Identification of Senescence-Related Genes for the Prediction of Ulcerative Colitis Based on Interpretable Machine...

Area of Science:

• Gerontology and Gastroenterology
• Molecular Biology and Genetics
• Bioinformatics and Computational Biology

Background:

• Cellular senescence, a key aging process, significantly contributes to ulcerative colitis (UC) pathology.
• The specific role of senescence-related genes in UC pathogenesis is not well understood.
• This study aims to elucidate the impact of cellular senescence on UC and identify potential diagnostic biomarkers.

Purpose of the Study:

• To identify key senescence-related genes associated with ulcerative colitis (UC).
• To develop and validate a diagnostic model for UC based on senescence-related genes.
• To explore the clinical utility of these genes as biomarkers for UC.

Main Methods:

• Utilized bioinformatics techniques on Gene Expression Omnibus (GEO) data to identify senescence-related differentially expressed genes (sene-DEGs) in UC patients.
• Performed functional enrichment, immune infiltration, and machine learning analyses to identify hub genes and develop predictive models.
• Validated gene expression and diagnostic model performance using independent datasets and human specimens, including ROC analysis.

Main Results:

• Identified 14 senescence-related differential genes between UC patients and healthy controls, enabling molecular subtyping of UC.
• ABCB1 and LCN2 were identified as central hub genes with significant diagnostic value, validated across multiple datasets and human samples.
• A nomogram model incorporating feature genes demonstrated excellent predictive capability for UC, correlating with immune cell profiles.

Conclusions:

• ABCB1 and LCN2 are significant biomarkers associated with cellular senescence in UC.
• These findings deepen the understanding of cellular senescence in UC pathogenesis.
• The identified genes show potential as valuable diagnostic biomarkers for ulcerative colitis.