Bioinformatics identification of phagosome-related genes and pathways with potential to be used as biomarkers of early detection of ALS

Background and Objectives: Amyotrophic lateral sclerosis (ALS) causes progressive and irreversible degeneration of motor neurons. Until now, mutations in genes such as SOD1, C9orf72, TBK1, UBQLN2 and TARDBP have been identified in these patients, but little is known about the sporadic type of the disease, which accounts for 90% of cases. Studies have shown that Riluzole and Edaravone only work in the early stages of the disease. Therefore, early detection of this disease and timely initiation of drug administration can increase the life expectancy of patients. CSF biopsy is aggressive and has side effects, so recent studies suggest that blood may be used as an alternative. In this study, we tried to present potential biomarkers in blood by bioinformatics.Methods: Two blood and brain related datasets of ALS patients were selected from GEO database. Enrichment of biomarkers was performed on the results of microarray analysis of the blood and brain of ALS patients and selection of key genes in the disease associated with known pathways. For this step, EnrichR and DAVID databases were used and KEGG database information was used to adapt the biological pathways. Finally, the network generated by these paths was constructed with Cytoscape software and the ClueGo app.Results: Two common pathogenesis pathways in blood and brain including Phagosome and Fc gamma R-mediated phagocytosis were identified that are predicted to be related to the dysfunction of proteins in ALS pathogenesis. FCGR2B was identified as one of the key common genes in these pathways.Conclusion: The results of this bioinformatics study suggest that genes related to autophagy pathway such as FCGR2B in peripheral blood mononuclear cells could be used as potential biomarkers to increase the accuracy of early detection of ALS, and increase the chances of the drugs being effective.

کلیدواژه ها:

Phagosome ، Diagnostic biomarker ، ALS ، bioinformatics