Shefa Ulfuara, Jung Junyang
Department of Biomedical Science, Graduate School, Kyung Hee University, Dongdaemun-gu, Seoul, Republic of Korea.
Department of Biomedical Science, Graduate School; Department of Anatomy and Neurobiology, College of Medicine, Kyung Hee University, Dongdaemun-gu, Seoul, Republic of Korea.
Neural Regen Res. 2019 Jun;14(6):1099-1104. doi: 10.4103/1673-5374.250632.
A Schwann cell has regenerative capabilities and is an important cell in the peripheral nervous system. This microarray study is part of a bioinformatics study that focuses mainly on Schwann cells. Microarray data provide information on differences between microarray-based and experiment-based gene expression analyses. According to microarray data, several genes exhibit increased expression (fold change) but they are weakly expressed in experimental studies (based on morphology, protein and mRNA levels). In contrast, some genes are weakly expressed in microarray data and highly expressed in experimental studies; such genes may represent future target genes in Schwann cell studies. These studies allow us to learn about additional genes that could be used to achieve targeted results from experimental studies. In the current big data study by retrieving more than 5000 scientific articles from PubMed or NCBI, Google Scholar, and Google, 1016 (up- and downregulated) genes were determined to be related to Schwann cells. However, no experiment was performed in the laboratory; rather, the present study is part of a big data analysis. Our study will contribute to our understanding of Schwann cell biology by aiding in the identification of genes. Based on a comparative analysis of all microarray data, we conclude that the microarray could be a good tool for predicting the expression and intensity of different genes of interest in actual experiments.
施万细胞具有再生能力,是周围神经系统中的一种重要细胞。这项微阵列研究是一项主要聚焦于施万细胞的生物信息学研究的一部分。微阵列数据提供了基于微阵列和基于实验的基因表达分析之间差异的信息。根据微阵列数据,一些基因表现出表达增加(倍数变化),但在实验研究(基于形态学、蛋白质和mRNA水平)中表达较弱。相反,一些基因在微阵列数据中表达较弱,而在实验研究中高度表达;这类基因可能代表施万细胞研究中未来的靶基因。这些研究使我们能够了解更多可用于从实验研究中获得靶向结果的基因。在当前的大数据研究中,通过从PubMed或NCBI、谷歌学术和谷歌检索5000多篇科学文章,确定了1016个(上调和下调)与施万细胞相关的基因。然而,实验室中未进行任何实验;相反,本研究是大数据分析的一部分。我们的研究将通过协助基因鉴定来促进我们对施万细胞生物学的理解。基于对所有微阵列数据的比较分析,我们得出结论,微阵列可能是预测实际实验中不同感兴趣基因的表达和强度的良好工具。