MicroRNA-gene regulatory network of TLR signaling in neuroinflammation-induced Parkinson ’s disease: a bioinformatics approach

In this study, we used GO, a bioinformatics tool that uses the representations for genes in an organism; PPI, which shows the physical interac tion between proteins in an organism; and miRNet, a tool to navigate the complex relationships between miRNAs and their targets for deeper biologic understanding. To find out the potential TLR genes and regulatory miRNAs that play a role in neuroinflammation-induced PD. We acquired the gene expressi on profile, GSE26927, from the GEO Omnibus. DAVID bioinformatics and SHINY GO software were employed for GO analysis of DEGs, and the fold enrichment score for each pathway was verified. TheTLR signaling pathways most deregulated genes (upregulated: log FC  ≥ 2.0, downregulated: log FC ≤ – 2.0) were chosen for network analysis to identify crucial or hub genes. Subsequently, a miRNA-gene network was constructed using the miRNet tool. The foremost TLR signaling gene, distinguishing between PD and control samples, has been discerned. In th e Protein–Protein Interaction (PPI) network, we identified genes with heightened connectivity, notablyTLR4, exhibiting the highest degree of betweenness (degree  = 22) in the TLR signaling pathway. Furthermore, in the miRNA-gene network, we unveiled the preeminent five miRNAs: hsa-miR-21-5p, hsa-miR-17-5p, hsa-miR-93-5p, hsa-miR-7-5p, and hsa-mir-92b-3p that interacted with the TLR signaling gene. The top tenTLR genes could be potential targets for new therapeutics. In addition, the iden...
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - Category: Bioinformatics Source Type: research