Metabolic analysis using HR‐MAS in prostate tissue for prostate cancer diagnosis

• Published in: The Prostate 2024-05, Vol.84 (6), p.549-559
• Main Authors: Panach‐Navarrete, Jorge, González‐Marrachelli, Vannina, Morales‐Tatay, José Manuel, García‐Morata, Francisco, Sales‐Maicas, María Ángeles, Monleón‐Salvado, Daniel, Martínez‐Jabaloyas, José María
• Format: Article
• Language: English
• Subjects: biomarkers; Biopsy; Carnitine; Citrates; diagnosis; Glycerol; Glycolysis; Humans; Magnetic resonance spectroscopy; Male; Metabolic pathways; Metabolism; Metabolites; metabolomic; Metabolomics; NMR; Nuclear magnetic resonance; Phosphocholine; Phosphorylcholine; Prediction models; prostate; Prostate - pathology; Prostate cancer; Prostatic Neoplasms - pathology; Rectum; Spectrum analysis; Tricarboxylic acid cycle
• ISSN: 0270-4137; 1097-0045
• DOI: 10.1002/pros.24670

Introduction In this study we used nuclear magnetic resonance spectroscopy in prostate tissue to provide new data on potential biomarkers of prostate cancer in patients eligible for prostate biopsy. Material and Methods Core needle prostate tissue samples were obtained. After acquiring all the spectra using a Bruker Avance III DRX 600 spectrometer, tissue samples were subjected to routine histology to confirm presence or absence of prostate cancer. Univariate and multivariate analyses with metabolic and clinical variables were performed to predict the occurrence of prostate cancer. Results A total of 201 patients, were included in the study. Of all cores subjected to high‐resolution magic angle spinning (HR‐MAS) followed by standard histological study, 56 (27.8%) tested positive for carcinoma. According to HR‐MAS probe analysis, metabolic pathways such as glycolysis, the Krebs cycle, and the metabolism of different amino acids were associated with presence of prostate cancer. Metabolites detected in tissue such as citrate or glycerol‐3‐phosphocholine, together with prostate volume and suspicious rectal examination, formed a predictive model for prostate cancer in tissue with an area under the curve of 0.87, a specificity of 94%, a positive predictive value of 80% and a negative predictive value of 84%. Conclusions Metabolomics using HR‐MAS analysis can uncover a specific metabolic fingerprint of prostate cancer in prostate tissue, using a tissue core obtained by transrectal biopsy. This specific fingerprint is based on levels of citrate, glycerol‐3‐phosphocholine, glycine, carnitine, and 0‐phosphocholine. Several clinical variables, such as suspicious digital rectal examination and prostate volume, combined with these metabolites, form a predictive model to diagnose prostate cancer that has shown encouraging results.