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Identifying disease feature genes based on cellular localized gene functional modules and regulation networks

ISSN号：1673-6273
期刊名称：《现代生物医学进展》
时间：0
分类：Q78[生物学—分子生物学]
作者机构：[1]Department of Bioinformatics, Harbin Medical University, Harbin 150086, China, [2]School of Biology Science and Technology, Tongji University, Shanghai 200092, China, [3]Departments of Molecular Cardiology and Cardiovascular Medicine, the Cleveland Clinic Foundation, 9500 Euclid Avenue, Cleveland, Ohio 44195, USA
相关基金：Acknowledgements This work was supported in part by the National Natural Science Foundation of China （Grant Nos. 30170515, 30370388, 30370798, 30570424 ＆ 30571034）, the National High Tech Development Project of China （Grant Nos. 2003AA2Z2051 ＆ 2002AA2Z2052）, Heilongjiang Science ＆ Technology Key Project （Grant No. GB03C602-4）, Harbin （City） Science ＆ Technology Key Project （Grant No. 2003AA3CS 113）, the Natural Science Foundation of Heilongjiang Province （Grant No. F0177）, and 0utstanding 0verseas Scientist Foundation of Education Department of Heilongjiang Province （Grant No. 1055HG009）.

作者： ZHANG Min[1], ZHU Jing[1], GUO Zheng[1,2], LI Xia[1,2], YANG Da[1], WANG Lei[1], RAO Shaoqi[1,3]

关键词：基因表达, 基因序列, 特征基因, 基因复制, gene expression profile, Gene Ontology, functional mod- ule, feature gene, gene regulation network

中文摘要：

识别疾病相关的基因和功能的模块，基于基因表示侧面和基因功能的知识，具有为学习疾病机制和 sub-typing 疾病显型的高重要性。用在基因本体论的生物过程和细胞的部件的基因范畴，我们建议选择与差别充实的功能的模块的一条途径表示了基因，并且识别特征高疾病区别的功能的模块能力。使用差别在每个特征模块中表示了基因为特征基因，我们揭示模块的关联到学习疾病。用三 data-setsfor 前列腺癌症，胃的癌症，和白血病，我们证明了建议模块化的途径具有在识别与疾病机制高度相关的机能上地综合的特征基因子集的高力量。我们的分析也证明了 criticaldisease 相关的基因可能更好从基因规定网络被认出，它用描绘的功能的模块被构造，把重要线索给对复杂疾病状态作出回应的模块的一致机制。另外，由联合认为基因是功能的知识选择疾病相关的基因的建议途径为精确与清楚的生物解释分类疾病样品建议一个新方法，它为临床的诊断和复杂疾病的病原的基础的说明是批评的。

英文摘要：

Identifying disease-relevant genes and functional modules, based on gene expression profiles and gene functional knowledge, is of high importance for studying disease mechanisms and subtyping disease phenotypes. Using gene categories of biological process and cellular component in Gene Ontology, we propose an approach to selecting functional modules enriched with differentially expressed genes, and identifying the feature functional modules of high disease discriminating abilities. Using the differentially expressed genes in each feature module as the feature genes, we reveal the relevance of the modules to the studied diseases. Using three datasets for prostate cancer, gastric cancer, and leukemia we have demonstrated that the proposed modular approach is of high power in identifying functionally integrated feature gene subsets that are highly relevant to the disease mechanisms. Our analysis has also shown that the critical disease-relevant genes might be better recognized from the gene regulation network, which is constructed using the characterized functional modules, giving important clues to the concerted mechanisms of the modules responding to complex disease states. approach to selecting the In addition, the proposed disease-relevant genes by jointly considering the gene functional knowledge suggests a new way for precisely classifying disease samples with clear biological interpretations, which is critical for the clinical diagnosis and the elucidation of the pathogenic basis of complex diseases.

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