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使用聚类分析来分析阿尔茨海默病的微阵列数据,以识别生物标志物基因。

Analyzing microarray data of Alzheimer's using cluster analysis to identify the biomarker genes.

作者信息

Guttula Satya Vani, Allam Apparao, Gumpeny R Sridhar

机构信息

Department of Biotechnology, Al-Ameer College of Engineering & IT, Andhra Pradesh, Visakhapatnam 531173, India.

出版信息

Int J Alzheimers Dis. 2012;2012:649456. doi: 10.1155/2012/649456. Epub 2012 Feb 14.

Abstract

Alzheimer is characterized by the presence of senile plaques and neurofibrillary tangles in cortical regions of the brain. The experimental data is taken from Gene Expression Omnibus. A hierarchical Cluster analysis and TreeView were performed to group genes on the basis of the expression pattern. The dynamic change of expression over time and diverse patterns of expression support the concept of a complex local milieu. TreeView allows the organized data to be visualized. List of 24 genes were obtained which showed high expression levels. Three genes, SORL1, APP, and APOE, are suspected to cause Alzheimer's whereas the other 21 genes are related to other diseases but may also be found to be associated with Alzheimer's, and these are TMEM59, CCT4, IGF2R, SFPQ, PRDX3, RNF14, IDS, SSBP1, SYNE2, TXNL4A, STXBP3, SMARCB1, ULK2, AGTPBP1, FABP7, CALB1, H2AFY, COPA, SAP18, ATIC and SYNCRIP.

摘要

阿尔茨海默病的特征是大脑皮质区域出现老年斑和神经原纤维缠结。实验数据取自基因表达综合数据库。基于表达模式进行了层次聚类分析和使用TreeView软件对基因进行分组。随着时间推移表达的动态变化以及多样的表达模式支持了复杂局部环境的概念。TreeView软件能将整理好的数据可视化。获得了24个显示高表达水平的基因列表。三个基因,即SORL1、APP和APOE,被怀疑会引发阿尔茨海默病,而其他21个基因与其他疾病相关,但也可能被发现与阿尔茨海默病有关,它们分别是TMEM59、CCT4、IGF2R、SFPQ、PRDX3、RNF14、IDS、SSBP1、SYNE2、TXNL4A、STXBP3、SMARCB1、ULK2、AGTPBP1、FABP7、CALB1、H2AFY、COPA、SAP18、ATIC和SYNCRIP。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5b3c/3296213/0fa4e1a9965f/IJAD2012-649456.001.jpg

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