Crohn’s disease (CD) and ulcerative colitis (UC) can be difficult to differentiate. As differential diagnosis is important in establishing a long-term treatment plan for patients, we aimed to develop a machine learning model for the differential diagnosis of the two diseases using RNA sequencing (RNA-seq) data from endoscopic biopsy tissue from patients with inflammatory bowel disease (n = 127; CD, 94; UC, 33). Biopsy samples were taken from inflammatory lesions or normal tissues. The RNA-seq dataset was processed via mapping to the human reference genome (GRCh38) and quantifying the corresponding gene models that comprised 19,596 protein-coding genes. An unsupervised learning model showed distinct clusters of four classes: CD inflammatory, CD normal, UC inflammatory, and UC normal. A supervised learning model based on partial least squares discriminant analysis was able to distinguish inflammatory CD from inflammatory UC after pruning the strong classifiers of normal CD vs. normal UC. The error rate was minimal and affected only two components: 20 and 50 genes for the first and second components, respectively. The corresponding overall error rate was 0.147. RNA-seq analysis of tissue and the two components revealed in this study may be helpful for distinguishing CD from UC.

Colorectal cancers (CRC) are classified into consensus molecular subtypes (CMS) based on gene expression profiles. The revised classification system iCMS was proposed by considering intrinsic epithelial status, microsatellite instability (MSI), and fibrosis. This study aimed to provide molecular evidence for the adenoma–carcinoma sequence concept by examining CRC and synchronous adenomas using iCMS. Epithelial CMS cell proportion was estimated using CiberSortx, an in silico cell fractionation method that included CMS cell types among the reference cell types. A random forest (RF) model estimated the posterior probabilities of CMS classes, which were compared with the CiberSortx results. Gene expression profiles of the published iCMS signature panel were retrieved from our dataset and subjected to heatmap clustering for classification. Bulk RNA sequencing data were collected from 29 adenocarcinomas and 11 adenoma samples. CiberSortx showed all CRC contained either CMS2 or CMS3 as the major epithelial cancer cell type. The RF model classified approximately half of the CRC as CMS4, whereas CMS4 was hardly detected by CiberSortx. Because they were enriched with myofibroblasts as per the CiberSortx classification, we tentatively designated them as iCMS2-F/iCMS3-F. iCMS coupled with the application of an in silico cell fractionation method can provide the molecular dissection of CRC and adenoma.

We investigated whether the response to anti-tumor necrosis factor (anti-TNF) treatment varied according to inflammatory tissue characteristics in Crohn’s disease (CD). Bulk RNA sequencing (RNA-seq) data were obtained from inflamed and non-inflamed tissues from 170 patients with CD. The samples were clustered based on gene expression profiles using principal coordinate analysis (PCA). Cellular heterogeneity was inferred using CiberSortx, with bulk RNA-seq data. The PCA results displayed two clusters of CD-inflamed samples: one close to (Inflamed_1) and the other far away (Inflamed_2) from the non-inflamed samples. Inflamed_1 was rich in anti-TNF durable responders (DRs), and Inflamed_2 was enriched in non-durable responders (NDRs). The CiberSortx results showed that the cell fraction of activated fibroblasts was six times higher in Inflamed_2 than in Inflamed_1. Validation with public gene expression datasets (GSE16879) revealed that the activated fibroblasts were enriched in NDRs over Next, we used DRs by 1.9 times pre-treatment and 7.5 times after treatment. Fibroblast activation protein (FAP) was overexpressed in the Inflamed_2 and was also overexpressed in the NDRs in both the RISK and GSE16879 datasets. The activation of fibroblasts may play a role in resistance to anti-TNF therapy. Characterizing fibroblasts in inflamed tissues at diagnosis may help to identify patients who are likely to respond to anti-TNF therapy.