Constructing and validating a diagnostic nomogram for multiple sclerosis via bioinformatic analysis.

Area of Science:

• Genomics and Bioinformatics
• Immunology
• Medical Diagnostics

Background:

• Multiple sclerosis (MS) diagnosis relies on clinical and imaging data, with a need for improved molecular biomarkers.
• Gene expression profiling offers a promising avenue for identifying diagnostic signatures in MS.
• Understanding immune cell dynamics is crucial in MS pathogenesis and diagnosis.

Purpose of the Study:

• To identify novel biomarkers for multiple sclerosis (MS).
• To construct and validate a diagnostic prediction model for MS using gene expression data.
• To investigate the role of immune cell infiltration in MS.

Main Methods:

• Downloaded and analyzed microarray datasets from the Gene Expression Omnibus (GEO).
• Employed Weighted Gene Coexpression Network Analysis (WGCNA) to identify hub genes and modules.
• Utilized Least Absolute Shrinkage and Selection Operator (LASSO) regression and multivariate logistic regression for biomarker selection and nomogram construction.
• Applied the CIBERSORT algorithm to quantify immune cell proportions.

Main Results:

• WGCNA identified a key module of 165 hub genes, predominantly involved in cell metabolism and immune cell activation.
• A diagnostic nomogram was constructed using six genes (ARPC5, ROD1, UBQLN2, ZNF281, ABCA1, FAS), demonstrating high predictive accuracy validated by ROC and calibration curves.
• Significant differences in monocyte and M0 macrophage proportions were observed in MS patients compared to healthy controls, with ARPC5, ZNF281, and ABCA1 expression correlating with M0 macrophages.

Conclusions:

• The developed diagnostic nomogram provides a novel tool for accurate MS diagnosis.
• The identified gene biomarkers and immune cell signatures offer insights into MS pathogenesis.
• This study contributes to advancing molecular diagnostic strategies for multiple sclerosis.