Zhang X-Y, Gu C-G, Gu J-W, Zhang J-H, Zhu H, Zhang Y-C, Cheng J-M, Li Y-M, Yang T
Department of Neurosurgery, ChengDu Military General Hospital, Chengdu, Sichuan, China.
Clinical Laboratory Department, 452 Hospital of PLA, Chengdu, Sichuan, China.
Genet Mol Res. 2014 Nov 7;13(4):9220-8. doi: 10.4238/2014.November.7.9.
Gene expression data acquired at different times after traumatic brain injury (TBI) were analyzed to identify differentially expressed genes (DEGs). Interaction network analysis and functional enrichment analysis were performed to extract valuable information, which may benefit diagnosis and treatment of TBI. Microarray data were downloaded from Gene Expression Omnibus and pre-treated with MATLAB. DEGs were screened out with the SAM method. Interaction networks of the DEGs were established, followed by module analysis and functional enrichment analysis to obtain insight into the molecular mechanisms. A total of 39 samples at six time points (30 min, 4, 8, 24 , 72 h, and 21 days) were analyzed and generated 377 DEGs. Eight modules were identified from the networks and network ontology analysis revealed that cell surface receptor-linked signaling pathway, response to wounding and signaling pathway were significantly overrepresented. Altered risk genes and modules in TBI were uncovered through comparing the gene expression data acquired at various time points. These genes or modules could be potential biomarkers for diagnosis and treatment of TBI.
分析创伤性脑损伤（TBI）后不同时间获取的基因表达数据，以鉴定差异表达基因（DEG）。进行相互作用网络分析和功能富集分析以提取有价值的信息，这可能有益于TBI的诊断和治疗。微阵列数据从基因表达综合数据库下载并用MATLAB进行预处理。用SAM方法筛选出DEG。建立DEG的相互作用网络，随后进行模块分析和功能富集分析以深入了解分子机制。分析了六个时间点（30分钟、4、8、24、72小时和21天）的总共39个样本，并产生了377个DEG。从网络中鉴定出八个模块，网络本体分析显示细胞表面受体连接信号通路、对伤口的反应和信号通路显著富集。通过比较在不同时间点获取的基因表达数据，揭示了TBI中改变的风险基因和模块。这些基因或模块可能是TBI诊断和治疗的潜在生物标志物。
