基于高通量芯片和生物信息学探索肌萎缩侧索硬化发病相关基因

Analysis of Amyotrophic Lateral Sclerosis Associated Genes Based on High-throughput Microarray and Bioinformatics

目的从分子水平揭示肌萎缩侧索硬化(ALS)的发病机制,为临床诊疗提供新工具。方法在GEO中检索ALS患者芯片数据,使用BRB-Array Tools、GSEA、GOEAST、TOPPGENE等生物信息学工具进行统合分析。结果对GSE56808和GSE26276两个样本集进行数据挖掘,发现6个共同差异表达基因,并进行样本层次聚类,功能富集主要集中在氧化应激、钙代谢障碍、炎症反应、血管生成、线粒体代谢、其它神经系统退行性疾病、PI3K/AKT通路、P38MAPK通路、NOTCH通路等模块上。利用多种分类预测工具构建出一个包含6个特征基因的最优化分类器,基本可用于区分ALS患者和健康对照组。结论利用多种生物信息学方法从不同的角度定义了ALS患者分子发病机制的表达特征,为进一步的生物学探索提供了依据。

Objective To explore the molecular pathogenesis of amyotrophic lateral sclerosis（ALS）,and provide novel tools for clinical diagnosis and treatment of ALS.Methods Gene expression profiles were obtained from GEO database.A set of bioinformatics tools,such as BRB-Array Tools,GSEA,GOEAST,TOPPGENE,were used to accomplish the data min-ing.Results By combining the results of two independent samples GSE56808 ＆amp; GSE26276,six common differentially ex-pressed genes were identified,which were used to generate hierarchical clustering.Network and functional enrichment showed that ALS related genes were closely associated with oxidative stress,calcium metabolism disorders,inflammation,angiogenesis, mitochondrial metabolism,other neurodegenerative disorders and etc.They played essential roles in some important signal path-ways such as PI3K/Akt,P38 MAPK,NOTCH,etc.The optimal six-gene classifier constructed by multiple prediction tools for classification could differentiate the ALS patients from healthy control subj ects.Conclusion Data Mining and Bioinformatics a-nalysis can help to investigate the molecular pathogenesis of ALS in various perspectives,which provides the basis for further biological investigations on ALS.