ArticleCancer informatics2019
Transcriptomics Signature from Next-Generation Sequencing Data Reveals New Transcriptomic Biomarkers Related to Prostate Cancer.
Article in Cancer informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers, 1 of them a synthesis that pooled it.
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Who cites it
51 citing papers in PubMed, 1 synthesis or guideline pooled it, 88 citations in OpenAlex.
- Advances in sequencing and omics studies in prostate cancer: unveiling molecular pathogenesis and clinical applications.Frontiers in oncology · 2024Pooled it
- RETRACTED: Polygenic risk score and prostate specific antigen predict death from prostate cancer in men with intermediate aggressive cancer.International journal of cancer · 2026Article
- Small data, big challenges: Machine- and deep-learning strategies for data-limited drug discovery.Advanced drug delivery reviews · 2026Review
- A palmitoylation-related signature predicts prognosis and immune microenvironment features in diffuse large B-cell lymphoma.Frontiers in immunology · 2026Article
- Graph neural network-based risk stratification of prostate cancer using gene expression and SHAP interpretability.PloS one · 2026Article
- The Role of Prostaglandins as Major Inflammatory Mediators in Colorectal Cancer.International journal of molecular sciences · 2025Review
- The role of nanomedicine and artificial intelligence in cancer health care: individual applications and emerging integrations-a narrative review.Discover oncology · 2025Review
- Telomere-related gene risk model predicts prognostic and immune microenvironment alterations in prostate cancer.Scientific reports · 2025Article
- DOCK9 as a predictive biomarker linked to angiogenesis and immune response in esophageal squamous cell carcinoma.Clinical and experimental medicine · 2025Article
- Article
- Adverse events of androgen receptor pathway inhibitors in prostate cancer from real world data.PloS one · 2025Article
- Review
- Population-Specific gene expression profiles in prostate cancer: insights from Weighted Gene Co-expression Network Analysis (WGCNA).World journal of surgical oncology · 2024Article
- Identification of modules and key genes associated with breast cancer subtypes through network analysis.Scientific reports · 2024Article
- Shared Genetic Features of Psoriasis and Myocardial Infarction: Insights From a Weighted Gene Coexpression Network Analysis.Journal of the American Heart Association · 2024Article
- Structural variants in the Epb41l4a locus: TAD disruption and Nrep gene misregulation as hypothetical drivers of neurodevelopmental outcomes.Scientific reports · 2024Article
- Advances in Prostate Cancer Biomarkers and Probes.Cyborg and bionic systems (Washington, D.C.) · 2024Review
- Identification of blood protein biomarkers associated with prostate cancer risk using genetic prediction models: analysis of over 140,000 subjects.Human molecular genetics · 2023Article
- The Olfaction Ability of Medical Detection Canine to Detect Prostate Cancer From Urine Samples: Progress Captured in Systematic Review and Meta-Analysis.World journal of oncology · 2023Article
- Prognostic model of hepatocellular carcinoma based on cancer grade.World journal of clinical cases · 2023Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Prostate cancer is one of the most common types of cancer among Canadian men. Next-generation sequencing using RNA-Seq provides large amounts of data that may reveal novel and informative biomarkers. We introduce a method that uses machine learning techniques to identify transcripts that correlate with prostate cancer development and progression. We have isolated transcripts that have the potential to serve as prognostic indicators and may have tremendous value in guiding treatment decisions. Analysis of normal versus malignant prostate cancer data sets indicates differential expression of the genes HEATR5B, DDC, and GABPB1-AS1 as potential prostate cancer biomarkers. Our study also supports PTGFR, NREP, SCARNA22, DOCK9, FLVCR2, IK2F3, USP13, and CLASP1 as potential biomarkers to predict prostate cancer progression, especially between stage II and subsequent stages of the disease.
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Registered trials
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.