Network Analysis to Identify Key Genes and Drug Targets for Amyotrophic Lateral Sclerosis using in silico Approaches

Objectives: The current study describes a comprehensive analysis using bioinformatics to identify the key genes and molecular pathways associated with Amyotrophic lateral sclerosis (ALS), and to evaluate potential therapeutic targets for disease management. Methods: ALS-associated genes were identified using GeneCodis, and text mining was performed. Pathways linked to ALS were found using KEGG. A protein-protein interaction (PPI) network for the Target Multi-Gene (TMG) set was created in Cytoscape and STRING. MCODE was utilized to group key modules. The DAVID tool was used to discover gene ontology and conduct KEGG analysis on key genes. ClueGO and ShinyGO illustrated the roles and operations of clustering gene keys within a large module. The DGIdb assessed drug interactions between key genes to uncover viable ALS therapeutics. Findings: A total of 47 genes linked to ALS were identified. The gene functional enrichment study revealed 41 elevated gene GO terms, as well as 12 pathways from KEGG. A single gene unit was constructed using the MCODE-based PPI network structure, featuring 18 nodes and 146 edges. We identified eight FDA-approved drugs that could target seven of the 21 key genes, which may provide a theoretical basis for advanced personalized therapy. Novelty: This study revealed a total of 21 key genes, 12 pathways, and 8 drug candidates that could provide the basis of novel targeted therapies for targeted treatment and management of ALS. Graphical Abstract: Schematic representation of evaluating and identifying key genes, molecular pathways, and drug targets Keywords: Amyotrophic lateral sclerosis, Pathways, Protein-protein interaction, Drug-gene interaction, Key genes