Next Article in Journal
PreS1 Mutations Alter the Large HBsAg Antigenicity of a Hepatitis B Virus Strain Isolated in Bangladesh
Previous Article in Journal
Vascular Endothelial Growth Factor A and VEGFR-1 Change during Preimplantation in Heifers
Previous Article in Special Issue
Knowledge Generation with Rule Induction in Cancer Omics
Open AccessArticle

Open Data for Differential Network Analysis in Glioma

Holzinger Group HCI-KDD, Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Auenbruggerplatz 2/V, 8036 Graz, Austria
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Int. J. Mol. Sci. 2020, 21(2), 547; https://doi.org/10.3390/ijms21020547
Received: 27 October 2019 / Revised: 29 December 2019 / Accepted: 3 January 2020 / Published: 15 January 2020
(This article belongs to the Special Issue Data Analysis and Integration in Cancer Research)
The complexity of cancer diseases demands bioinformatic techniques and translational research based on big data and personalized medicine. Open data enables researchers to accelerate cancer studies, save resources and foster collaboration. Several tools and programming approaches are available for analyzing data, including annotation, clustering, comparison and extrapolation, merging, enrichment, functional association and statistics. We exploit openly available data via cancer gene expression analysis, we apply refinement as well as enrichment analysis via gene ontology and conclude with graph-based visualization of involved protein interaction networks as a basis for signaling. The different databases allowed for the construction of huge networks or specified ones consisting of high-confidence interactions only. Several genes associated to glioma were isolated via a network analysis from top hub nodes as well as from an outlier analysis. The latter approach highlights a mitogen-activated protein kinase next to a member of histondeacetylases and a protein phosphatase as genes uncommonly associated with glioma. Cluster analysis from top hub nodes lists several identified glioma-associated gene products to function within protein complexes, including epidermal growth factors as well as cell cycle proteins or RAS proto-oncogenes. By using selected exemplary tools and open-access resources for cancer research and differential network analysis, we highlight disturbed signaling components in brain cancer subtypes of glioma. View Full-Text
Keywords: open data; cancer research; differential network analysis; differential gene expression; protein-protein interaction; graph-based analysis; glioma; glioblastoma multiforme; astrocytoma; biological data integration open data; cancer research; differential network analysis; differential gene expression; protein-protein interaction; graph-based analysis; glioma; glioblastoma multiforme; astrocytoma; biological data integration
Show Figures

Graphical abstract

MDPI and ACS Style

Jean-Quartier, C.; Jeanquartier, F.; Holzinger, A. Open Data for Differential Network Analysis in Glioma. Int. J. Mol. Sci. 2020, 21, 547.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop