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 metastasis-related genes for predicting prostate cancer diagnosis, metastasis and immunotherapy drug candidates using machine learning approaches

25 Jun 2024

Abstract excerpt

BACKGROUND: Prostate cancer (PCa) is the second leading cause of tumor-related mortality in men. Metastasis from advanced tumors is the primary cause of death among patients. Identifying novel and effective biomarkers is essential for understanding the mechanisms of metastasis in PCa patients and developing successful interventions. METHODS: Using the GSE8511 and GSE27616 data sets, 21 metastasis-related genes...

Read the complete abstract on PubMed

Topics

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

Share to Clusterin in disease pathology
Share to Ferroptosis and cancer prognosis
Share to GDF15 and Related Biomarkers

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Source

Identifiers

PMID: 38918844
DOI: 10.1186/s13062-024-00494-x

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

Identification of metastasis-related genes for predicting prostate cancer diagnosis, metastasis and immunotherapy drug candidates using machine learning approachesPMID 38918844Read ↗

A Risk Prediction Model of DNA Methylation Improves Prognosis Evaluation and Indicates Gene Targets in Prostate CancerSemantic proximity Read ↗

Identification of HCG18 and MCM3AP-AS1 That Associate With Bone Metastasis, Poor Prognosis and Increased Abundance of M2 Macrophage Infiltration in Prostate CancerSemantic proximity Read ↗

Prostate cancer invasion and metastasis: insights from mining genomic dataSemantic proximity Read ↗

Identification of prostate cancer bone metastasis related genes and potential therapy targets by bioinformatics and in vitro experimentsSemantic proximity Read ↗

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

Machine learning-based identification of co-expressed genes in prostate cancer and CRPC and construction of prognostic modelsSemantic proximity Read ↗

Identification and validation of a novel overall survival prediction model for immune-related genes in bone metastases of prostate cancerSemantic proximity Read ↗

Prediction of metastatic and recurrence in prostate cancer patients based on BMP-related differential gene indicators in prostate cancer bone metastasesSemantic proximity Read ↗

Machine Learning-Based Identification of Co-expressed Genes in Prostate Cancer and CRPC and Construction of Prognostic ModelsSemantic proximity Read ↗

LncRNA weighted gene co-expression network analysis reveals novel biomarkers related to prostate cancer metastasisSemantic proximity Read ↗

Gene selection driven by DNA methylation in relation to lymph node metastasis in prostate cancer and prognosis analysisSemantic proximity Read ↗

LncRNA weighted gene co-expression network analysis reveals novel biomarkers related to prostate cancer metastasisSemantic proximity Read ↗

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