| Literature DB >> 35757383 |
Xiaoxuan Lin1, Yu Wang1, Zishan Liu1, Sinan Lin1, Jinyu Tan1, Jinshen He1, Fan Hu1, Xiaomin Wu1, Subrata Ghosh2, Minhu Chen1, Fen Liu3, Ren Mao3.
Abstract
Intestinal strictures remain one of the most intractable and common complications of Crohn's disease (CD). Approximately 70% of CD patients will develop fibrotic strictures after 10 years of CD diagnosis. Since specific antifibrotic therapies are unavailable, endoscopic balloon dilation and surgery remain the mainstay treatments despite a high recurrence rate. Besides, there are no reliable methods for accurately evaluating intestinal fibrosis. This is largely due to the fact that the mechanisms of initiation and propagation of intestinal fibrosis are poorly understood. There is growing evidence implying that the pathogenesis of stricturing CD involves the intricate interplay of factors including aberrant immune and nonimmune responses, host-microbiome dysbiosis, and genetic susceptibility. Currently, the progress on intestinal strictures has been fueled by the advent of novel techniques, such as single-cell sequencing, multi-omics, and artificial intelligence. Here, we perform a timely and comprehensive review of the substantial advances in intestinal strictures in 2021, aiming to provide prompt information regarding fibrosis and set the stage for the improvement of diagnosis, treatment, and prognosis of intestinal strictures.Entities:
Keywords: Crohn’s disease; diagnosis; fibrosis; intestinal strictures; pathogenesis; treatment
Year: 2022 PMID: 35757383 PMCID: PMC9218441 DOI: 10.1177/17562848221104951
Source DB: PubMed Journal: Therap Adv Gastroenterol ISSN: 1756-283X Impact factor: 4.802
Figure 1.Schematic diagram of novel mechanisms of intestinal stricture in 2021. NOD2 mutations caused dysregulated homeostasis of activated fibroblasts and macrophages. Tryptase released from MCs, VDR deficiency in epithelial cells, AGR2 secreted by epithelial cells under ER stress, and acidic pH facilitated differentiation of fibroblasts into myofibroblasts. VDR deficiency in epithelial cells also contributed to EMT. Bacteroides fragilis colonization and the increasement of circulating bone marrow-derived fibrocytes were correlated with fibrosis formation. FN which is secreted by HIMC mediated the migration of preadipocytes via the FN/α5β1 integrin signaling pathway, thus contributing to the formation of CrF.
AGR2, anterior gradient protein 2 homolog; CrF, creeping fat; ECM, extracellular matrix; EMT, epithelial-to-mesenchymal transition; ER stress, endoplasmic reticulum stress; FN, fibronectin; HIMC, human intestinal muscle cells; MCs, mast cells; NOD2, nucleotide-binding oligomerization domain 2; VDR, vitamin D receptor.
Figure 2.Novel molecules are involved in activation of intestinal fibroblasts. (a) YAP/TAZ activation was dependent on Rho/ROCK signaling pathway, and increased the expression of profibrotic-related genes and proliferation of fibroblasts. (b) The upregulation of miR-155 targeted HBP1 and then activated the Wnt/βcatenin signaling pathway, leading to the activation of fibroblasts. (c) TL1A accompanied with IL-13 induced EMT and activated fibroblasts via TGF-β1/Smad3 pathway. (d) The pharmacological activation of NR4A1 with cytosporone B or 6-mercaptopurine attenuated TGF-β1-induced fibrosis in myofibroblasts. (e) Gp130 family- and STAT3-related genes were highly enriched in NOD2-mutated fibroblasts and macrophages, which led to the differentiation of macrophages into activated fibroblasts.
EMT, epithelial-to-mesenchymal transition; HBP1, HMG-box transcription factor 1; IL, interleukin; NOD2, nucleotide-binding oligomerization domain 2; NR4A1, nuclear receptor 4 A1; ROCK, Rho-associated coiled-coil-containing protein kinase; STAT3, signal transducer and activator of transcription 3; TAZ, transcriptional coactivator with PDZ-binding motif; TGF-β1, transforming growth factor beta 1; TL1A, tumor necrosis factor-like ligand 1A; YAP, Yes-associated protein; α-SMA, α-smooth muscle actin.
Novel assessment and prediction models for intestinal strictures in CD.
| Model | Equipment | Parameters | Functions | AUC value | Accuracy | Sensitivity | Specificity | Limitations | References |
|---|---|---|---|---|---|---|---|---|---|
| RM | CTE | Busynessoriginal, busynesscoif3r05, busynessdb1r05, busynessdb1r15 | Differentiate moderate–severe from none–mild intestinal fibrosis | 0.888 | 0.857 | 0.815 | 0.939 | Relatively small sample sizes; time-consuming for manual volume of interest segmentation; not suitable for intestinal strictures with blurred contour | Li |
| State-of-the-art deep learning network | CE | Strictures images, normal mucosa images, and ulcer images | Identify intestinal strictures | 0.989 | 0.935 | 0.92 | 0.89 | A retrospective analysis; lack of strictures that were failed to pass CE or visible on cross-sectional images; lack of dynamic time-lapse movement images | Klang |
| Blood protein and serologic markers | – | Four plasma proteins (IL7, MMP10, IL12B, and CCL11) and two serologic markers (LnASCA IgA and LnCbir) | Predict the risk of stricturing complications | 0.68 | – | – | – | Relatively small sample sizes; no specific timepoints to diagnosis complications; no comparable external validation cohort | Ungaro |
The accuracy, sensitivity, and specificity were calculated from the receiver operating characteristic curve according to the Youden index.
AUC, area under the receiver operating characteristic curve; CD, Crohn’s disease; CE, capsule endoscopy; CTE, computed tomography enterography; RM, radiomic model.