Persson Oscar, Krogh Morten, Saal Lao H, Englund Elisabet, Liu Jian, Parsons Ramon, Mandahl Nils, Borg Ake, Widegren Bengt, Salford Leif G
Department of Neurosurgery, The Rausing Laboratory, Lund University, 22100 Lund, Sweden.
J Neurooncol. 2007 Oct;85(1):11-24. doi: 10.1007/s11060-007-9383-6. Epub 2007 Jul 17.
Gliomas are among the most aggressive malignant tumors and the most refractory to therapy, in part due to the propensity for malignant cells to disseminate diffusely throughout the brain. Here, we have used 27 K cDNA microarrays to investigate global gene expression changes between normal brain and high-grade glioma (glioblastoma multiforme) to try and better understand gliomagenesis and to identify new therapeutic targets. We have also included smaller groups of grade II and grade III tumors of mixed astrocytic and oligodendroglial origin as comparison. We found that the expression of hundreds of genes was significantly correlated to each group, and employed a naïve Bayesian classifier with leave-one-out cross-validation to accurately classify the samples. We developed a novel algorithm to analyze the gene expression data from the perspective of chromosomal position, and identified distinct regions of the genome that displayed coordinated expression patterns that correlated significantly to tumor grade. The regions identified corresponded to previously known genetic copy number changes in glioma (e.g. 10q23, 10q25, 7q, 7p) as well as regions not previously associated significantly with glioma (e.g. 1p13, 6p22). Furthermore, to enrich for more suitable targets for therapy, we took a bioinformatics approach and annotated our signatures with two published datasets that identified membrane/secreted genes from cytosolic genes. The resulting focused list of 31 genes included interesting novel potential targets as well as several proteins already being investigated for immunotherapy (e.g. CD44 and tenascin-C). Software for the chromosome analysis was developed and is freely available at http://base.thep.lu.se.
神经胶质瘤是最具侵袭性的恶性肿瘤之一，也是最难治疗的肿瘤之一，部分原因是恶性细胞倾向于在整个大脑中弥漫性扩散。在此，我们使用27K cDNA微阵列来研究正常脑与高级别神经胶质瘤（多形性胶质母细胞瘤）之间的全局基因表达变化，以试图更好地理解神经胶质瘤的发生机制并确定新的治疗靶点。我们还纳入了较小的混合星形细胞和少突胶质细胞起源的II级和III级肿瘤组作为对照。我们发现数百个基因的表达与每组显著相关，并采用留一法交叉验证的朴素贝叶斯分类器对样本进行准确分类。我们开发了一种新颖的算法，从染色体位置的角度分析基因表达数据，并确定了基因组中显示出与肿瘤分级显著相关的协调表达模式的不同区域。所确定的区域对应于神经胶质瘤中先前已知的基因拷贝数变化（例如10q23、10q25、7q、7p）以及先前与神经胶质瘤无显著关联的区域（例如1p13、6p22）。此外，为了富集更合适的治疗靶点，我们采用生物信息学方法，用两个已发表的数据集对我们的特征进行注释，这两个数据集从胞质基因中识别出膜/分泌基因。由此产生的31个基因的重点列表包括有趣的新型潜在靶点以及几种已经在进行免疫治疗研究的蛋白质（例如CD44和腱生蛋白-C）。开发了用于染色体分析的软件，可从http://base.thep.lu.se免费获取。
