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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。
OBJECTIVE: To screen bioinformatics techniques for genes involved in chemosensitivity of glioblastoma (GBM). Methods: Two groups of patients with glioma BIOSTAR gene chip analysis. Through follow-up and improvement of clinical data, screening chips in glioblastoma patients with long survival, the difference between the two short genes, clear differences in gene involved in the function and access, and construction and alkylating agent-related signal transduction network, combined Chip data, patient prognosis and signal transduction networks to screen for GBM chemosensitivity related genes. Results: There were 503 differential genes in the two groups of chips. Two batches of differentially expressed genes are involved in 62 gene functions, mainly involved in 31 signaling pathways. Through the analysis of differential gene function, pathway and alkylation signal transduction network, we obtained the key genes IFNGR2, IL8, ITGA5 and TNFRSF1B that affect the chemosensitivity of glioblastoma. Conclusion: Through rigorous experimental design and scientific statistical discrimination, combined with the patient’s complete survival data, this study successfully applied the bioinformatics techniques to excavate and analyze a large amount of data of the gene chip, and screened out the possible impact on the prognosis of GBM patients And chemosensitivity genes for further functional experiments and individual patients laid the foundation for treatment.