通过整合基因表达数据的双聚类分析鉴定多种脑部疾病共表达基因模块

Kihoon Cha, Kimin Oh, Taeho Hwang, G. Yi
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引用次数: 4

摘要

据报道,几种脑疾病在临床水平上可能具有相同的症状,这表明开发治疗方法的必要性和可能性。在本文中,我们对神经退行性疾病和精神疾病的几个微阵列数据集进行了整合基因表达分析,以发现其分子基础的独特性和共性。首先,我们选择并组合了三组包括八种脑部疾病的微阵列数据。其次,我们应用了一种基于相关性的双聚类方法BICLIC[1],以有效地识别在个体或多种脑部疾病组合中相关的共表达基因模块。第三,进行基于基因本体的功能富集分析,分析鉴定出的跨疾病或疾病特异性模块的功能特征。在这种方法中,我们可以在单一和多种疾病中检测各种相关基因。结果,总共有4307个共表达的基因模块被证明是两种或两种以上脑部疾病的共同基因。其中,共有16个基因的不同组合的8个模块与7种以上的脑部疾病相关。功能分析表明,与单一疾病特定模块相比,多疾病特定模块与认知功能等高级脑功能的关联更大。本研究的结果为进一步研究影响脑疾病的关键分子提供了宝贵的资源,无论是在transnoology还是疾病特异性方面。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Identification of Coexpressed Gene Modules across Multiple Brain Diseases by a Biclustering Analysis on Integrated Gene Expression Data
It has been reported that several brain diseases could share symptoms at clinical level, suggesting the necessity and possibility to develop therapeutics. In this paper, we carried out an integrated gene expression analysis on several microarray datasets of neurodegenerative diseases and psychiatric disorders to discover the uniqueness and commonness in their molecular basis. First, we selected and combined three sets of microarray data including eight brain diseases. Second, we applied a correlation-based biclustering approach, BICLIC [1], to efficiently identify coexpressed gene modules that are correlated in individual or multiple combinations of brain diseases. Third, Gene ontology-based functional enrichment analysis is performed to analyze functional characteristics of the identified cross-disease or and disease-specific modules. In this approach, we could examine various sets of correlated genes significantly in both single and multiple diseases. As a result, in total, 4,307 coexpressed gene modules were turned out to be common to two or more of brain diseases. Among them, eight modules having different combinations of total 16 genes were involved correlatively in more than seven brain diseases. The functional analysis showed that the multi-disease specific modules were more associated to higher brain functions like cognitive functions than single disease specific modules. The results in this study provide valuable resources to further investigate the key molecular players affecting on brain diseases in both transnosological or disease specific manner.
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