ArticleJournal of translational medicine2022
Identification of characteristic metabolic panels for different stages of prostate cancer by
Article in Journal of translational medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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16 citing papers in PubMed, 1 synthesis or guideline pooled it, 24 citations in OpenAlex.
- Pre-diagnostic circulating untargeted metabolomics and risk of overall and clinically significant prostate cancer: a systematic review and meta-analysis.British journal of cancer · 2026Pooled it
- Identification of biomarkers for non-invasive diagnosis and risk stratification in prostate cancer using NMR-based metabolomics and machine learning.NPJ precision oncology · 2026Article
- Discovery of Novel NMR-Based Biomarkers and Interpretable Machine Learning Models for Risk Prediction of Rheumatoid Arthritis.Metabolites · 2026Article
- Epigenetic alterations of AKT1 orchestrate a metabolic reprogramming in advanced lipedema: translational insights from an integrated multi-omics study.Journal of translational medicine · 2026Article
- Metabolic profiling of human melanoma cell lines with high and low metastatic capacity by 1H-NMR spectroscopy.PloS one · 2026Article
- Evaluating Differential Metabolic Profiles by Prostate Cancer Risk Among Prostate Cancer Patients.Metabolites · 2025Article
- Entabolons: How Metabolites Modify the Biochemical Function of Proteins and Cause the Correlated Behavior of Proteins in Pathways.Journal of chemical information and modeling · 2025Review
- The causal effect of serum amino acids on the risk of prostate cancer: a two-sample mendelian randomization study.Scientific reports · 2024Article
- Brucine Suppresses Malignant Progression of Prostate Cancer by Decreasing Sarcosine Accumulation via Downregulation of GNMT in the Glycine/sarcosine Metabolic Pathway.Cell biochemistry and biophysics · 2024Article
- Integrative Metabolomic Analysis of Serum and Selected Serum Exosomal microRNA in Metastatic Castration-Resistant Prostate Cancer.International journal of molecular sciences · 2024Article
- Plasma metabolomics profiling of 580 patients from an Early Detection Research Network prostate cancer cohort.Scientific data · 2023Article
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- FASN multi-omic characterization reveals metabolic heterogeneity in pancreatic and prostate adenocarcinoma.Journal of translational medicine · 2023Article
- Implications of the Essential Role of Small Molecule Ligand Binding Pockets in Protein-Protein Interactions.The journal of physical chemistry. B · 2022Article
- Prostate cancer in omics era.Cancer cell international · 2022Article
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundProstate cancer (PCa) is the second most prevalent cancer in males worldwide, yet detecting PCa and its metastases remains a major challenging task in clinical research setups. The present study aimed to characterize the metabolic changes underlying the PCa progression and investigate the efficacy of related metabolic panels for an accurate PCa assessment.
methodsIn the present study, 75 PCa subjects, 62 PCa patients with bone metastasis (PCaB), and 50 benign prostatic hyperplasia (BPH) patients were enrolled, and we performed a cross-sectional metabolomics analysis of serum samples collected from these subjects using a
resultsMultivariate analysis revealed that BPH, PCa, and PCaB groups showed distinct metabolic divisions, while univariate statistics integrated with variable importance in the projection (VIP) scores identified a differential metabolite series, which included energy, amino acid, and ketone body metabolism. Herein, we identified a series of characteristic serum metabolic changes, including decreased trends of 3-HB and acetone as well as elevated trends of alanine in PCa patients compared with BPH subjects, while increased levels of 3-HB and acetone as well as decreased levels of alanine in PCaB patients compared with PCa. Additionally, our results also revealed the metabolic panels of discriminant metabolites coupled with the clinical parameters (age and body mass index) for discrimination between PCa and BPH, PCaB and BPH, PCaB and PCa achieved the AUC values of 0.828, 0.917, and 0.872, respectively.
conclusionsOverall, our study gave successful discrimination of BPH, PCa and PCaB, and we characterized the potential metabolic alterations involved in the PCa progression and its metastases, including 3-HB, acetone and alanine. The defined biomarker panels could be employed to aid in the diagnosis and classification of PCa in clinical practice.
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