ArticleMolecular oncology2018
Integrating biomarkers across omic platforms: an approach to improve stratification of patients with indolent and aggressive prostate cancer.
Article in Molecular oncology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Advances in sequencing and omics studies in prostate cancer: unveiling molecular pathogenesis and clinical applications.Frontiers in oncology · 2024Pooled it
- Review
- Exploring the potential causal role of IgG N-glycosylation in urological cancers: a two-sample Mendelian randomization study using European ancestry datasets.Archives of medical science : AMS · 2026Article
- Multi-omic network inference from time-series data.NPJ systems biology and applications · 2025Article
- An 11-gene glycosyltransferases-related model for the prognosis of patients with bladder urothelial carcinoma: development and validation based on TCGA and GEO datasets.Translational andrology and urology · 2024Article
- From molecular mechanisms of prostate cancer to translational applications: based on multi-omics fusion analysis and intelligent medicine.Health information science and systems · 2024Review
- Review
- High Throughput Isolation and Data Independent Acquisition Mass Spectrometry (DIA-MS) of Urinary Extracellular Vesicles to Improve Prostate Cancer Diagnosis.Molecules (Basel, Switzerland) · 2022Article
- Prostate cancer in omics era.Cancer cell international · 2022Article
- The Integration of Metabolomics with Other Omics: Insights into Understanding Prostate Cancer.Metabolites · 2022Review
- Glycoforms of human prostate-specific membrane antigen (PSMA) in human cells and prostate tissue.The Prostate · 2022Article
- Integrating Serum Biomarkers into Prediction Models for Biochemical Recurrence Following Radical Prostatectomy.Cancers · 2021Article
- Review
- Development of Parallel Reaction Monitoring Assays for the Detection of Aggressive Prostate Cancer Using Urinary Glycoproteins.Journal of proteome research · 2021Article
- Proteomic analyses identify major vault protein as a prognostic biomarker for fatal prostate cancer.Carcinogenesis · 2021Article
- Identification of predictors based on drug targets highlights accurate treatment of goserelin in breast and prostate cancer.Cell & bioscience · 2021Article
- Post-Biopsy Cell-Free DNA From Blood: An Open Window on Primary Prostate Cancer Genetics and Biology.Frontiers in oncology · 2021Article
- Associations Between Genetically Predicted Plasma N-Glycans and Prostate Cancer Risk: Analysis of Over 140,000 European Descendants.Pharmacogenomics and personalized medicine · 2021Article
- Glycosylation in Indolent, Significant and Aggressive Prostate Cancer by Automated High-ThroughputInternational journal of molecular sciences · 2020Article
- Analysis of serum glycome by lectin microarrays for prostate cancer patients - a search for aberrant glycoforms.Glycoconjugate journal · 2020Article
Corrections and comments
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Authors and funding
19 authors.
Funding
Abstract
Classifying indolent prostate cancer represents a significant clinical challenge. We investigated whether integrating data from different omic platforms could identify a biomarker panel with improved performance compared to individual platforms alone. DNA methylation, transcripts, protein and glycosylation biomarkers were assessed in a single cohort of patients treated by radical prostatectomy. Novel multiblock statistical data integration approaches were used to deal with missing data and modelled via stepwise multinomial logistic regression, or LASSO. After applying leave-one-out cross-validation to each model, the probabilistic predictions of disease type for each individual panel were aggregated to improve prediction accuracy using all available information for a given patient. Through assessment of three performance parameters of area under the curve (AUC) values, calibration and decision curve analysis, the study identified an integrated biomarker panel which predicts disease type with a high level of accuracy, with Multi AUC value of 0.91 (0.89, 0.94) and Ordinal C-Index (ORC) value of 0.94 (0.91, 0.96), which was significantly improved compared to the values for the clinical panel alone of 0.67 (0.62, 0.72) Multi AUC and 0.72 (0.67, 0.78) ORC. Biomarker integration across different omic platforms significantly improves prediction accuracy. We provide a novel multiplatform approach for the analysis, determination and performance assessment of novel panels which can be applied to other diseases. With further refinement and validation, this panel could form a tool to help inform appropriate treatment strategies impacting on patient outcome in early stage prostate cancer.
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