biostudies-literatureBauckneht MAssociazione Italiana per la Ricerca sul CancroMinistero della Salute3https://www.ebi.ac.uk/biostudies/studies/S-EPMC9811737biostudies-literatureUnknown21(1)<h4>Background</h4>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.<h4>Methods</h4>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-PSJournal of translational medicineGene'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.PMC9811737IG 23201Morbelli SChiola SPiana MRiondato MBarboro PMiceli ACossu VChiesa SD'Amico FFornarini GSambuceti GVitale FCarta SBruno STerrone CMarini COrengo AMCampi CSignori ABauckneht MfalseGene'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.<h4>Background</h4>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.<h4>Methods</h4>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-PS2023-01-01T00:00:00Z2023 Jan2025-04-19T09:21:53.495Z2025-02-19T03:31:28.479ZS-EPMC98117373660026510.1186/s12967-022-03846-1