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

Protein-network modeling of prostate cancer gene signatures reveals essential pathways in disease recurrence

14 Jun 2011

Abstract excerpt

OBJECTIVE: Uncovering the dominant molecular deregulation among the multitude of pathways implicated in aggressive prostate cancer is essential to intelligently developing targeted therapies. Paradoxically, published prostate cancer gene expression signatures of poor prognosis share little overlap and thus do not reveal shared mechanisms. The authors hypothesize that, by analyzing gene signatures with...

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 Clusterin in disease pathology
Share to Prostate Cancer Diagnosis and Treatment

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Source

Identifiers

PMID: 21672909
DOI: 10.1136/amiajnl-2011-000178

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Protein-network modeling of prostate cancer gene signatures reveals essential pathways in disease recurrencePMID 21672909Read ↗

5 Gleason score-associated gene signatures serve as novel biomarkers for identifying early recurring events and contributing to early diagnosis for Prostate AdenocarcinomaSemantic proximity Read ↗

Exploring the interaction between immune cells in the prostate cancer microenvironment combining weighted correlation gene network analysis and single-cell sequencing: An integrated bioinformatics analysisSemantic proximity Read ↗

Identification and validation of a prognostic 5-protein signature for biochemical recurrence following radical prostatectomy in prostate cancerSemantic proximity Read ↗

Identification of key regulators in Prostate cancer from gene expression datasets of patientsSemantic proximity Read ↗

Patient-matched analysis identifies deregulated networks in prostate cancer to guide personalized therapeutic interventionSemantic proximity Read ↗

Comprehensive evaluation of machine learning models and gene expression signatures for prostate cancer prognosis using large population cohortsSemantic proximity Read ↗

Transcriptome Network Analysis Identifies CXCL13-CXCR5 Signaling Modules in the Prostate Tumor Immune MicroenvironmentSemantic proximity Read ↗

Proteomic-based stratification of intermediate-risk prostate cancer patientsSemantic proximity Read ↗

Identification of Biomarkers and Trajectories of Prostate Cancer Progression: A Bioinformatics Fusion of Weighted Correlation Network Analysis and Machine LearningSemantic proximity Read ↗

Multiple Oncogenic Pathway Signatures Show Coordinate Expression Patterns in Human Prostate TumorsSemantic proximity Read ↗

Convergent network effects along the axis of gene expression during prostate cancer progressionSemantic proximity Read ↗

microRNA Dysregulation in Prostate Cancer: Network Analysis Reveals Preferential Regulation of Highly Connected NodesSemantic proximity Read ↗

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