Identification of shared oxidative stress related hub genes in NAFLD and atherosclerosis using bioinformatics and machine learning

2026-01-05 · Source: Machine learning : nature.com subject feeds · Field: Science & Research — Health & Medical Research, Mathematics & Computational Sciences · Depth: Advanced, long

Summary

A study identified shared oxidative stress-related hub genes and mechanisms between Non-alcoholic fatty liver disease (NAFLD) and atherosclerosis (AS) using bioinformatics and machine learning. Researchers retrieved gene expression profiles for NAFLD (GSE89632) and AS (GSE100927) from the Gene Expression Omnibus (GEO) database and oxidative stress-related genes from GeneCards. Differential expression analysis revealed 20 co-expressed oxidative stress-related genes (8 upregulated, 12 downregulated) common to both diseases. Functional enrichment analysis linked these genes to the MAPK and FoxO signaling pathways. Machine learning methods pinpointed ADM and IL2RB as key hub genes with significant diagnostic potential, showing AUC values of 0.787 and 0.887 for AS, and 0.979 and 0.931 for NAFLD. A nomogram model incorporating these two hub genes achieved excellent predictive accuracy, with AUC values exceeding 0.9 for both conditions.

Key takeaway

For AI scientists and clinical researchers investigating metabolic and cardiovascular diseases, this study highlights ADM and IL2RB as promising diagnostic biomarkers for NAFLD and atherosclerosis. You should consider these genes for further validation in clinical cohorts and explore their roles in the MAPK and FoxO signaling pathways to develop targeted therapeutic strategies. The nomogram model's high predictive accuracy suggests a robust framework for future diagnostic tool development.

Key insights

Bioinformatics and machine learning can identify shared oxidative stress-related hub genes in NAFLD and atherosclerosis.

Principles

• Oxidative stress links NAFLD and atherosclerosis.
• MAPK and FoxO pathways are key shared mechanisms.

Method

Gene expression profiles from GEO were analyzed for differentially expressed oxidative stress genes. Machine learning then screened for hub genes, and a nomogram model assessed diagnostic potential.

In practice

• ADM and IL2RB are potential diagnostic biomarkers.
• Targeting MAPK/FoxO pathways may offer therapeutic avenues.

Topics

• Non-alcoholic Fatty Liver Disease
• Atherosclerosis
• Oxidative Stress
• Bioinformatics
• Machine Learning

Best for: AI Scientist, AI Researcher, Research Scientist, AI Data Scientist

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