ArticleMolecular medicine reports2017
Integrated mRNAseq and microRNAseq data analysis for grade III gliomas.
Article in Molecular medicine reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- The G Protein-Coupled Receptor-Related Gene Signatures for Diagnosis and Prognosis in Glioblastoma: A Deep Learning Model Using RNA-Seq Data.Asian Pacific journal of cancer prevention : APJCP · 2024Article
- High Expression of KIFC1 in Glioma Correlates with Poor Prognosis.Journal of Korean Neurosurgical Society · 2024Article
- Inferring Diagnostic and Prognostic Gene Expression Signatures Across WHO Glioma Classifications: A Network-Based Approach.Bioinformatics and biology insights · 2024Article
- AnPeerJ · 2023Article
- An integrated multi-omics analysis of topoisomerase family in pan-cancer: Friend or foe?PloS one · 2022Article
- Identification of Molecular Characteristics and New Prognostic Targets for Thymoma by Multiomics Analysis.BioMed research international · 2021Article
- Article
- Tumor Suppressor microRNA-138 Suppresses Low-Grade Glioma Development and Metastasis via Regulating IGF2BP2.OncoTargets and therapy · 2020Article
- Investigation of miRNA and mRNA Co-expression Network in Ependymoma.Frontiers in bioengineering and biotechnology · 2020Article
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The World Health Organization classification distinguishes four grades for gliomas. Grade III gliomas, which are brain malignant brain tumors with variable biological behavior and propensity, have been not widely investigated. The objective of the present study was to identify specific gene modules and valuable hubs associated with gliomagenesis and molecular signatures to assist in determining grade III glioma prognosis. mRNAseq and micro (mi)RNAseq data were used to construct a co‑expression network of gliomas using weight gene co‑expression network analysis, and revealed the prognostic molecular signature of grade III gliomas. The differently expressed miRNAs and mRNAs were identified. A total of 37 mRNAs and 10 miRNAs were identified, which were closely associated with the survival rates of patients with grade III glioma. To further understand the tumorigenesis, Cytoscape software was used to construct a network containing these differently expressed molecules. The result suggested that both the downregulated genes and upregulated genes are vital in the process of glioma deterioration, and certain genes are closely associated with clinical prognosis.
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