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Gene’s expression underpinning the divergent predictive value of [18F]F-fluorodeoxyglucose and prostate-specific membrane antigen positron emission tomography in primary prostate cancer: a bioinformatic and experimental study
BACKGROUND: Positron Emission Tomography (PET) imaging with Prostate-Specific Membrane Antigen (PSMA) and Fluorodeoxyglucose (FDG) represent promising biomarkers for risk-stratification of Prostate Cancer (PCa). We verified whether the expression of genes encoding for PSMA and enzymes regulating FDG...
Autores principales: | , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9811737/ https://www.ncbi.nlm.nih.gov/pubmed/36600265 http://dx.doi.org/10.1186/s12967-022-03846-1 |
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author | Bauckneht, Matteo Marini, Cecilia Cossu, Vanessa Campi, Cristina Riondato, Mattia Bruno, Silvia Orengo, Anna Maria Vitale, Francesca Carta, Sonia Chiola, Silvia Chiesa, Sabrina Miceli, Alberto D’Amico, Francesca Fornarini, Giuseppe Terrone, Carlo Piana, Michele Morbelli, Silvia Signori, Alessio Barboro, Paola Sambuceti, Gianmario |
author_facet | Bauckneht, Matteo Marini, Cecilia Cossu, Vanessa Campi, Cristina Riondato, Mattia Bruno, Silvia Orengo, Anna Maria Vitale, Francesca Carta, Sonia Chiola, Silvia Chiesa, Sabrina Miceli, Alberto D’Amico, Francesca Fornarini, Giuseppe Terrone, Carlo Piana, Michele Morbelli, Silvia Signori, Alessio Barboro, Paola Sambuceti, Gianmario |
author_sort | Bauckneht, Matteo |
collection | PubMed |
description | BACKGROUND: Positron Emission Tomography (PET) imaging with Prostate-Specific Membrane Antigen (PSMA) and Fluorodeoxyglucose (FDG) represent promising biomarkers for risk-stratification of Prostate Cancer (PCa). We verified whether the expression of genes encoding for PSMA and enzymes regulating FDG cellular uptake are independent and additive prognosticators in PCa. METHODS: mRNA expression of genes involved in glucose metabolism and PSMA regulation obtained from primary PCa specimens were retrieved from open-source databases and analyzed using an integrative bioinformatics approach. Machine Learning (ML) techniques were used to create predictive Progression-Free Survival (PFS) models. Cellular models of primary PCa with different aggressiveness were used to compare [18F]F-PSMA-1007 and [18F]F-FDG uptake kinetics in vitro. Confocal microscopy, immunofluorescence staining, and quantification analyses were performed to assess the intracellular and cellular membrane PSMA expression. RESULTS: ML analyses identified a predictive functional network involving four glucose metabolism-related genes: ALDOB, CTH, PARP2, and SLC2A4. By contrast, FOLH1 expression (encoding for PSMA) did not provide any additive predictive value to the model. At a cellular level, the increase in proliferation rate and migratory potential by primary PCa cells was associated with enhanced FDG uptake and decreased PSMA retention (paralleled by the preferential intracellular localization). CONCLUSIONS: The overexpression of a functional network involving four glucose metabolism-related genes identifies a higher risk of disease progression since the earliest phases of PCa, in agreement with the acknowledged prognostic value of FDG PET imaging. By contrast, the prognostic value of PSMA PET imaging is independent of the expression of its encoding gene FOLH1. Instead, it is influenced by the protein docking to the cell membrane, regulating its accessibility to tracer binding. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12967-022-03846-1. |
format | Online Article Text |
id | pubmed-9811737 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-98117372023-01-05 Gene’s expression underpinning the divergent predictive value of [18F]F-fluorodeoxyglucose and prostate-specific membrane antigen positron emission tomography in primary prostate cancer: a bioinformatic and experimental study Bauckneht, Matteo Marini, Cecilia Cossu, Vanessa Campi, Cristina Riondato, Mattia Bruno, Silvia Orengo, Anna Maria Vitale, Francesca Carta, Sonia Chiola, Silvia Chiesa, Sabrina Miceli, Alberto D’Amico, Francesca Fornarini, Giuseppe Terrone, Carlo Piana, Michele Morbelli, Silvia Signori, Alessio Barboro, Paola Sambuceti, Gianmario J Transl Med Research BACKGROUND: Positron Emission Tomography (PET) imaging with Prostate-Specific Membrane Antigen (PSMA) and Fluorodeoxyglucose (FDG) represent promising biomarkers for risk-stratification of Prostate Cancer (PCa). We verified whether the expression of genes encoding for PSMA and enzymes regulating FDG cellular uptake are independent and additive prognosticators in PCa. METHODS: mRNA expression of genes involved in glucose metabolism and PSMA regulation obtained from primary PCa specimens were retrieved from open-source databases and analyzed using an integrative bioinformatics approach. Machine Learning (ML) techniques were used to create predictive Progression-Free Survival (PFS) models. Cellular models of primary PCa with different aggressiveness were used to compare [18F]F-PSMA-1007 and [18F]F-FDG uptake kinetics in vitro. Confocal microscopy, immunofluorescence staining, and quantification analyses were performed to assess the intracellular and cellular membrane PSMA expression. RESULTS: ML analyses identified a predictive functional network involving four glucose metabolism-related genes: ALDOB, CTH, PARP2, and SLC2A4. By contrast, FOLH1 expression (encoding for PSMA) did not provide any additive predictive value to the model. At a cellular level, the increase in proliferation rate and migratory potential by primary PCa cells was associated with enhanced FDG uptake and decreased PSMA retention (paralleled by the preferential intracellular localization). CONCLUSIONS: The overexpression of a functional network involving four glucose metabolism-related genes identifies a higher risk of disease progression since the earliest phases of PCa, in agreement with the acknowledged prognostic value of FDG PET imaging. By contrast, the prognostic value of PSMA PET imaging is independent of the expression of its encoding gene FOLH1. Instead, it is influenced by the protein docking to the cell membrane, regulating its accessibility to tracer binding. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12967-022-03846-1. BioMed Central 2023-01-04 /pmc/articles/PMC9811737/ /pubmed/36600265 http://dx.doi.org/10.1186/s12967-022-03846-1 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Bauckneht, Matteo Marini, Cecilia Cossu, Vanessa Campi, Cristina Riondato, Mattia Bruno, Silvia Orengo, Anna Maria Vitale, Francesca Carta, Sonia Chiola, Silvia Chiesa, Sabrina Miceli, Alberto D’Amico, Francesca Fornarini, Giuseppe Terrone, Carlo Piana, Michele Morbelli, Silvia Signori, Alessio Barboro, Paola Sambuceti, Gianmario Gene’s expression underpinning the divergent predictive value of [18F]F-fluorodeoxyglucose and prostate-specific membrane antigen positron emission tomography in primary prostate cancer: a bioinformatic and experimental study |
title | Gene’s expression underpinning the divergent predictive value of [18F]F-fluorodeoxyglucose and prostate-specific membrane antigen positron emission tomography in primary prostate cancer: a bioinformatic and experimental study |
title_full | Gene’s expression underpinning the divergent predictive value of [18F]F-fluorodeoxyglucose and prostate-specific membrane antigen positron emission tomography in primary prostate cancer: a bioinformatic and experimental study |
title_fullStr | Gene’s expression underpinning the divergent predictive value of [18F]F-fluorodeoxyglucose and prostate-specific membrane antigen positron emission tomography in primary prostate cancer: a bioinformatic and experimental study |
title_full_unstemmed | Gene’s expression underpinning the divergent predictive value of [18F]F-fluorodeoxyglucose and prostate-specific membrane antigen positron emission tomography in primary prostate cancer: a bioinformatic and experimental study |
title_short | Gene’s expression underpinning the divergent predictive value of [18F]F-fluorodeoxyglucose and prostate-specific membrane antigen positron emission tomography in primary prostate cancer: a bioinformatic and experimental study |
title_sort | gene’s expression underpinning the divergent predictive value of [18f]f-fluorodeoxyglucose and prostate-specific membrane antigen positron emission tomography in primary prostate cancer: a bioinformatic and experimental study |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9811737/ https://www.ncbi.nlm.nih.gov/pubmed/36600265 http://dx.doi.org/10.1186/s12967-022-03846-1 |
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