Chen Lei, Pan Xiaoyong, Zhang Yu-Hang, Kong Xiangyin, Huang Tao, Cai Yu-Dong
School of Life Sciences, Shanghai University, Shanghai, China.
College of Information Engineering, Shanghai Maritime University, Shanghai, China.
J Cell Biochem. 2019 May;120(5):7068-7081. doi: 10.1002/jcb.27977. Epub 2018 Oct 28.
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.
组织形成和维持的机制尚不清楚，但却是生物学的基本方面。组织特异性基因表达是研究此类机制的宝贵工具。但在许多生物医学研究中，用于研究生物学机制的是细胞系而非人体组织。细胞系在离体培养后是否仍保持其组织特异性特征仍有待探索。在本研究中，我们应用了一种新颖的计算方法来识别有助于不同组织细胞系分化的核心基因。所提出的方法纳入了几种先进的计算技术，如蒙特卡罗特征选择方法、增量特征选择方法和支持向量机（SVM）算法，该方法广泛分析了来自不同组织的细胞系的基因表达谱。结果，我们提取了一组能够指示不同组织中细胞系差异的功能基因，并构建了一个用于识别不同组织中细胞系的最优支持向量机分类器。此外，还报告了一组细胞系分类规则，尽管其性能不如最优支持向量机分类器，但能更清晰地呈现不同组织中的细胞系情况。最后，我们将此类基因与基因型-组织表达项目所识别的组织特异性基因进行了比较。结果表明，尽管某些基因表现出组织特异性的独特性，但组织间的大多数表达模式在衍生的细胞系中仍然保留。
