Identification and validation of novel platelet-related genes in ulcerative colitis using bioinformatics and machine learning strategies

Abstract

Ulcerative colitis (UC) is a chronic, relapsing inflammatory disorder of the colonic mucosa. Although colonoscopy remains the diagnostic cornerstone for UC, its invasive nature highlights the need for additional biomarkers. This study used differential expression analysis, weighted gene co-expression network analysis (WGCNA), and machine-learning algorithms to identify three key platelet-related genes (PRGs)—TRIM22, TIMP1, and RAC2—that are involved in UC development. Immune cell analysis and single-cell analyses indicated that the expression of these key genes was significantly correlated with macrophages, neutrophils, and mast cells. Moreover, these key PRGs were mainly involved in pathways related to UC, such as chemokine signaling and leukocyte transendothelial migration. The expression levels of the key PRGs were further validated in independent datasets, dextran sulfate sodium (DSS)-induced colitis tissues, and peripheral blood mononuclear cells (PBMC) samples from UC patients. Collectively, these findings support a platelet-related three-gene signature with potential diagnostic and biological relevance in UC.