A bioinformatics approach to identify candidate biomarkers and common pathways between bipolar disorder and stroke

Bipolar disorder (BD) is a severe psychiatric disease that is associated with a variety of physical disorders that substantially contribute to morbidity and mortality. People with BD now face a higher risk of stroke in recent years. The goal of this work is to identify therapeutic candidates and associated pathways with the help of a number of bioinformatics techniques. GSE23848 and GSE58294 microarray datasets are utilized for BD and stroke samples. Datasets are preprocessed and screened by making using R language, and then concordant differentially expressed genes (DEGs) are found. The DEGs’ regulatory structure is illustrated with a venn diagram. After that, protein-protein interactions (PPIs) are constructed based on the concordant DEGs and the most active genes are discovered using topological assessment. CBL, LY96, TLR5, TRAF6, and TLR4 are the leading 5 hub genes in the PPI network. The pathways of KEGG showed that the concordant DEGs are associated with the Toll-like receptor signaling pathway and Hepatitis B. Gene ontology (GO), TF and miRNA analysis and module analysis network is considered the future work of this research. Finally, the assortment of drug compounds have been suggested based on the concordant DEGs.