ArticleMolecular medicine reports2020
Identification of aberrantly methylated‑differentially expressed genes and gene ontology in prostate cancer.
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 10 papers.
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Who cites it
10 citing papers in PubMed, 11 citations in OpenAlex.
- The Years 2015-2025 as a Prospective Decade for the Identification of Specific Methylation Biomarkers of Prostate Cancer.Biomolecules · 2025Review
- Exploring the role of hepsin in prostate cancer: bioinformatics, molecular Docking and molecular dynamics simulations.Discover oncology · 2025Article
- Uncovering chikungunya virus-encoded miRNAs and host-specific targeted genes associated with antiviral immune responses: an integrated bioinformatics approach.Scientific reports · 2024Article
- Risk predictions of hospital-acquired pressure injury in the intensive care unit based on a machine learning algorithm.International wound journal · 2023Article
- Article
- Review
- Ontologies and Knowledge Graphs in Oncology Research.Cancers · 2022Review
- DNA Methylation Variation Is Identified in Monozygotic Twins Discordant for Non-syndromic Cleft Lip and Palate.Frontiers in cell and developmental biology · 2021Article
- Comprehensive Analysis of Expression Regulation for RNA m6A Regulators With Clinical Significance in Human Cancers.Frontiers in oncology · 2021Article
- Identification of Novel Biomarkers Associated With the Prognosis and Potential Pathogenesis of Breast Cancer via Integrated Bioinformatics Analysis.Technology in cancer research & treatmentArticle
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Prostate cancer (PCa) is the most frequent urological malignancy in men worldwide. DNA methylation has an essential role in the etiology and pathogenesis of PCa. The purpose of the present study was to identify the aberrantly methylated‑differentially expressed genes and to determine their potential roles in PCa. The important node genes identified were screened by integrated analysis. Gene expression microarrays and gene methylation microarrays were downloaded and aberrantly methylated‑differentially expressed genes were obtained. Enrichment analysis and protein‑protein interactions (PPI) were obtained, their interactive and visual networks were created, and the node genes in the PPI network were validated. A total of 105 hypomethylation‑high expression genes and 561 hypermethylation‑low expression genes along with their biological processes were identified. The top 10 node genes obtained from the PPI network were identified for each of the two gene groups. The methylation and gene expression status of node genes in TCGA database, GEPIA tool, and the HPA database were generally consistent with those of our results. In conclusion, the present study identified 20 aberrantly methylated‑differentially expressed genes in PCa by combining bioinformatics analyses of gene expression and gene methylation microarrays, and concurrently, the survival of these genes was analyzed. Notably, methylation is a reversible biological process, which makes it of great biological significance for the diagnosis and treatment of prostate cancer using bioinformatics technology to determine abnormal methylation gene markers. The present study provided novel therapeutic targets for the treatment of PCa.
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