ArticleInternational journal of molecular sciences2020
Open Data for Differential Network Analysis in Glioma.
Article in International journal of molecular sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed.
- Multi-Layered Analysis of TGF-β Signaling and Regulation via DNA Methylation and microRNAs in Astrocytic Tumors.International journal of molecular sciences · 2025Article
- Article
- Analysis of the Expression Patterns of Tumor Necrosis Factor Alpha Signaling Pathways and Regulatory MicroRNAs in Astrocytic Tumors.International journal of molecular sciences · 2025Article
- Key genes altered in glioblastoma based on bioinformatics (Review).Oncology letters · 2025Review
- Review
- Computational pathology-based weakly supervised prediction model for MGMT promoter methylation status in glioblastoma.Frontiers in neurology · 2024Article
- Identification of hub genes and biological pathways in glioma via integrated bioinformatics analysis.The Journal of international medical research · 2022Article
- Mutation-based clustering and classification analysis reveals distinctive age groups and age-related biomarkers for glioma.BMC medical informatics and decision making · 2021Article
- Decreased Expression ofInternational journal of molecular sciences · 2020Article
- Hypoxia Inducible Factors' Signaling in Pediatric High-Grade Gliomas: Role, Modelization and Innovative Targeted Approaches.Cancers · 2020Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.