A framework for cancer-related genes mining over the Internet

J. Tsai, Jan-Gowth Chang, S. H. Shih, Rong-Ming Chen, H. Hsiao, Rouh-Mei Hu, S. N. Chen, M. M. Lee, Falcon F. M. Liu, Wen-Ling Chan
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引用次数: 2

Abstract

Clinically, cancer is a complex family of diseases. From the view of molecular biology, cancer is a genetic disease resulting from abnormal gene expression. This alternation of gene expression could be resulting from DNA instability, such as translocation, amplification, deletion or point mutations. A large amplification or deletion of a chromosome region can be easily detected by two methods: loss of heterozygosity (LOH) and comparative genomic hybridization (CGH). The different gene expression pattern can be monitored by high throughput microarray analysis. Enormous data accumulated by practicing these technologies and the data pool is continuing enlarging with an amazing rate. To aid investigators mining useful information in these data deposits, new data storing and analysis tools must be developed. Two value-added databases are constructed to achieve this purpose. They contain information of genes in the unstable regions of cancer cells basing on the data accumulated from LOH and CGH experiments and information of cancer cell gene expression profiles according to microarray analysis, respectively. An automatic system to retrieve interesting gene information, to compare with the known databases, to analyze and predict the protein functions, and to group the genes of the same function will be integrated into the database circuit. An automatic update system will be installed and performed after the setup of the two databases. The system keeps also the probability to modify and to accept new data obtained from any new techniques. Our goal is to help biologists to find the needles in a haystack that is, to find the real cancer-related genes (oncogenes or tumor suppressor genes) for further research purpose.
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互联网上癌症相关基因挖掘的框架
在临床上,癌症是一个复杂的疾病家族。从分子生物学的角度看,癌症是一种由基因表达异常引起的遗传性疾病。这种基因表达的改变可能是由DNA不稳定引起的,如易位、扩增、缺失或点突变。有两种方法可以很容易地检测到染色体区域的大量扩增或缺失:杂合性缺失(LOH)和比较基因组杂交(CGH)。不同的基因表达模式可以通过高通量微阵列分析来监测。通过实践这些技术积累了大量的数据,并且数据池正在以惊人的速度继续扩大。为了帮助研究人员从这些数据中挖掘有用的信息,必须开发新的数据存储和分析工具。为此构建了两个增值数据库。它们分别包含了基于LOH和CGH实验积累数据的癌细胞不稳定区域的基因信息和基于微阵列分析的癌细胞基因表达谱信息。数据库电路将集成一个自动检索感兴趣的基因信息,与已知数据库进行比较,分析和预测蛋白质功能,以及对相同功能的基因进行分组的系统。安装两个数据库后,将安装并执行自动更新系统。该系统还保留了修改和接受从任何新技术获得的新数据的可能性。我们的目标是帮助生物学家大海捞针,即找到真正的癌症相关基因(致癌基因或肿瘤抑制基因),为进一步的研究目的。
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