ArticleScientific reports2018
Metabolomic Prediction of Human Prostate Cancer Aggressiveness: Magnetic Resonance Spectroscopy of Histologically Benign Tissue.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed, 59 citations in OpenAlex.
- The influence of low-carbohydrate diets on the metabolic response to androgen-deprivation therapy in prostate cancer.The Prostate · 2021Trial
- Metabolomic effects of androgen deprivation therapy treatment for prostate cancer.Cancer medicine · 2020Trial
- Biomarkers for Precision Prognosis in Prostate Cancer: Imaging, Molecular, and Integrated Approaches.Cancers · 2026Review
- Urine metabolic analysis as a non-invasive method to predict biochemical recurrence in prostate cancer.Translational andrology and urology · 2025Article
- Article
- Metabolic readouts of tumor instructed normal tissues (TINT) identify aggressive prostate cancer subgroups for tailored therapy.Frontiers in molecular biosciences · 2025Article
- Untargeted metabolomics revealed urinary metabolic pattern for discriminating prostate cancer from benign prostatic hyperplasia in Chinese participants.Frontiers in oncology · 2025Article
- Deep learning-based metabolomics data study of prostate cancer.BMC bioinformatics · 2024Article
- Application value of magnetic resonance spectroscopy imaging in the diagnosis of prostate cancer.Scientific reports · 2024Article
- Review
- Article
- Metabolomic profiles of intact tissues reflect clinically relevant prostate cancer subtypes.Journal of translational medicine · 2023Article
- Rethinking our approach to cancer metabolism to deliver patient benefit.British journal of cancer · 2023Review
- Article
- Metabolomics by NMR Combined with Machine Learning to Predict Neoadjuvant Chemotherapy Response for Breast Cancer.Cancers · 2022Article
- Article
- Ex Vivo High-Resolution Magic Angle Spinning (HRMAS)Cancers · 2022Article
- Review
- Multiplatform Metabolomics Studies of Human Cancers With NMR and Mass Spectrometry Imaging.Frontiers in molecular biosciences · 2022Article
- Metabolic fingerprinting of chemotherapy-resistant prostate cancer stem cells. An untargeted metabolomic approach by liquid chromatography-mass spectrometry.Frontiers in cell and developmental biology · 2022Article
Corrections and comments
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Authors and funding
12 authors at 3 institutions in 2 countries.
Funding
Abstract
Prostate cancer alters cellular metabolism through events potentially preceding cancer morphological formation. Magnetic resonance spectroscopy (MRS)-based metabolomics of histologically-benign tissues from cancerous prostates can predict disease aggressiveness, offering clinically-translatable prognostic information. This retrospective study of 185 patients (2002-2009) included prostate tissues from prostatectomies (n = 365), benign prostatic hyperplasia (BPH) (n = 15), and biopsy cores from cancer-negative patients (n = 14). Tissues were measured with high resolution magic angle spinning (HRMAS) MRS, followed by quantitative histology using the Prognostic Grade Group (PGG) system. Metabolic profiles, measured solely from 338 of 365 histologically-benign tissues from cancerous prostates and divided into training-testing cohorts, could identify tumor grade and stage, and predict recurrence. Specifically, metabolic profiles: (1) show elevated myo-inositol, an endogenous tumor suppressor and potential mechanistic therapy target, in patients with highly-aggressive cancer, (2) identify a patient sub-group with less aggressive prostate cancer to avoid overtreatment if analysed at biopsy; and (3) subdivide the clinicopathologically indivisible PGG2 group into two distinct Kaplan-Meier recurrence groups, thereby identifying patients more at-risk for recurrence. Such findings, achievable by biopsy or prostatectomy tissue measurement, could inform treatment strategies. Metabolomics information can help transform a morphology-based diagnostic system by invoking cancer biology to improve evaluation of histologically-benign tissues in cancer environments.
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