Tissue differences revealed by gene expression profiles of various cell lines

被引:21
作者
Chen, Lei [1 ,2 ,3 ]
Pan, Xiaoyong [4 ]
Zhang, Yu-Hang [5 ]
Kong, Xiangyin [5 ]
Huang, Tao [5 ]
Cai, Yu-Dong [1 ]
机构
[1] Shanghai Univ, Sch Life Sci, Shanghai 200444, Peoples R China
[2] Shanghai Maritime Univ, Coll Informat Engn, Shanghai, Peoples R China
[3] East China Normal Univ, Shanghai Key Lab PMMP, Shanghai, Peoples R China
[4] Erasmus MC, Dept Med Informat, Rotterdam, Netherlands
[5] Chinese Acad Sci, Shanghai Inst Biol Sci, Inst Hlth Sci, Shanghai 200031, Peoples R China
基金
上海市自然科学基金; 中国国家自然科学基金;
关键词
cell line; gene expression; incremental feature selection; Monte Carlo feature selection; support vector machine; CARLO FEATURE-SELECTION; IDENTIFICATION; PROTEIN; LUNG; TRANSLOCATION; PREDICTION; CANCER;
D O I
10.1002/jcb.27977
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
Mechanisms through which tissues are formed and maintained remain unknown but are fundamental aspects in biology. Tissue-specific gene expression is a valuable tool to study such mechanisms. But in many biomedical studies, cell lines, rather than human body tissues, are used to investigate biological mechanisms Whether or not cell lines maintain their tissue-specific characteristics after they are isolated and cultured outside the human body remains to be explored. In this study, we applied a novel computational method to identify core genes that contribute to the differentiation of cell lines from various tissues. Several advanced computational techniques, such as Monte Carlo feature selection method, incremental feature selection method, and support vector machine (SVM) algorithm, were incorporated in the proposed method, which extensively analyzed the gene expression profiles of cell lines from different tissues. As a result, we extracted a group of functional genes that can indicate the differences of cell lines in different tissues and built an optimal SVM classifier for identifying cell lines in different tissues. In addition, a set of rules for classifying cell lines were also reported, which can give a clearer picture of cell lines in different issues although its performance was not better than the optimal SVM classifier. Finally, we compared such genes with the tissue-specific genes identified by the Genotype-tissue Expression project. Results showed that most expression patterns between tissues remained in the derived cell lines despite some uniqueness that some genes show tissue specificity.
引用
收藏
页码:7068 / 7081
页数:14
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