ArticlePloS one2012
G-cimp status prediction of glioblastoma samples using mRNA expression data.
Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Integrative DNA methylation and gene expression analysis to assess the universality of the CpG island methylator phenotype.Human genomics · 2015Pooled it
- Proteomic analysis reveals microvesicles containing NAMPT as mediators of radioresistance in glioma.Life science alliance · 2023Article
- SUPREME: multiomics data integration using graph convolutional networks.NAR genomics and bioinformatics · 2023Article
- Comprehensive transcriptomic characterization reveals core genes and module associated with immunological changes via 1619 samples of brain glioma.Cell death & disease · 2021Article
- PI3Kγ inhibition suppresses microglia/TAM accumulation in glioblastoma microenvironment to promote exceptional temozolomide response.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- SH2B3, Transcribed by STAT1, Promotes Glioblastoma Progression Through Transducing IL-6/gp130 Signaling to Activate STAT3 Signaling.Frontiers in cell and developmental biology · 2021Article
- Murine models of IDH-wild-type glioblastoma exhibit spatial segregation of tumor initiation and manifestation during evolution.Nature communications · 2020Article
- Current Opinion on Molecular Characterization for GBM Classification in Guiding Clinical Diagnosis, Prognosis, and Therapy.Frontiers in molecular biosciences · 2020Review
- Transcriptome profiling-based identification of prognostic subtypes and multi-omics signatures of glioblastoma.Scientific reports · 2019Article
- Modeling Patient-Derived Glioblastoma with Cerebral Organoids.Cell reports · 2019Article
- Treatment of Glioblastoma in the Elderly.Drugs & aging · 2018Review
- Opposing Tumor-Promoting and -Suppressive Functions of Rictor/mTORC2 Signaling in Adult Glioma and Pediatric SHH Medulloblastoma.Cell reports · 2018Article
- Identification of key genes in glioma CpG island methylator phenotype via network analysis of gene expression data.Molecular medicine reports · 2017Article
- Unsupervised deep learning reveals prognostically relevant subtypes of glioblastoma.BMC bioinformatics · 2017Article
- Tumor Evolution of Glioma-Intrinsic Gene Expression Subtypes Associates with Immunological Changes in the Microenvironment.Cancer cell · 2017Article
- Aberrant DNA methylation in melanoma: biomarker and therapeutic opportunities.Clinical epigenetics · 2017Review
- Sample Level Enrichment Analysis of KEGG Pathways Identifies Clinically Relevant Subtypes of Glioblastoma.Journal of Cancer · 2016Article
- Integrated analysis of genome-wide DNA methylation, gene expression and protein expression profiles in molecular subtypes of WHO II-IV gliomas.Journal of experimental & clinical cancer research : CR · 2015Article
- Epigenetic suppression of EGFR signaling in G-CIMP+ glioblastomas.Oncotarget · 2014Article
- Somatic intronic microsatellite loci differentiate glioblastoma from lower-grade gliomas.Oncotarget · 2014Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Glioblastoma Multiforme (GBM) is a tumor with high mortality and no known cure. The dramatic molecular and clinical heterogeneity seen in this tumor has led to attempts to define genetically similar subgroups of GBM with the hope of developing tumor specific therapies targeted to the unique biology within each of these subgroups. Recently, a subset of relatively favorable prognosis GBMs has been identified. These glioma CpG island methylator phenotype, or G-CIMP tumors, have distinct genomic copy number aberrations, DNA methylation patterns, and (mRNA) expression profiles compared to other GBMs. While the standard method for identifying G-CIMP tumors is based on genome-wide DNA methylation data, such data is often not available compared to the more widely available gene expression data. In this study, we have developed and evaluated a method to predict the G-CIMP status of GBM samples based solely on gene expression data.
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