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
Abstract 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 regula...
| Main Authors: | , , , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-01-01
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| Series: | Journal of Translational Medicine |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12967-022-03846-1 |
| _version_ | 1797958483575832576 |
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| author | Matteo Bauckneht Cecilia Marini Vanessa Cossu Cristina Campi Mattia Riondato Silvia Bruno Anna Maria Orengo Francesca Vitale Sonia Carta Silvia Chiola Sabrina Chiesa Alberto Miceli Francesca D’Amico Giuseppe Fornarini Carlo Terrone Michele Piana Silvia Morbelli Alessio Signori Paola Barboro Gianmario Sambuceti |
| author_facet | Matteo Bauckneht Cecilia Marini Vanessa Cossu Cristina Campi Mattia Riondato Silvia Bruno Anna Maria Orengo Francesca Vitale Sonia Carta Silvia Chiola Sabrina Chiesa Alberto Miceli Francesca D’Amico Giuseppe Fornarini Carlo Terrone Michele Piana Silvia Morbelli Alessio Signori Paola Barboro Gianmario Sambuceti |
| author_sort | Matteo Bauckneht |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-04-11T00:20:46Z |
| format | Article |
| id | doaj.art-6cd832d06026417086a422455d891893 |
| institution | Directory Open Access Journal |
| issn | 1479-5876 |
| language | English |
| last_indexed | 2024-04-11T00:20:46Z |
| publishDate | 2023-01-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Translational Medicine |
| spelling | doaj.art-6cd832d06026417086a422455d8918932023-01-08T12:19:33ZengBMCJournal of Translational Medicine1479-58762023-01-0121111210.1186/s12967-022-03846-1Gene’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 studyMatteo Bauckneht0Cecilia Marini1Vanessa Cossu2Cristina Campi3Mattia Riondato4Silvia Bruno5Anna Maria Orengo6Francesca Vitale7Sonia Carta8Silvia Chiola9Sabrina Chiesa10Alberto Miceli11Francesca D’Amico12Giuseppe Fornarini13Carlo Terrone14Michele Piana15Silvia Morbelli16Alessio Signori17Paola Barboro18Gianmario Sambuceti19Department of Health Sciences, University of GenoaNuclear Medicine Unit, IRCCS, Ospedale Policlinico San MartinoDepartment of Health Sciences, University of GenoaLISCOMP Lab, Department of Mathematics (DIMA), University of GenoaNuclear Medicine Unit, IRCCS, Ospedale Policlinico San MartinoDepartment of Experimental Medicine, Human Anatomy, University of GenoaNuclear Medicine Unit, IRCCS, Ospedale Policlinico San MartinoNuclear Medicine Unit, IRCCS, Ospedale Policlinico San MartinoNuclear Medicine Unit, IRCCS, Ospedale Policlinico San MartinoNuclear Medicine Unit, IRCCS, Ospedale Policlinico San MartinoNuclear Medicine Unit, IRCCS, Ospedale Policlinico San MartinoDepartment of Health Sciences, University of GenoaDepartment of Health Sciences, University of GenoaMedical Oncology Unit 1, IRCCS Ospedale Policlinico San MartinoDepartment of Urology, IRCCS Ospedale Policlinico San MartinoLISCOMP Lab, Department of Mathematics (DIMA), University of GenoaDepartment of Health Sciences, University of GenoaDepartment of Health Sciences, University of GenoaProteomic and Mass Spectrometry Unit, IRCCS, Ospedale Policlinico San MartinoDepartment of Health Sciences, University of GenoaAbstract 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.https://doi.org/10.1186/s12967-022-03846-1Prostate cancerGlucose metabolismProstate-specific membrane antigenPositron emission tomographyPrognosis |
| spellingShingle | Matteo Bauckneht Cecilia Marini Vanessa Cossu Cristina Campi Mattia Riondato Silvia Bruno Anna Maria Orengo Francesca Vitale Sonia Carta Silvia Chiola Sabrina Chiesa Alberto Miceli Francesca D’Amico Giuseppe Fornarini Carlo Terrone Michele Piana Silvia Morbelli Alessio Signori Paola Barboro Gianmario Sambuceti 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 Journal of Translational Medicine Prostate cancer Glucose metabolism Prostate-specific membrane antigen Positron emission tomography Prognosis |
| 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 | Prostate cancer Glucose metabolism Prostate-specific membrane antigen Positron emission tomography Prognosis |
| url | https://doi.org/10.1186/s12967-022-03846-1 |
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