Poos Kathrin, Smida Jan, Nathrath Michaela, Maugg Doris, Baumhoer Daniel, Neumann Anna, Korsching Eberhard
Institute of Bioinformatics, University of Münster, Münster, Germany, Clinical Cooperation Group Osteosarcoma, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764 Neuherberg, Germany, Children's Cancer Research Center and Department of Pediatrics, Klinikum rechts der Isar, Technische Universität München, 81664 Munich, Germany and Bone Tumor Reference Center at the Institute of Pathology, University Hospital Basel, Basel, Switzerland.
Institute of Bioinformatics, University of Münster, Münster, Germany, Clinical Cooperation Group Osteosarcoma, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764 Neuherberg, Germany, Children's Cancer Research Center and Department of Pediatrics, Klinikum rechts der Isar, Technische Universität München, 81664 Munich, Germany and Bone Tumor Reference Center at the Institute of Pathology, University Hospital Basel, Basel, SwitzerlandInstitute of Bioinformatics, University of Münster, Münster, Germany, Clinical Cooperation Group Osteosarcoma, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764 Neuherberg, Germany, Children's Cancer Research Center and Department of Pediatrics, Klinikum rechts der Isar, Technische Universität München, 81664 Munich, Germany and Bone Tumor Reference Center at the Institute of Pathology, University Hospital Basel, Basel, Switzerland.
Database (Oxford). 2014 May 27;2014. doi: 10.1093/database/bau042. Print 2014.
Osteosarcoma (OS) is the most common primary bone cancer exhibiting high genomic instability. This genomic instability affects multiple genes and microRNAs to a varying extent depending on patient and tumor subtype. Massive research is ongoing to identify genes including their gene products and microRNAs that correlate with disease progression and might be used as biomarkers for OS. However, the genomic complexity hampers the identification of reliable biomarkers. Up to now, clinico-pathological factors are the key determinants to guide prognosis and therapeutic treatments. Each day, new studies about OS are published and complicate the acquisition of information to support biomarker discovery and therapeutic improvements. Thus, it is necessary to provide a structured and annotated view on the current OS knowledge that is quick and easily accessible to researchers of the field. Therefore, we developed a publicly available database and Web interface that serves as resource for OS-associated genes and microRNAs. Genes and microRNAs were collected using an automated dictionary-based gene recognition procedure followed by manual review and annotation by experts of the field. In total, 911 genes and 81 microRNAs related to 1331 PubMed abstracts were collected (last update: 29 October 2013). Users can evaluate genes and microRNAs according to their potential prognostic and therapeutic impact, the experimental procedures, the sample types, the biological contexts and microRNA target gene interactions. Additionally, a pathway enrichment analysis of the collected genes highlights different aspects of OS progression. OS requires pathways commonly deregulated in cancer but also features OS-specific alterations like deregulated osteoclast differentiation. To our knowledge, this is the first effort of an OS database containing manual reviewed and annotated up-to-date OS knowledge. It might be a useful resource especially for the bone tumor research community, as specific information about genes or microRNAs is quick and easily accessible. Hence, this platform can support the ongoing OS research and biomarker discovery. Database URL: http://osteosarcoma-db.uni-muenster.de.
骨肉瘤（OS）是最常见的原发性骨癌，具有高度的基因组不稳定性。这种基因组不稳定性会不同程度地影响多个基因和微小RNA，具体取决于患者和肿瘤亚型。目前正在进行大量研究，以确定与疾病进展相关的基因及其基因产物和微小RNA，这些基因和微小RNA可能用作骨肉瘤的生物标志物。然而，基因组的复杂性阻碍了可靠生物标志物的识别。到目前为止，临床病理因素是指导预后和治疗的关键决定因素。每天都有关于骨肉瘤的新研究发表，这使得获取支持生物标志物发现和治疗改进的信息变得更加复杂。因此，有必要提供一个结构化且有注释的当前骨肉瘤知识视图，以便该领域的研究人员能够快速轻松地获取。因此，我们开发了一个公开可用的数据库和网络界面，作为骨肉瘤相关基因和微小RNA的资源。使用基于自动词典的基因识别程序收集基因和微小RNA，随后由该领域的专家进行人工审核和注释。总共收集了与1331篇PubMed摘要相关的911个基因和81个微小RNA（最后更新时间：2013年10月29日）。用户可以根据基因和微小RNA的潜在预后和治疗影响、实验程序、样本类型、生物学背景以及微小RNA靶基因相互作用来进行评估。此外，对收集到的基因进行的通路富集分析突出了骨肉瘤进展的不同方面。骨肉瘤需要癌症中常见的失调通路，但也具有骨肉瘤特异性改变，如破骨细胞分化失调。据我们所知，这是首次努力构建一个包含经过人工审核和注释的最新骨肉瘤知识的数据库。它可能是一个有用的资源，特别是对于骨肿瘤研究群体，因为关于基因或微小RNA的特定信息可以快速轻松地获取。因此，这个平台可以支持正在进行的骨肉瘤研究和生物标志物发现。数据库网址：http://osteosarcoma-db.uni-muenster.de
