Unravelling personalized dysfunctional gene network of complex diseases based on differential network model

	Unravelling personalized dysfunctional gene network of complex diseases based on differential network model
	Yu, XT; Zeng, T; Wang, XD; Li, GJ; Chen, LN
刊名	JOURNAL OF TRANSLATIONAL MEDICINE
	2015
卷号	13 期号:1 页码:189-189
关键词	Gene expression Expression variance Precision medicine Gene network Network biomarker Disease heterogeneity Edge biomarker
通讯作者	Li, GJ (reprint author), Shandong Univ, Sch Math, Jinan 250100, Peoples R China.,guojunsdu@gmail.com ; lnchen@sibs.ac.cn
英文摘要	In the conventional analysis of complex diseases, the control and case samples are assumed to be of great purity. However, due to the heterogeneity of disease samples, many disease genes are even not always consistently up-/down-regulated, leading to be under-estimated. This problem will seriously influence effective personalized diagnosis or treatment. The expression variance and expression covariance can address such a problem in a network manner. But, these analyses always require multiple samples rather than one sample, which is generally not available in clinical practice for each individual. To extract the common and specific network characteristics for individual patients in this paper, a novel differential network model, e.g. personalized dysfunctional gene network, is proposed to integrate those genes with different features, such as genes with the differential gene expression (DEG), genes with the differential expression variance (DEVG) and gene-pairs with the differential expression covariance (DECG) simultaneously, to construct personalized dysfunctional networks. This model uses a new statistic-like measurement on differential information, i.e., a differential score (DEVC), to reconstruct the differential expression network between groups of normal and diseased samples; and further quantitatively evaluate different feature genes in the patient-specific network for each individual. This DEVC-based differential expression network (DEVC-net) has been applied to the study of complex diseases for prostate cancer and diabetes. (1) Characterizing the global expression change between normal and diseased samples, the differential gene networks of those diseases were found to have a new bi-coloured topological structure, where their non hub-centred sub-networks are mainly composed of genes/proteins controlling various biological processes. (2) The differential expression variance/covariance rather than differential expression is new informative sources, and can be used to identify genes or gene-pairs with discriminative power, which are ignored by traditional methods. (3) More importantly, DEVC-net is effective to measure the expression state or activity of different feature genes and their network or modules in one sample for an individual. All of these results support that DEVC-net indeed has a clear advantage to effectively extract discriminatively interpretable features of gene/protein network of one sample (i.e. personalized dysfunctional network) even when disease samples are heterogeneous, and thus can provide new features like gene-pairs, in addition to the conventional individual genes, to the analysis of the personalized diagnosis and prognosis, and a better understanding on the underlying biological mechanisms.
学科主题	Research & Experimental Medicine
类目[WOS]	Medicine, Research & Experimental
关键词[WOS]	PROTEIN INTERACTION NETWORKS ; PROSTATE-CANCER ; GRAPH ENTROPY ; EXPRESSION ; BIOMARKERS ; CLASSIFICATION ; HETEROGENEITY ; PROGRESSION ; PREDICTION ; CENTRALITY
收录类别	SCI
语种	英语
WOS记录号	WOS:000356212200001
内容类型	期刊论文
版本	出版稿
源URL	[http://202.127.25.143/handle/331003/109]
专题	上海生化细胞研究所_上海生科院生化细胞研究所
推荐引用方式 GB/T 7714	Yu, XT,Zeng, T,Wang, XD,et al. Unravelling personalized dysfunctional gene network of complex diseases based on differential network model[J]. JOURNAL OF TRANSLATIONAL MEDICINE,2015,13(1):189-189.
APA	Yu, XT,Zeng, T,Wang, XD,Li, GJ,&Chen, LN.(2015).Unravelling personalized dysfunctional gene network of complex diseases based on differential network model.JOURNAL OF TRANSLATIONAL MEDICINE,13(1),189-189.
MLA	Yu, XT,et al."Unravelling personalized dysfunctional gene network of complex diseases based on differential network model".JOURNAL OF TRANSLATIONAL MEDICINE 13.1(2015):189-189.