ArticleNature communications2016
Targeted proteomics identifies liquid-biopsy signatures for extracapsular prostate cancer.
Article in Nature communications, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 68 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
68 citing papers in PubMed, 128 citations in OpenAlex.
- Evaluation of FASP for Mass Spectrometry-Based Untargeted Metabolomics Analysis of Urine Samples.Journal of proteome research · 2026Article
- Development and validation of a noninvasive machine learning model using urinary extracellular vesicle physical parameters for prostate cancer diagnosis.Scientific reports · 2026Article
- Extracellular Matrix-Associated Biomarkers for Hepatocellular Carcinoma: Insights From Machine Learning and Single-Cell Analysis.International journal of genomics · 2026Article
- Prostate cancer diagnosis using sensitive and sophisticated machine learning classifiers based on non-invasive urinary RNA biomarkers (PCASSO).Scientific reports · 2025Article
- Evolving stratification and biomarker discovery in cancer research with technological advancement of proteomics: 35 years and counting.Bioscience reports · 2025Review
- The evolution of analytical techniques for multiplex analysis of protein biomarkers.Expert review of proteomics · 2025Review
- A field resource for the glioma cerebrospinal fluid proteome: Impacts of resection and location on biomarker discovery.Neuro-oncology · 2025Article
- Emerging Trends in Neuroblastoma Diagnosis, Therapeutics, and Research.Molecular neurobiology · 2025Review
- PCLSurv: a prototypical contrastive learning-based multi-omics data integration model for cancer survival prediction.Briefings in bioinformatics · 2025Article
- A prognostic model for highly aggressive prostate cancer using interpretable machine learning techniques.Frontiers in medicine · 2025Article
- Recent progress in prompt molecular detection of liquid biopsy using Cas enzymes: innovative approaches for cancer diagnosis and analysis.Journal of translational medicine · 2024Review
- Recent developments in mass-spectrometry-based targeted proteomics of clinical cancer biomarkers.Clinical proteomics · 2024Review
- Screening and identification of serum exosomal protein ZNF587B in liquid biopsy for ovarian cancer diagnosis.American journal of cancer research · 2024Article
- Multiplexed quantitative proteomics in prostate cancer biomarker development.Advances in cancer research · 2024Review
- Development of a predictive model to distinguish prostate cancer from benign prostatic hyperplasia by integrating serum glycoproteomics and clinical variables.Clinical proteomics · 2023Article
- Review
- Data-Independent Acquisition Mass Spectrometry of EPS-Urine Coupled to Machine Learning: A Predictive Model for Prostate Cancer.ACS omega · 2023Article
- Enhancing clinical potential of liquid biopsy through a multi-omic approach: A systematic review.Frontiers in genetics · 2023Review
- Review
- CSF proteome profiling across the Alzheimer's disease spectrum reflects the multifactorial nature of the disease and identifies specific biomarker panels.Nature aging · 2022Article
8 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors at 5 institutions in 2 countries.
Funding
Abstract
Biomarkers are rapidly gaining importance in personalized medicine. Although numerous molecular signatures have been developed over the past decade, there is a lack of overlap and many biomarkers fail to validate in independent patient cohorts and hence are not useful for clinical application. For these reasons, identification of novel and robust biomarkers remains a formidable challenge. We combine targeted proteomics with computational biology to discover robust proteomic signatures for prostate cancer. Quantitative proteomics conducted in expressed prostatic secretions from men with extraprostatic and organ-confined prostate cancers identified 133 differentially expressed proteins. Using synthetic peptides, we evaluate them by targeted proteomics in a 74-patient cohort of expressed prostatic secretions in urine. We quantify a panel of 34 candidates in an independent 207-patient cohort. We apply machine-learning approaches to develop clinical predictive models for prostate cancer diagnosis and prognosis. Our results demonstrate that computationally guided proteomics can discover highly accurate non-invasive biomarkers.
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What OpenQuestion holds
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.