Diagnosing phenotypes of single-sample individuals by edge biomarkers

doi:10.1093/jmcb/mjv025

	Diagnosing phenotypes of single-sample individuals by edge biomarkers
	Zhang, Wanwei 1; Zeng, Tao 1; Liu, Xiaoping 1; Chen, Luonan1,2
	2015-06
发表期刊	JOURNAL OF MOLECULAR CELL BIOLOGY (IF:5.3[JCR-2023],6.1[5-Year])
ISSN	1674-2788
卷号	7 期号:3 页码:231-241
发表状态	已发表
DOI	10.1093/jmcb/mjv025
摘要	Network or edge biomarkers are a reliable form to characterize phenotypes or diseases. However, obtaining edges or correlations between molecules for an individual requires measurement of multiple samples of that individual, which are generally unavailable in clinical practice. Thus, it is strongly demanded to diagnose a disease by edge or network biomarkers in one-sample-for-one-individual context. Here, we developed a new computational framework, EdgeBiomarker, to integrate edge and node biomarkers to diagnose phenotype of each single test sample. By applying the method to datasets of lung and breast cancer, it reveals new marker genes/gene-pairs and related sub-networks for distinguishing earlier and advanced cancer stages. Our method shows advantages over traditional methods: (i) edge biomarkers extracted from non-differentially expressed genes achieve better cross-validation accuracy of diagnosis than molecule or node biomarkers from differentially expressed genes, suggesting that certain pathogenic information is only present at the level of network and under-estimated by traditional methods; (ii) edge biomarkers categorize patients into low/high survival rate in a more reliable manner; (iii) edge biomarkers are significantly enriched in relevant biological functions or pathways, implying that the association changes ina network, rather than expression changes in individual molecules, tend to be causally related to cancer development. The new framework of edge biomarkers paves the way for diagnosing diseases and analyzing their molecular mechanisms by edges or networks in one-sample-for-one-individual basis. This also provides a powerful tool for precision medicine or big-data medicine.
关键词	edge biomarker edge feature progressive stages disease diagnosis big biological data
收录类别	SCI
语种	英语
资助项目	Knowledge Innovation Program of SIBS of CAS[2013KIP218]
WOS研究方向	Cell Biology
WOS类目	Cell Biology
WOS记录号	WOS:000357857100005
出版者	OXFORD UNIV PRESS
WOS关键词	IDENTIFYING CRITICAL TRANSITIONS ; UNFOLDED PROTEIN RESPONSE ; BREAST-CANCER CELLS ; P53 MESSENGER-RNA ; COMPLEX DISEASES ; NETWORK BIOMARKERS ; TUMOR-METASTASIS ; PATHWAY ; PROGRESSION ; PREDICTION
原始文献类型	Article
引用统计	正在获取...
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/2203
专题	生命科学与技术学院_特聘教授组_陈洛南组
通讯作者	Chen, Luonan
作者单位	1.Chinese Acad Sci, Shanghai Inst Biol Sci, Inst Biochem & Cell Biol, Key Lab Syst Biol,Innovat Ctr Cell Signaling Netw, Shanghai 200031, Peoples R China 2.ShanghaiTech Univ, Sch Life Sci & Technol, Shanghai 201210, Peoples R China
通讯作者单位	生命科学与技术学院
推荐引用方式 GB/T 7714	Zhang, Wanwei,Zeng, Tao,Liu, Xiaoping,et al. Diagnosing phenotypes of single-sample individuals by edge biomarkers[J]. JOURNAL OF MOLECULAR CELL BIOLOGY,2015,7(3):231-241.
APA	Zhang, Wanwei,Zeng, Tao,Liu, Xiaoping,&Chen, Luonan.(2015).Diagnosing phenotypes of single-sample individuals by edge biomarkers.JOURNAL OF MOLECULAR CELL BIOLOGY,7(3),231-241.
MLA	Zhang, Wanwei,et al."Diagnosing phenotypes of single-sample individuals by edge biomarkers".JOURNAL OF MOLECULAR CELL BIOLOGY 7.3(2015):231-241.