Objective
To investigate the interactions between major depressive disorder(MDD) and Alzheimer's disease(AD) through bioinformatics to detect biomarkers that contribute to the onset and progression of MDD and AD, so as to allow for immediate intervention and treatment.
Methods
MDD dataset GSE98793 and AD dataset GSE63060 were obtained from the Gene Expression Omnibus(GEO) database. Identification of common differential genes(DEGs) in both datasets, followed by GO and Pathway analysis, then constructing protein-protein interaction(PPI) networks, identifying hub genes and validating with the GSE63061 dataset. TF-gene and gene-miRNA interactions networks were then constructed and potential therapeutic agents were identified.
Results
Totally 31 common DEGs were identified. GO analysis revealed that these DEGs were enriched in cytoplasmic translation, fructose-2,6-bisphosphate 2-phosphatase activity, tertiary granule lumen. Additionally, Pathway analysis enriched in the Cytoplasmic Ribosomal Proteins, Ribosome, Viral mRNA Translation and TSP-1 Induced Apoptosis in Microvascular Endothelial Cell. By structuring PPI network, 10 hub genes were identified, and 9(RPS3A, RPS15A, RPL9, NDUFA4, RPS17, CD3D, GZMA, S100A12, KLRB1) were validated. Through the NetworkAnalyst platform, TFs(GTF2E2, FOXJ2, CREB3L1, TFDP1, SAP30), miRNAs(mir-16-5p, mir-1-3p, mir-124-3p, mir-7-5p, mir-146a-5p) and chemicals(Aflatoxin B, Benzo(a)pyrene, Estradiol, Valproic Acid, Nickel) interacting with common DEGs were identified. Through Enrichr platform, drugs including aspirin, medroxyprogesterone acetate, p-Phenylenediamine, COBALT, sodium dodecyl sulfate were identified. Additionally, totally 53 effective drugs were identified through the Drug-Gene Interaction Database.
Conclusion
Overall, these hub genes, TFs, and miRNAs may represent potential diagnostic and therapeutic targets for MDD and AD, and these agents may provide fresh insights and alternatives for the treatment of MDD and AD.