ReviewBiophysical reviews2020
Metabolism of prostate cancer by magnetic resonance spectroscopy (MRS).
Review in Biophysical reviews, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 32 citations in OpenAlex.
- Tools and Technologies for Studying Cancer Metabolism.Cancer treatment and research · 2026Review
- Non-Invasive Regional Neurochemical Profiling of Zebrafish Brain Using Localized Magnetic Resonance Spectroscopy at 28.2 T.Molecules (Basel, Switzerland) · 2025Article
- Overcoming Immune Evasion in the Prostate Tumor Microenvironment: Novel Targeted Strategies to Improve Treatment Outcomes.Cancers · 2025Review
- An Update on MR Spectroscopy in Cancer Management: Advances in Instrumentation, Acquisition, and Analysis.Radiology. Imaging cancer · 2024Review
- Denoising magnetic resonance spectroscopy (MRS) data using stacked autoencoder for improving signal-to-noise ratio and speed of MRS.Medical physics · 2023Article
- Correlation between choline kinase alpha expression andScientific reports · 2023Article
- Emerging Role of Nuclear Medicine in Prostate Cancer: Current State and Future Perspectives.Cancers · 2023Review
- Imaging in translational cancer research.Cancer biology & medicine · 2022Review
- Dictionary learning compressed sensing reconstruction: pilot validation of accelerated echo planar J-resolved spectroscopic imaging in prostate cancer.Magma (New York, N.Y.) · 2022Article
- Developments in proton MR spectroscopic imaging of prostate cancer.Magma (New York, N.Y.) · 2022Review
- The Integration of Metabolomics with Other Omics: Insights into Understanding Prostate Cancer.Metabolites · 2022Review
- Change in knee cartilage components in stroke patients with genu recurvatum analysed by zero TE MR imaging.Scientific reports · 2022Article
- Deuterium Metabolic Imaging-Rediscovery of a Spectroscopic Tool.Metabolites · 2021Review
- Biophysical Reviews: a Q1 ranked journal in biophysics and structural biology.Biophysical reviews · 2020Article
- Potential of nuclear magnetic resonance metabolomics in the study of prostate cancer.Indian journal of urology : IJU : journal of the Urological Society of IndiaReview
- The Potential for Enhanced Ovarian Cancer Diagnostics Through Optimized Derivative Magnetic Resonance Spectroscopy.Technology in cancer research & treatmentArticle
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
Abstract
Understanding the metabolism of prostate cancer (PCa) is important for developing better diagnostic approaches and also for exploring new therapeutic targets. Magnetic resonance spectroscopy (MRS) techniques have been shown to be useful in the detection and quantification of metabolites. PCa illustrates metabolic phenotype, showing lower levels of citrate (Cit), a key metabolite of oxidative phosphorylation and alteration in several metabolic pathways to sustain tumor growth. Recently, dynamic nuclear polarization (DNP) studies have documented high rates of glycolysis (Warburg phenomenon) in PCa. High-throughput metabolic profiling strategies using MRS on variety of samples including intact tissues, biofluids like prostatic fluid, seminal fluid, blood plasma/sera, and urine have also played a vital role in understanding the abnormal metabolic activity of PCa patients. The enhanced analytical potential of these techniques in the detection and quantification of a large number of metabolites provides an in-depth understanding of metabolic rewiring associated with the tumorigenesis. Metabolomics analysis offers dual advantages of identification of diagnostic and predictive biomarkers as well as in understanding the altered metabolic pathways which can be targeted for inhibiting the cancer progression. This review briefly describes the potential applications of in vivo
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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.