ArticleOxidative medicine and cellular longevity2021
Construction of Potential Gene Expression and Regulation Networks in Prostate Cancer Using Bioinformatics Tools.
Article in Oxidative medicine and cellular longevity, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
What it found
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it, 8 citations in OpenAlex.
- Integrated identification of key immune related genes and patterns of immune infiltration in calcified aortic valvular disease: A network based meta-analysis.Frontiers in genetics · 2022Pooled it
- TGBWDriver: A Cancer Driver Gene Identification Method Based on GraphSAGE and Bidirectional Weighted Feature Aggregation.International journal of molecular sciences · 2026Article
- Article
- Proteomic-based stratification of intermediate-risk prostate cancer patients.Life science alliance · 2024Article
- Article
- Environmental Phenol and Paraben Exposure Risks and Their Potential Influence on the Gene Expression Involved in the Prognosis of Prostate Cancer.International journal of molecular sciences · 2022Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo identify the key genes involved in prostate cancer and their regulatory network.
methodsThe dataset of mRNA/miRNA transcriptome sequencing was downloaded from The Cancer Genome Atlas/the Gene Expression Omnibus database for analysis. The "edgeR" package in the R environment was used to normalize and analyze differentially expressed genes (DEGs) and miRNAs (DEmiRNAs). First, the PANTHER online tool was used to analyze the function enrichment of DEGs. Next, a protein-protein interaction (PPI) network was constructed using STRING and Cytoscape tools. Finally, miRNA-gene regulatory networks were constructed using the miRTarBase.
resultsWe identified 4339 important DEGs, of which 2145 were upregulated (Up-DEGs) and 2194 were downregulated (Down-DEGs). Functional enrichment analysis showed that the Up-DEGs were related to the immune system and the cell cycle in prostate cancer, whereas the Down-DEGs were related to the nucleic acid metabolic process and metabolism pathways. Twelve core protein clusters were found in the PPI network. Further, the constructed miRNA-gene interaction network showed that 11 downregulated miRNAs regulated 16 Up-DEGs and 22 upregulated miRNAs regulated 22 Down-DEGs.
conclusionWe identified 4339 genes and 70 miRNAs that may be involved in immune response, cell cycle, and other key pathways of the prostate cancer regulatory network. Genes such as BUB1B, ANX1A1, F5, HTR4, and MUC4 can be used as biomarkers to assist in the diagnosis and prognosis of prostate cancer.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.