ArticleOncology letters2018
Identification of potential key genes and high-frequency mutant genes in prostate cancer by using RNA-Seq data.
Article in Oncology letters, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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.
The trial behind it
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
7 citing papers in PubMed, 10 citations in OpenAlex.
- A Comprehensive multi-network analysis of ceRNAs and transcription factors for papillary thyroid carcinoma diagnosis and prognosis.Scientific reports · 2025Article
- Generation of prostate cancer assembloids modeling the patient-specific tumor microenvironment.PLoS genetics · 2025Article
- Uncovering Forensic Evidence: A Path to Age Estimation through DNA Methylation.International journal of molecular sciences · 2024Review
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- Identification of Key Gene Signatures Associated With Bone Metastasis in Castration-Resistant Prostate Cancer Using Co-Expression Analysis.Frontiers in oncology · 2020Article
- Polycystins and Mechanotransduction in Human Disease.International journal of molecular sciences · 2019Review
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The aim of the present study was to identify potential key genes and single nucleotide variations (SNVs) in prostate cancer. RNA sequencing (RNA-seq) data, GSE22260, were downloaded from the Gene Expression Omnibus database, including 4 prostate cancer samples and 4 normal tissues samples. RNA-Seq reads were processed using Tophat and differentially-expressed genes (DEGs) were identified using the Cufflinks package. Gene Ontology enrichment analysis of DEGs was performed. Subsequently, Seqpos was used to identify the potential upstream regulatory elements of DEGs. SNV was analyzed using Genome Analysis Toolkit. In addition, the frequency and risk-level of mutant genes were calculated using VarioWatch. A total of 150 upregulated and 211 downregulated DEGs were selected and 25 upregulated and 17 downregulated potential upstream regulatory elements were identified, respectively. The SNV annotations of somatic mutations revealed that 65% were base transition and 35% were base transversion. At frequencies ≥2, a total of 17 mutation sites were identified. The mutation site with the highest frequency was located in the folate hydrolase 1B (
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Registered trials
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