Department of Neurosurgery, ChangZheng Hospital, Second Military Medical University, Shanghai, China.
Brain Res Bull. 2011 Oct 10;86(3-4):189-94. doi: 10.1016/j.brainresbull.2011.07.010. Epub 2011 Jul 22.
The prognosis of glioblastoma (GBM) is poor. The therapeutic outcome of conventional surgical and adjuvant treatments remains unsatisfactory, and therefore individualized adjuvant chemotherapy has aroused more attention. Microarrays have been applied to study mechanism of GBM development and progression but it has difficulty in determining responsible genes from the plethora of genes on microarrays unrelated to outcome. The present study was attempted to use bioinformatics method to investigate candidate genes that may influence chemosensitivity of GBM to Semustine (Me-CCNU).
Clinical data of 4 GBM patients in Affymetrix 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. Me-CCNU-related signal transduction networks were constructed. The methods combined three steps before were used to screen core genes that influenced Me-CCNU chemosensitivity in GBM.
In Affymetrix microarray there were altogether 2018 differential expression genes that influenced survival duration of GBM. Of them, 934 genes were up-regulated and 1084 down-regulated. They mainly participated in 94 pathways. Me-CCNU-related signal transduction networks were constructed. The total number of genes in the networks was 466, of which 66 were also found in survival duration-related differential expression genes. Studied key genes through GO-analysis, pathway-analysis and in the Me-CCNU-related signal transduction networks, 25 core genes that influenced chemosensitivity of GBM to Me-CCNU were obtained, including TP53, MAP2K2, EP300, PRKCA, TNF, CCND1, AKT2, RBL1, CDC2, ID2, RAF1, CDKN2C, FGFR1, SP1, CDK6, IGFBP3, MDM4, PDGFD, SOCS2, CCNG2, CDK2, SDC2, STMN1, TCF7L1, TUBB.
Bioinformatics may help excavate and analyze large amounts of data in microarrays by means of rigorous experimental planning, scientific statistical analysis and collection of complete data about survival of GBM patients. In the present study, a novel differential gene expression pattern was constructed and advanced study will provide new targets for chemosensitivity of GBM.
胶质母细胞瘤（GBM）的预后较差。传统手术和辅助治疗的治疗效果仍不理想，因此个体化辅助化疗引起了更多关注。微阵列已被应用于研究 GBM 发生和发展的机制，但从与结果无关的微阵列上众多基因中确定负责基因具有一定难度。本研究试图使用生物信息学方法来研究可能影响亚硝脲（Me-CCNU）对 GBM 化疗敏感性的候选基因。
通过长期随访研究完善了 4 例 GBM 患者在 Affymetrix 微阵列中的临床数据。挑出长生存期和短生存期组之间的差异表达基因，对差异表达基因进行 GO 分析和通路分析。构建 Me-CCNU 相关信号转导网络。采用三步结合的方法筛选影响 GBM 中 Me-CCNU 化疗敏感性的核心基因。
在 Affymetrix 微阵列中，共有 2018 个差异表达基因影响 GBM 的生存时间。其中，上调基因 934 个，下调基因 1084 个。它们主要参与了 94 条通路。构建了 Me-CCNU 相关信号转导网络。网络中的基因总数为 466 个，其中也有 66 个在与生存时间相关的差异表达基因中发现。通过 GO 分析、通路分析和 Me-CCNU 相关信号转导网络研究关键基因，获得了 25 个影响 GBM 对 Me-CCNU 化疗敏感性的核心基因，包括 TP53、MAP2K2、EP300、PRKCA、TNF、CCND1、AKT2、RBL1、CDC2、ID2、RAF1、CDKN2C、FGFR1、SP1、CDK6、IGFBP3、MDM4、PDGFD、SOCS2、CCNG2、CDK2、SDC2、STMN1、TCF7L1、TUBB。
生物信息学可以通过严格的实验规划、科学的统计分析和收集完整的 GBM 患者生存数据，帮助挖掘和分析微阵列中的大量数据。在本研究中，构建了一种新的差异基因表达模式，进一步的研究将为 GBM 的化疗敏感性提供新的靶点。
