Discover Community Create You

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

Article

Identification of glioblastoma gene prognosis modules based on weighted gene co-expression network analysis

1 Nov 2018

Abstract excerpt

BACKGROUND: Glioblastoma multiforme, the most prevalent and aggressive brain tumour, has a poor prognosis. The molecular mechanisms underlying gliomagenesis remain poorly understood. Therefore, molecular research, including various markers, is necessary to understand the occurrence and development of glioma. METHOD: Weighted gene co-expression network analysis (WGCNA) was performed to construct a gene...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Bioinformatics and Genomic Networks
Share to Ferroptosis and cancer prognosis
Share to Glioma Diagnosis and Treatment

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Source

Identifiers

PMID: 30382873
DOI: 10.1186/s12920-018-0407-1

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 6 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Identification of glioblastoma gene prognosis modules based on weighted gene co-expression network analysisPMID 30382873Read ↗

Identification of a Specific Gene Module for Predicting Prognosis in Glioblastoma PatientsSemantic proximity Read ↗

Dissecting Prognosis Modules and Biomarkers in Glioblastoma Based on Weighted Gene Co-Expression Network AnalysisSemantic proximity Read ↗

Identification of a Key Glioblastoma Candidate Gene, FUBP3, Based on Weighted Gene Co-expression Network AnalysisSemantic proximity Read ↗

Predicting glioblastoma prognosis networks using weighted gene co-expression network analysis on TCGA dataSemantic proximity Read ↗

Construct ion of co - expression modules related to survival by WGCNA and identif ication of potential prognostic biomarkers in glioblastomaSemantic proximity Read ↗

Identification of the potential biomarkers in patients with glioma: a weighted gene co-expression network analysisSemantic proximity Read ↗

Identification of key genes involved in the recurrence of glioblastoma multiforme using weighted gene co-expression network analysis and differential expression analysisSemantic proximity Read ↗

Expression Profile Analysis Identifies a Novel Five-Gene Signature to Improve Prognosis Prediction of GlioblastomaSemantic proximity Read ↗

Identification of an Immune-Related Prognostic Signature for Glioblastoma by Comprehensive Bioinformatics and Experimental AnalysesSemantic proximity Read ↗

Construction of co‐expression modules related to survival by WGCNA and identification of potential prognostic biomarkers in glioblastomaSemantic proximity Read ↗

Comprehensive transcriptomic characterization reveals core genes and module associated with immunological changes via 1619 samples of brain gliomaSemantic proximity Read ↗

Analysis of long non-coding RNAs in glioblastoma for prognosis prediction using weighted gene co-expression network analysis, Cox regression, and L1-LASSO penalizationSemantic proximity Read ↗

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