Natarajan Jeyakumar, Berrar Daniel, Dubitzky Werner, Hack Catherine, Zhang Yonghong, DeSesa Catherine, Van Brocklyn James R, Bremer Eric G
School of Biomedical Sciences, University of Ulster at Coleraine, Cromore Road, Northern Ireland, UK.
BMC Bioinformatics. 2006 Aug 10;7:373. doi: 10.1186/1471-2105-7-373.
Sphingosine 1-phosphate (S1P), a lysophospholipid, is involved in various cellular processes such as migration, proliferation, and survival. To date, the impact of S1P on human glioblastoma is not fully understood. Particularly, the concerted role played by matrix metalloproteinases (MMP) and S1P in aggressive tumor behavior and angiogenesis remains to be elucidated.
To gain new insights in the effect of S1P on angiogenesis and invasion of this type of malignant tumor, we used microarrays to investigate the gene expression in glioblastoma as a response to S1P administration in vitro. We compared the expression profiles for the same cell lines under the influence of epidermal growth factor (EGF), an important growth factor. We found a set of 72 genes that are significantly differentially expressed as a unique response to S1P. Based on the result of mining full-text articles from 20 scientific journals in the field of cancer research published over a period of five years, we inferred gene-gene interaction networks for these 72 differentially expressed genes. Among the generated networks, we identified a particularly interesting one. It describes a cascading event, triggered by S1P, leading to the transactivation of MMP-9 via neuregulin-1 (NRG-1), vascular endothelial growth factor (VEGF), and the urokinase-type plasminogen activator (uPA). This interaction network has the potential to shed new light on our understanding of the role played by MMP-9 in invasive glioblastomas.
Automated extraction of information from biological literature promises to play an increasingly important role in biological knowledge discovery. This is particularly true for high-throughput approaches, such as microarrays, and for combining and integrating data from different sources. Text mining may hold the key to unraveling previously unknown relationships between biological entities and could develop into an indispensable instrument in the process of formulating novel and potentially promising hypotheses.
鞘氨醇-1-磷酸(S1P),一种溶血磷脂,参与多种细胞过程,如迁移、增殖和存活。迄今为止,S1P对人类胶质母细胞瘤的影响尚未完全了解。特别是,基质金属蛋白酶(MMP)和S1P在侵袭性肿瘤行为和血管生成中所起的协同作用仍有待阐明。
为了深入了解S1P对这种恶性肿瘤血管生成和侵袭的影响,我们使用微阵列研究胶质母细胞瘤中基因表达情况,作为体外给予S1P的反应。我们比较了在重要生长因子表皮生长因子(EGF)影响下相同细胞系的表达谱。我们发现一组72个基因作为对S1P的独特反应有显著差异表达。基于对癌症研究领域五年内发表的20种科学期刊全文文章的挖掘结果,我们推断了这72个差异表达基因的基因-基因相互作用网络。在生成的网络中,我们发现了一个特别有趣的网络。它描述了一个由S1P触发的级联事件,导致通过神经调节蛋白-1(NRG-1)、血管内皮生长因子(VEGF)和尿激酶型纤溶酶原激活剂(uPA)对MMP-9的反式激活。这个相互作用网络有可能为我们理解MMP-9在侵袭性胶质母细胞瘤中的作用提供新的线索。
从生物文献中自动提取信息有望在生物知识发现中发挥越来越重要的作用。对于高通量方法,如微阵列,以及结合和整合来自不同来源的数据尤其如此。文本挖掘可能是揭示生物实体之间先前未知关系的关键,并可能发展成为形成新颖且潜在有前景的假设过程中不可或缺的工具。