ArticleTranslational oncology2015
Novel prostate cancer biomarkers derived from autoantibody signatures.
Article in Translational oncology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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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
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 18 citations in OpenAlex.
- Autoantibodies in cancer: a systematic review of their clinical role in the most prevalent cancers.Frontiers in immunology · 2024Pooled it
- Rethinking biomarker strategy in gastric cancer immunotherapy: from tumor to host.Frontiers in immunology · 2026Review
- Prostate cancer autoantibodies - applications in diagnosis, prognosis, monitoring disease progression and immunotherapy.American journal of clinical and experimental urology · 2023Review
- Protein Modification Employing Non-Canonical Amino Acids to Prepare SUMOylation Detecting Bioconjugates.Pharmaceutics · 2022Article
- miRNAs as Therapeutic Tools and Biomarkers for Prostate Cancer.Pharmaceutics · 2021Review
- Identification of novel autoantibodies in ascites of relapsed paclitaxel-resistant gastric cancer with peritoneal metastasis using immunome protein microarrays and proteomics.Cancer biomarkers : section A of Disease markers · 2021Article
- Liquid Biopsy Potential Biomarkers in Prostate Cancer.Diagnostics (Basel, Switzerland) · 2018Article
- The Use of Biomarkers in Prostate Cancer Screening and Treatment.Reviews in urology · 2017Review
- Immunoseroproteomic Profiling in African American Men with Prostate Cancer: Evidence for an Autoantibody Response to Glycolysis and Plasminogen-Associated Proteins.Molecular & cellular proteomics : MCP · 2016Article
- Autoantibodies against oncogenic ERG protein in prostate cancer: potential use in diagnosis and prognosis in a panel with C-MYC, AMACR and HERV-K Gag.Genes & cancer · 2016Article
- Developing a model for forecasting Gleason score ≥7 in potential prostate cancer patients to reduce unnecessary prostate biopsies.International urology and nephrology · 2016Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDue to the low specificity of the prostate-specific antigen (PSA) assay and a high false positive rate, a large number of prostate cancer (PCA) biopsies are performed unnecessarily. Consequently, there is a need for new biomarkers that can identify PCA at any stage of progression while limiting the number of false positives. The use of autoantibody signature-developed biomarkers has proven to be an effective method to solve this problem.
resultsUsing T7 phage-peptide detection, we identified a panel of eight biomarkers for PCA on a training set. The estimated receiver-operating characteristic (ROC) curve had an area under the ROC curve of 0.69 when applied to the validation set. Spearman correlations were high, within 0.7 to 0.9, indicating that the biomarkers have a degree of inter-relatedness. The identified biomarkers play a role in processes such as androgen response regulation and cellular structural integrity and are proteins that are thought to play a role in prostate tumorigenesis.
conclusionsAutoantibodies against PCA can be developed as biomarkers for detecting PCA. The scores from the algorithm developed here can be used to indicate a relative high or low risk of PCA, particularly for patients with intermediate (4.0 to 10 ng/ml) PSA levels. Since most commercially available assays test for PSA or have a PSA component, this novel approach has the potential to improve diagnosis of PCA using a biologic measure independent of PSA.
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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.