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Structuring osteosarcoma knowledge: an osteosarcoma-gene association database based on literature mining and manual

Kathrin Poos1, Jan Smida2, Michaela Nathrath2

  • 1Institute 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 : the Journal of Biological Databases and Curation
|May 29, 2014
PubMed
Summary

Osteosarcoma research is complex due to genomic instability. A new database consolidates genes and microRNAs, aiding biomarker discovery and therapeutic insights for this bone cancer.

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Area of Science:

  • Oncology
  • Genetics
  • Bioinformatics

Background:

  • Osteosarcoma (OS) is the most common primary bone cancer, characterized by significant genomic instability.
  • This instability impacts numerous genes and microRNAs, complicating the identification of reliable prognostic and therapeutic biomarkers.
  • The rapid influx of new research necessitates a structured approach to OS knowledge management.

Purpose of the Study:

  • To create a publicly accessible database and web interface for curated information on OS-associated genes and microRNAs.
  • To facilitate easier access to annotated data for researchers, supporting biomarker discovery and therapeutic advancements.
  • To provide a centralized resource for the bone tumor research community.

Main Methods:

  • Genes and microRNAs were identified using automated dictionary-based recognition followed by expert manual review and annotation.
  • Data was collected from 1331 PubMed abstracts, resulting in the curation of 911 genes and 81 microRNAs.
  • A pathway enrichment analysis was performed on the collected genes to understand OS progression.

Main Results:

  • The database contains 911 genes and 81 microRNAs linked to 1331 PubMed abstracts, updated as of October 29, 2013.
  • Users can evaluate genes and microRNAs based on prognostic/therapeutic impact, experimental details, sample types, and biological context.
  • Pathway analysis revealed common cancer pathways and OS-specific alterations, such as deregulated osteoclast differentiation.

Conclusions:

  • This database represents the first manually reviewed and annotated resource for up-to-date osteosarcoma knowledge.
  • The platform offers quick and easy access to specific gene and microRNA information, crucial for ongoing OS research.
  • It is expected to significantly support the bone tumor research community in biomarker discovery and therapeutic strategy development.