Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Topics Community

Article

Identification of Candidate Genes Associated With Prognosis in Glioblastoma

7 Jul 2022

Abstract excerpt

Background: Glioblastoma (GBM) is the most common malignant primary brain tumor, which associated with extremely poor prognosis. Methods: Data from datasets GSE16011, GSE7696, GSE50161, GSE90598 and The Cancer Genome Atlas (TCGA) were analyzed to identify differentially expressed genes (DEGs) between patients and controls. DEGs common to all five datasets were analyzed for functional enrichment and for...

Source

Identifiers

PMID: 35875673
DOI: 10.3389/fnmol.2022.913328

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 31 Aug 2026.

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Community interpretation

Discuss the evidence

Ask a question, add context or challenge an interpretation. Community activity is separate from scientific evidence strength.

0

discussions

0

replies

Citations encouraged

Claims remain attributable.

No discussion yet

Be the first to add a careful, cited interpretation.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Identification of Candidate Genes Associated With Prognosis in GlioblastomaPMID 35875673Read ↗

Identification of an Immune-Related Prognostic Risk Model in GlioblastomaSemantic proximity Read ↗

Identification of a Novel Eight-Gene Risk Model for Predicting Survival in Glioblastoma: A Comprehensive Bioinformatic AnalysisSemantic proximity Read ↗

Bioinformatics analyses of significant genes, related pathways and candidate prognostic biomarkers in glioblastomaSemantic proximity Read ↗

Identification of Hypoxia Prognostic Signature in Glioblastoma Multiforme Based on Bulk and Single-Cell RNA-SeqSemantic proximity Read ↗

RRN3P2, RPL7AP64, ACTR3C, & RPL7AP30 as Novel Prognostic Biomarkers for Glioblastoma (GBM): A Bioinformatics-Based AnalysisSemantic proximity Read ↗

Molecular Insights and Prognosis Associated With RBM8A in GlioblastomaSemantic proximity Read ↗

Identification of Key Differentially Expressed Transcription Factors in GlioblastomaSemantic proximity Read ↗

Machine learning and multi-omics analysis reveal key regulators of proneural–mesenchymal transition in glioblastomaSemantic proximity Read ↗

RRN3P2, RPL7AP64, ACTR3C, & RPL7AP30 as Novel Prognostic Biomarkers for Glioblastoma (GBM): A Bioinformatics-Based AnalysisSemantic proximity Read ↗

The genes significantly associated with an improved prognosis and long-term survival of glioblastomaSemantic proximity Read ↗

Molecular subtyping of glioblastoma based on immune-related genes for prognosisSemantic proximity Read ↗

The identification of key genes and pathways in glioblastoma by bioinformatics analysisSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.