls and patients with UC. Based on the feature selection analysis, 33 genes were identified as useful for discriminating CCD and ICD with high accuracy and AUC, for not only paediatric patients but also independent cohorts. We propose that our approach and the final gene set are useful for the molecular classification of patients with CD, and it could be beneficial in treatments based on disease location.

Keywords: Colonic CD; Crohn’s disease; Logistic regression; Paediatric patients; Terminal-ileal CD; Transcriptomic profile.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
Principal component analysis (PCA) of newly diagnosed patients with paediatric CD. Unsupervised PCA analysis revealed differences according to diagnosis and gut segment; colon control (royal blue) vs. ileum control (spring green) and colon CD (navy blue) vs. ileum CD (dark green). CD Crohn’s disease
Fig. 2
Fig. 2
Overview of the study design. Schematic representation of this research. CD Crohn’s disease, UC ulcerative colitis, AUC area under curve
Fig. 3
Fig. 3
A Venn diagram illustrating differentially expressed genes in each tissue type. CCGs [n = 240] and ICGs [n = 310] were obtained in mucosal biopsies samples. Shaded area indicates CD common DEGs [n = 471] regardless of tissue type; CD Crohn’s disease
Fig. 4
Fig. 4
Samples from treatment-naïve paediatric patients with CD show molecular differences. Hierarchical clustering and heatmaps of RNA-Seq data of CCGs and ICGs with six different expression types. Samples are color-coded in the top bar according to the sample types and diagnosis [CD, control] and genes are color-coded in the left sidebar according to their expression types. a A heatmap of CCGs. b A heatmap of ICGs. CD Crohn’s disease
Fig. 5
Fig. 5
Gene ontology (GO) enrichment analysis of CCGs and ICGs. The x-axes represent the rich factor, which is the ratio of differentially expressed targeted gene numbers in the process to all the annotated genes located in the process. The higher rich factor indicates the higher level of enrichment. The dot size represents the number of target genes in the GO. a Scatter chart displaying Gene ontology of CCD genes, b Scatter chart displaying Gene ontology of ICD genes. CCGs colonic CD genes, ICGs terminal-ileal CD genes
Fig. 6
Fig. 6
Protein–protein interactions among selected colonic CD genes (CCGs) involved in mitochondrial dysfunction. a The STRING graph shows 20 genes of mitochondrial dysfunction with interactions. Edges represent protein–protein associations and line width shows the strength of interaction. b Expression pattern of differentially expressed genes of type B. All 20 genes were belong to expression type B. CCGs colonic CD genes
Fig. 7
Fig. 7
Final candidate genes that discriminate colonic CD from ileal CD and CD from UC. The boxplot of expression levels of ANP32E in the colon CD, Ileum CD, and UC samples of paediatric patients. Boxplots shows differences between three groups. Three group comparisons for each analysis are performed by Kruskal–Wallis test shown in p-values. CD Crohn’s disease, UC ulcerative colitis

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