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.

中文翻译：

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整合性生物信息学分析，用于鉴定颅脑外伤后海马中的关键分子和通路。

高通量和生物信息学技术已被广泛应用于证明与创伤性脑损伤（TBI）有关的关键分子，而尚无研究整合了可用的TBI相关数据集进行分析。在这项研究中，分析了来自大鼠海马体的液压冲击损伤（FPI）和假样品的四个可用表达数据集。总共鉴定了248个差异表达基因（DEG）和10个差异表达microRNA（DEMI）。然后，使用基因本体论（GO）和《京都基因与基因组百科全书》（KEGG）途径分析进行功能注释。大部分DEG对术语“炎性免疫反应”而言是富集的。Cytoscape软件中的MCODE插件用于构建蛋白质间相互作用（PPI）网络，并证实了18个中枢基因在细胞周期途径中富集。此外，使用短时间序列表达挖掘器（STEM）进行了时间序列（3 h，6 h，12 h，24 h和48 h）概况分析。将显着表达的基因分配到具有四个显着上升趋势簇的24个模式簇中。发现四个DEG，Fcgr2a，Bcl2a1，Cxcl16和Gbp2，在所有时间点均差异表达。使用miRWalk3.0和Cytoscape，鉴定出53个DEG和8个DEMI形成miRNA-mRNA负调控网络。此外，通过qRT-PCR验证了八个毂基因的mRNA水平。这些DEG，DEMI，