Su Yanlin, Xiong Jie, Bing Zhitong, Zeng Xiaomin, Zhang Yong, Fu Xiaohua, Peng Xiaoning
Department of Internal Medicine, College of Medicine, Hunan Normal University, Changsha, Hunan, China.
Institute of Modern Physics of Chinese Academy of Sciences, Lanzhou, Gansu, China.
Cancer Biomark. 2013;13(5):367-75. doi: 10.3233/CBM-130367.
Glioblastoma multiforme (GBM) remains the most common and aggressive primary brain tumor in adults with a poor median survival, and molecular biomarkers for GBM pathogenesis are in need.
The objective of this study is to identify potential novel genes for GBM pathogenesis by gene expression data mining.
Available SAGE libraries of GBM, astrocytoma, and normal brain tissues were collected from the Cancer Genome Anatomy Project (CGAP). Significance analysis for microarray (SAM) and CGAP-SAGE-Genie-DGED were used to identify differentially expressed tags, and specific tags that were differentially expressed only in GBM were further selected. Tags to genes association was performed by CGAP-SAGE-Genie-SAV. Immunohistochemistry was used to investigate distribution and validate expression of the interested gene.
Three genes were significantly differentially expressed just in brain. up-regulated expression of STAB1 and down-regulated expression of SH3GL2 and DNM3. Immunohistochemistry assay indicated that STAB1 mainly expressed in vascular endothelial cells and over-expressed in GBM samples compared to normal samples.
Our study shows that data mining of public sources of gene expression is an effective way to identify novel tumor-associated genes, and this work may contribute to the identification of candidate genes for GBM angiogenesis.
多形性胶质母细胞瘤（GBM）仍是成人中最常见且侵袭性最强的原发性脑肿瘤，其生存中位数较差，因此需要GBM发病机制的分子生物标志物。
本研究的目的是通过基因表达数据挖掘来识别GBM发病机制中潜在的新基因。
从癌症基因组解剖计划（CGAP）收集了GBM、星形细胞瘤和正常脑组织的可用SAGE文库。使用微阵列显著性分析（SAM）和CGAP-SAGE-Genie-DGED来识别差异表达标签，并进一步选择仅在GBM中差异表达的特定标签。通过CGAP-SAGE-Genie-SAV进行标签与基因的关联。采用免疫组织化学研究感兴趣基因的分布并验证其表达。
三个基因仅在脑中显著差异表达。STAB1表达上调，SH3GL2和DNM3表达下调。免疫组织化学分析表明，STAB1主要表达于血管内皮细胞，与正常样本相比，在GBM样本中过度表达。
我们的研究表明，对公共基因表达来源进行数据挖掘是识别新的肿瘤相关基因的有效方法，这项工作可能有助于识别GBM血管生成的候选基因。
