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Identification Of Senescence-related Genes For The Prediction Of Ulcerative Colitis Based On Interpretable Machine Learning Models.

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Identification Of Senescence-related Genes For The Prediction Of Ulcerative Colitis Based On Interpretable Machine Learning Models.

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

Jingjing Ma¹, Chen Chen^2,3, Nian Wang¹

¹Department of Geriatric, Renmin Hospital of Wuhan University, Wuhan, Hubei, 430060, People's Republic of China.

Journal of Inflammation Research

|March 17, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

Cellular senescence is linked to ulcerative colitis (UC). Researchers identified ABCB1 and LCN2 as key senescence-related genes, developing a diagnostic model for UC prediction.

Keywords:

biomarkers cellular senescence diagnostic model machine learning ulcerative colitis

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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.