Introduction: Amyotrophic Lateral Sclerosis (ALS) is a neurological disorder characterized by progressive motor neuron damage leading to muscle atrophy and various clinical manifestations. However, its underlying mechanisms are still unknown. The prognosis of the disease cannot be accurately determined because of the current lack of suitable biomarkers and targeted therapies. Materials and Methods: In this study, ALS-affected muscle tissue was analyzed using a bioinformatics approach based on the expression dataset GSE139384, which was obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified using the criteria of an adjusted p-value < 0.05 and an absolute log2 fold change (|log2FC|) greater than 2. Then, enrichment analysis was performed, and a protein-protein interaction network was constructed. Hub genes were identified using the CytoHubba computational algorithm in Cytoscape software. Results: Using the GSE13938479, 184 DEGs were identified. Among these DEGs, all of them had increased expression. The most important identified pathways were related to vesicle-mediated transport at the synapse, synaptic vesicle exocytosis, and regulation of neurotransmitter release. Protein-protein interaction network analysis identified 20 hub genes. Several upregulated core genes, including SNAP25, MAPT, SYP, SYN1, DLG4, and HSP90AB1, were identified as being associated with ALS. Conclusion: Using bioinformatics analysis, we identified novel potential biomarkers and disease-related therapeutic targets for patients with ALS. This could enhance our understanding of the molecular processes of this neurodegenerative disease.