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

发布时间：2018-05-07 04:11

本文选题：肌萎缩侧索硬化 + 差异表达　；参考：《华中科技大学学报(医学版)》2016年03期

【摘要】：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。
[Abstract]:Objective to reveal the pathogenesis of amyotrophic lateral sclerosis (ALS) at molecular level and to provide a new tool for clinical diagnosis and treatment. Methods the data of ALS patients were retrieved in GEO, and the bioinformatics tools, such as BRB-Array tools, BRB-Array tools, GOEAST, TopPGENE and so on, were used for integrative analysis. Results six common differentially expressed genes were found in GSE56808 and GSE26276 samples, and cluster was carried out at sample level. The function enrichment was mainly focused on oxidative stress, calcium metabolism disorder, inflammatory reaction, angiogenesis and mitochondrial metabolism. Other neurodegenerative diseases include PI3 K / AKT pathway, P38 MAPK pathway, NOTCH pathway and other modules. An optimal classifier consisting of six characteristic genes was constructed by using a variety of classification and prediction tools, which can be used to distinguish ALS patients from healthy controls. Conclusion various bioinformatics methods are used to define the expression characteristics of molecular pathogenesis in patients with ALS from different angles, which provides a basis for further biological exploration.
【作者单位】：华中科技大学同济医学院附属同济医院神经内科;华中科技大学同济医学院附属同济医院血液内科;
【基金】：国家自然科学基金青年基金资助项目(No.81400122)
【分类号】：R744.8

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