ArticleThe Prostate2010
Retrospective analysis of prostate cancer recurrence potential with tissue metabolomic profiles.
Article in The Prostate, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed, 46 citations in OpenAlex.
- Dysregulation of the urea cycle - potential targets for treatment and diagnosis in prostate cancer.Expert reviews in molecular medicine · 2026Review
- Urine metabolic analysis as a non-invasive method to predict biochemical recurrence in prostate cancer.Translational andrology and urology · 2025Article
- The Oncometabolite 2-Hydroxyglutarate Is Upregulated in Post-Prostatectomy PSA Recurrence of Prostate Cancer: A Metabolomic Analysis.Molecules (Basel, Switzerland) · 2025Article
- Targeting Glutamine Metabolism in Prostate Cancer.Frontiers in bioscience (Elite edition) · 2023Review
- Review
- Energy Metabolism-Related Gene Prognostic Index Predicts Biochemical Recurrence for Patients With Prostate Cancer Undergoing Radical Prostatectomy.Frontiers in immunology · 2022Article
- The Emerging Clinical Role of Spermine in Prostate Cancer.International journal of molecular sciences · 2021Review
- Biomarkers in Prostate Cancer Diagnosis: From Current Knowledge to the Role of Metabolomics and Exosomes.International journal of molecular sciences · 2021Review
- NMR metabolomic profiles associated with long-term risk of prostate cancer.Metabolomics : Official journal of the Metabolomic Society · 2021Article
- Molecular Characterization of Prostate Cancer with Associated Gleason Score Using Mass Spectrometry Imaging.Molecular cancer research : MCR · 2019Article
- Metabolomics Biomarkers of Prostate Cancer: A Systematic Review.Diagnostics (Basel, Switzerland) · 2019Review
- Surface-enhanced Raman spectroscopy before radical prostatectomy predicts biochemical recurrence better than CAPRA-S.International journal of nanomedicine · 2019Article
- NMR-based metabolomics studies of human prostate cancer tissue.Metabolomics : Official journal of the Metabolomic Society · 2018Review
- Profiling Prostate Cancer Therapeutic Resistance.International journal of molecular sciences · 2018Review
- Ex vivo metabolic fingerprinting identifies biomarkers predictive of prostate cancer recurrence following radical prostatectomy.British journal of cancer · 2017Article
- Applications of high-resolution magic angle spinning MRS in biomedical studies II-Human diseases.NMR in biomedicine · 2017Review
- Article
- Biomarker Discovery in Human Prostate Cancer: an Update in Metabolomics Studies.Translational oncology · 2016Review
- Metabolomic Biomarkers of Prostate Cancer: Prediction, Diagnosis, Progression, Prognosis, and Recurrence.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2016Review
- Evaluation of Cancer Metabolomics Using ex vivo High Resolution Magic Angle Spinning (HRMAS) Magnetic Resonance Spectroscopy (MRS).Metabolites · 2016Review
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Authors and funding
8 authors at 3 institutions in 2 countries.
Funding
Abstract
backgroundIn clinical care of prostate cancer patients, an improved method to assess the risk of recurrence after surgical treatment is urgently needed. We aim to retrospectively evaluate the ability of ex vivo tissue magnetic-resonance-spectroscopy-based metabolomic profiles to estimate the risk of recurrence.
methodsPCa recurrence is defined biochemically as the detection of serum PSA after radical prostatectomy. Sixteen consecutive PCa-recurrent cases, those with an initial PSA increase of 0.69 +/- 0.26 ng/ml monitored 47.7 +/- 2.6 months after prostatectomy were paired by age and Gleason score with cases without recurrence of the same pathological and clinical stages (n = 16/each). We analyzed ex vivo intact-tissue spectroscopy results from these 48 individuals at the time of prostatectomy at 14T. From these spectra, we identified the 27 most common and intense spectral metabolic regions for statistical analyses.
resultsPrincipal component analysis (PCA) on these spectral regions from cases of clinical-stage-matched groups with and without recurrence identified four pathology-related principal components. Canonical analysis of these four and the first nine principal components for cases in the two groups defined metabolomic profiles as the canonical score that can differentiate the two groups with statistical significance. By applying the coefficients from PCA and canonical analysis to the pathological-stage-matched groups, recurrence was predicted with an accuracy of 78%.
conclusionsResults indicate the potential of tissue metabolomic profiles measured with ex vivo spectroscopy to identify PCa aggressiveness in terms of cancer recurrence. With further study, this may greatly contribute to the future design of clinical strategy for personalized treatment of PCa patients.
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