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

作　　者：ZHANG Min ZHU Jing GUO Zheng LI Xia YANG Da WANG Lei RAO Shaoqi 

机构地区：[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

出　　处：《Chinese Science Bulletin》2006年第15期1848-1856,共9页

基　　金：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.2003AA3CS113);the Natural Science Foundation of Heilongjiang Province(Grant No.F0177);0utstanding 0verseas Scientist Foundation of Education Department of Heilongjiang Province(Grant No.1055HG009).

摘　　要：Identifying disease-relevant genes and functional modules, based on gene expression pro- files and gene functional knowledge, is of high im- portance for studying disease mechanisms and sub- typing disease phenotypes. Using gene categories of biological process and cellular component in Gene Ontology, we propose an approach to selecting func- tional 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 data- sets 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 rele- vant 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. In addition, the proposed approach to selecting the disease-relevant genes byjointly 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.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.

关 键 词：基因表达 基因序列 特征基因 基因复制 

分 类 号：Q78[生物学—分子生物学]