ArticleMolecular medicine reports2020
Screening and identification of key biomarkers in prostate cancer using bioinformatics.
Article in Molecular medicine reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
6 citing papers in PubMed, 8 citations in OpenAlex.
- DiCE: differential centrality-ensemble analysis based on gene expression profiles and protein-protein interaction network.Nucleic acids research · 2025Article
- In Vitro and In Silico Evaluation of the Potential Anti-Prostate Cancer Activity ofInternational journal of molecular sciences · 2025Article
- DiCE: differential centrality-ensemble analysis based on gene expression profiles and protein-protein interaction network.bioRxiv : the preprint server for biology · 2025Article
- Identification of bicalutamide resistance-related genes and prognosis prediction in patients with prostate cancer.Frontiers in endocrinology · 2023Article
- Screening a novel six critical gene-based system of diagnostic and prognostic biomarkers in prostate adenocarcinoma patients with different clinical variables.American journal of translational research · 2022Article
- Bioinformatic analysis of dysregulated proteins in prostate cancer patients reveals putative urinary biomarkers and key biological pathways.Medical oncology (Northwood, London, England) · 2021Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Prostate cancer (PCa) is the second most common cancer amongst males worldwide. In the current study, microarray datasets GSE3325 and GSE6919 from the Gene Expression Omnibus database were screened to identify candidate genes that are associated with the progression of PCa. A total of 273 differentially expressed genes (DEGs) were identified, which included 173 downregulated genes and 100 upregulated genes, and a protein‑protein interaction network was constructed using Search Tool for the Retired of Interacting Genes. The enriched functions and pathways of the identified DEGs included cell adhesion, the negative regulation of cell proliferation, protein binding and focal adhesion. A total of 8 hub genes were identified, of which PDZ binding kinase, Krüppel‑like factor 4, collagen type XII α‑1 chain, RAP1A and RAP39B were indicated to be associated with the progression and recurrence of PCa. In conclusion, the DEGs and hub genes identified in the present study may aid in determining the molecular mechanisms associated with PCa carcinogenesis and progression.
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Registered trials
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