Identifying Diagnostic Markers and Constructing Predictive Models for Oxidative Stress in Multiple Sclerosis.

Area of Science:

• Neuroimmunology
• Genomics
• Computational Biology

Background:

• Multiple sclerosis (MS) involves central nervous system inflammation and neurodegeneration, with oxidative stress playing a key role in its pathogenesis.
• The precise molecular mechanisms of oxidative stress in MS remain incompletely understood, necessitating further investigation into relevant genes and pathways.

Purpose of the Study:

• To identify differentially expressed oxidative-stress-related genes (DE-OSRGs) associated with multiple sclerosis (MS).
• To develop a predictive diagnostic model for MS based on identified key genes.
• To explore potential therapeutic targets by analyzing drug-gene interactions.

Main Methods:

• Utilized microarray datasets from the GEO database to identify 101 DE-OSRGs.
• Performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses.
• Employed protein-protein interaction (PPI) networks, LASSO, and logistic regression to identify key genes (MMP9, NFKBIA, NFKB1, SRC).
• Conducted immune-cell infiltration analysis (CIBERSORT) and molecular docking studies.

Main Results:

• Identified 101 DE-OSRGs, primarily involved in oxidative stress and immune responses.
• Four key genes (MMP9, NFKBIA, NFKB1, SRC) were strongly associated with MS.
• Developed a logistic regression model with good predictive power for MS diagnosis.
• Observed abnormal oxidative stress and gene upregulation in EAE mouse models, with potential drug interactions identified.

Conclusions:

• Identified and validated potential diagnostic biomarkers for MS, offering new avenues for early detection.
• Established an effective prediction model for MS, aiding in clinical assessment.
• Provided insights into the role of oxidative stress and identified potential therapeutic targets for personalized MS treatment.