Oxidative stress gene expression in ulcerative colitis: implications for colon cancer biomarker discovery

There is a complex interrelationship between colorectal cancer (CRC) and ulcerative colitis (UC). This study aimed to identify key molecules and pathways involved in the co-occurrence of CRC and UC, as well as the role of oxidative stress in disease progression, through bioinformatics analysis of public RNA sequencing databases. We downloaded datasets from public repositories and conducted gene set enrichment analysis (GSEA), screening for oxidative stress-related differentially expressed genes (OXSRDEGs) to evaluate their diagnostic potential. Subsequently, we performed Gene Ontology (GO) analysis and Kyoto encyclopedia of genes and genomes (KEGG) analyses, followed by immune infiltration analysis using the single-sample gene-set enrichment analysis (ssGSEA) and CIBERSORT algorithms. By constructing a multivariate Cox prognostic model using Kaplan-Meier curves and least absolute shrinkage and selection operator (LASSO) regression analysis, we assessed the model's prognostic capability. Furthermore, we utilized the STRING database and Cytoscape to establish a protein-protein interaction (PPI) network and constructed an mRNA-transcription factor (TF) and mRNA-miRNA interaction networks. The molecular functions and signaling pathways enriched in OXSRDEGs were determined. The robust diagnostic efficacy of OXSRDEGs was verified. This analysis suggests that immune cells may collaborate with OXSRDEGs to impact the onset and progression of diseases. A total of 6 OXSRDEGs with prognostic significance were identified, and the multifactorial Cox regression model constructed demonstrated a strong clinical predictive capacity. The mRNA-transcription factor (TF) and mRNA-miRNA interaction networks revealed that OXSRDEGs are regulated by multiple miRNAs and many transcription factors. Common biomarkers of oxidative stress in the pathogenesis, disease progression, gene expression, and transcription of ulcerative colitis and colorectal cancer have been identified, presenting potential therapeutic targets. The model may be beneficial in prognostic prediction and guiding treatment decisions.