Integrated Bioinformatics Analysis for the Identification of Key Molecules and Pathways in the Hippocampus of Rats After Traumatic Brain Injury

Abstract

High-throughput and bioinformatics technology have been broadly applied to demonstrate the key molecules involved in traumatic brain injury (TBI), while no study has integrated the available TBI-related datasets for analysis. In this study, four available expression datasets of fluid percussion injury (FPI) and sham samples from the hippocampus of rats were analysed. A total of 248 differentially expressed genes (DEGs) and 10 differentially expressed microRNAs (DEMIs) were identified. Then, functional annotation was performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. Most of the DEGs were enriched for the term inflammatory immune response. The MCODE plug-in in the Cytoscape software was applied to build a protein-protein interaction (PPI) network, and 18 hub genes were demonstrated to be enriched in the cell cycle pathway. Besides, time sequence (3 h, 6 h, 12 h, 24 h, and 48 h) profile analysis was performed using short time-series expression miner (STEM). The significantly expressed genes were assigned into 24 pattern clusters with four significant uptrend clusters. Four DEGs, Fcgr2a, Bcl2a1, Cxcl16, and Gbp2, were found to be differentially expressed at all time-points. Fifty-three DEGs and eight DEMIs were identified to form a miRNA-mRNA negative regulatory network using miRWalk3.0 and Cytoscape. Moreover, the mRNA levels of eight hub genes were validated by qRT-PCR. These DEGs, DEMIs, and time-dependent expression patterns facilitate our knowledge of the molecular mechanisms underlying the process of TBI in the hippocampus of rats and have the potential to improve the diagnosis and treatment of TBI.

Keywords: Bioinformatics analysis; DEGs; DEMIs; Time dependent expression pattern; Traumatic brain injury.