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Tissue differences revealed by gene expression profiles of various cell lines
Author(s) -
Chen Lei,
Pan Xiaoyong,
Zhang YuHang,
Kong Xiangyin,
Huang Tao,
Cai YuDong
Publication year - 2019
Publication title -
journal of cellular biochemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.028
H-Index - 165
eISSN - 1097-4644
pISSN - 0730-2312
DOI - 10.1002/jcb.27977
Subject(s) - cell culture , gene , computational biology , biology , gene expression , cell , gene expression profiling , support vector machine , feature selection , cell type , bioinformatics , genetics , computer science , artificial intelligence
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.