Identification of neutrophil extracellular trap-related biomarkers in ulcerative colitis based on bioinformatics and machine learning.

BACKGROUND

The incidence of ulcerative colitis (UC) is rapidly increasing worldwide, but existing therapeutics are limited. Neutrophil extracellular traps (NETs), which have been associated with the development of various autoimmune diseases, may serve as a novel therapeutic target for UC treatment.

METHODS

Bioinformatics analysis was performed to investigate UC-related datasets downloaded from the GEO database, including GSE87466, GSE75214, and GSE206285. Differentially expressed genes (DEGs) related to NETs in UC patients and healthy controls were identified using Limma R package and WGCNA, followed by functional enrichment analysis. To identify potential diagnostic biomarkers, we applied the Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine-Recursive Feature Elimination (SVM-RFE) model, and Random Forest (RF) algorithm, and constructed Receiver Operating Characteristic (ROC) curves to evaluate accuracy. Additionally, immune infiltration analysis was conducted to identify immune cells potentially involved in the regulation of NETs. Finally, the expression of core genes in patients was validated using Quantitative real-time PCR (qRT-PCR), and potential therapeutic drugs for UC were explored through drug target databases.

RESULT

Differential analysis of transcriptomic sequencing data from UC samples identified 29 DEGs related to NETs. Enrichment analysis showed that these genes primarily mediate UC-related damage through biological functions such as leukocyte activation, migration, immune receptor activity, and the IL-17 signaling pathway. Three machine learning algorithms successfully identified core NETs-related genes in UC (IL1B, MMP9 and DYSF). According to ROC analysis, all three demonstrated excellent diagnostic efficacy. Additionally, Immune infiltration analysis revealed that the expression of these core genes was closely associated with neutrophils infiltration and CD4 memory T cell activation, and negatively associated with M2 macrophage infiltration. qRT-PCR showed that the core genes were significantly overexpressed in UC patients. Gevokizumab, canakinumab and carboxylated glucosamine were predicted as potential therapeutic drugs for UC.

CONCLUSION

By combining three machine learning algorithms and bioinformatics, this research identified three hub genes that could serve as novel targets for the diagnosis and therapy of UC, which may provide valuable insights into the mechanism of NETs in UC and potential related therapies.