Screening core genes for minimal change disease based on bioinformatics and machine learning approaches

Area of Science:

• Genomics
• Bioinformatics
• Immunology

Background:

• Minimal change disease (MCD) is a significant cause of nephrotic syndrome, but its underlying molecular mechanisms remain incompletely understood.
• Identifying core genes and pathways is crucial for elucidating MCD pathogenesis and developing targeted therapies.

Purpose of the Study:

• To screen and identify core genes associated with minimal change disease (MCD) using bioinformatics and machine learning.
• To explore the pathogenesis of MCD by analyzing the functional roles of identified core genes.

Main Methods:

• Acquisition and analysis of gene expression datasets (GSE108113, GSE200828) from the Gene Expression Omnibus (GEO) using R software.
• Functional enrichment analysis, Cytoscape for core gene screening, and machine learning algorithms (random forest, LASSO regression) for gene identification.
• Validation through immunohistochemistry (IHC) and gene set enrichment analysis (GSEA).

Main Results:

• Identification of 394 highly expressed differential genes in MCD.
• Enrichment analysis revealed involvement in T cell differentiation and PI3K-Akt signaling pathway.
• NOTCH1, TP53, GATA3, and TGF-β1 were identified as core genes, with differential expression in MCD kidneys.
• GSEA indicated association with glycosaminoglycans signaling pathway in MCD pathogenesis.

Conclusions:

• NOTCH1, TP53, GATA3, and TGF-β1 are identified as core genes in minimal change disease (MCD).
• These genes are significantly associated with immune response pathways and MCD kidney pathology.
• The glycosaminoglycans signaling pathway plays a crucial role in the pathogenesis of MCD.