Exploring the Relevance of Disulfidptosis to the Pathophysiology of Ulcerative Colitis by Bioinformatics Analysis

Ulcerative colitis (UC) is a nonspecific inflammatory disease confined to the intestinal mucosa and submucosa, and its prevalence significantly increases each year. Disulfidptosis is a recently discovered new form of cell death that has been suggested to be involved in multiple diseases. The aim of this study was to explore the relevance of disulfidptosis in UC.

First, the UC datasets were downloaded from the Gene Expression Omnibus (GEO) database, and UC samples were typed based on upregulated disulfidptosis-related genes (DRGs). Then, weighted gene co-expression network analysis (WGCNA) was performed on the datasets and molecular subtypes of UC, respectively, to obtain candidate signature genes. After validation of the validation set and qRT-PCR, we constructed a nomogram model by signature genes to predict the risk of UC. Finally, single-cell sequencing analysis was used to study the heterogeneity of UC and to demonstrate the expression of DRGs and signature genes at the single-cell level.

A total of 7 DRGs were significantly upregulated in the expression profiles of UC, and 180 UC samples were divided into two subtypes based on these DRGs. Five candidate signature genes were obtained by intersecting two key gene modules selected by WGCNA. After evaluation, four signature genes with diagnostic relevance (COL4A1, PRRX1, NNMT, and PECAM1) were eventually identified. The nomogram model showed excellent prediction ability. Finally, in the single-cell analysis, there were eight cell types (including B cells, T cells, monocyte, smooth muscle cells, epithelial cells, neutrophil, endothelial cells and NK cells) were identified. The signature genes were significantly expressed mainly in endothelial cells and smooth muscle cells.

In this study, subtypes related to disulfidptosis were identified, and single-cell analysis was performed to understand the pathogenesis of UC from a new perspective. Four signature genes were screened and a prediction model with high accuracy was established. This provides novel insights for early diagnosis and therapeutic targets in UC.