基于生物信息学技术筛选影响胶质母细胞瘤化疗敏感性相关基因的研究

目的：应用生物信息学技术筛选影响胶质母细胞瘤（GBM）化疗敏感性的相关基因。方法：对2批胶质瘤患者BIOSTAR基因芯片进行分析。通过随访完善临床资料，筛选芯片中胶质母细胞瘤患者生存期长、短两组间的差异基因，明确差异基因参与的功能和通路，并构建与烷化剂相关基因的信号传导网络，结合芯片数据、患者预后和信号传导网络，筛选GBM化疗敏感性的相关基因。结果：两组芯片中间差异基因有503条。2批芯片的差异基因主要参与62项基因功能，主要参与31条信号传导通路。通过对差异基因功能、通路，烷化剂信号转导网络的分析，得到影响胶质母细胞瘤化疗敏感性的核心的差异基因IFNGR2、IL8、ITGA5、TNFRSF1B。结论：通过严谨的实验设计和科学的统计学判别，结合患者完整的生存资料，本研究成功地应用生物信息学技术对基因芯片的大量数据进行挖掘和分析，并筛选出了可能影响GBM患者预后和化疗药物敏感性的基因，为进一步功能实验和患者个体化治疗奠定了基础。

Using Bioinformatics Method to Investigate the Genes Related to Chemosensitivity in Human Glioblastoma

Objective： Bioinformatics method was used to analyze data of eDNA microarray to investigate the genes related to survival time and drug sensitivity in GBM. Methods： Biostar microarray of GBM patients were analyzed, and clinical data of these patients in the microarray were perfected through long-term follow-up study. Differential expression genes between the long- and short- survival groups were picked out, GO-analysis and pathway-analysis of the differential expression genes were performed. Drug-related signal transduction networks were constructed. The methods combined three steps before were used to screen core genes that influenced chemosensitivity. Results： There were 503 differentiate genes that influenced survival duration of GBM respectively. They mainly participated in 62 gene functions and 31 signaling transduction pathways, based on which 4 core genes that influenced chemosensitivity of GBM to Chemistry drug were obtained, including IFNGR2, ILS, ITGA5, TNFRgF1B. Conclusion： With complete clinical information, mass data of cDNA microaray can be further analyzed by using bioinformatics method. These genes were found to predict the clinical outcomes and have a significant influence on chemosensitivity in GBM. This study will provide a foundation not only for the further functional research but also for prediction of prognosis and fulfillment of personalization chemotherapy in GBM.