Identification of Biomarker in Brain-specific Gene Regulatory Network Using Structural Controllability Analysis

Area of Science:

• Systems biology
• Neuroscience
• Bioinformatics

Background:

• Brain tumor research is vital for human health.
• Understanding brain activity relies on brain network research.
• Gene regulatory networks (GRNs) model complex biological processes.

Purpose of the Study:

• To apply structural controllability theory to human brain-specific GRNs.
• To identify critical genes within these networks and explore their biological significance.
• To analyze the robustness of GRNs in normal versus cancerous brain conditions.

Main Methods:

• Structural controllability theory applied to forebrain, hindbrain, and neuron-associated cancer GRNs.
• Calculation of eight topological properties (degree, betweenness, closeness, clustering coefficient) for gene nodes.
• Bioinformatic analyses including gene database enrichment, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis.
• Network robustness analysis through edge/routine deletion.

Main Results:

• Critical genes exhibit significantly higher topological property scores than ordinary genes.
• Critical genes are enriched in essential genes, cancer genes, and neuron-related disease genes, suggesting biomarker potential.
• Critical genes are implicated in cancer-related KEGG pathways, linking them to brain tumors.
• Neuron-associated cancer GRNs demonstrate greater robustness compared to normal brain GRNs.

Conclusions:

• Critical genes in brain GRNs are key players in neural function and disease.
• These critical genes may serve as biomarkers for brain tumors and neurological disorders.
• The increased robustness of cancer-related GRNs highlights network adaptivity in disease states.