Evidence map›Paper›PMID 36600265›Full record

ArticleJournal of translational medicine2023

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.

Matteo Bauckneht, Cecilia Marini, Vanessa Cossu, Cristina Campi, Mattia Riondato, Silvia Bruno, Anna Maria Orengo, Francesca Vitale, Sonia Carta, Silvia Chiola and 10 more

Open access · goldAbstract read
In one paragraph

Article in Journal of translational medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
5.2field-weighted citation impact, top 4% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed, 21 citations in OpenAlex.

  1. Review
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  7. KineticEJNMMI physics · 2024
    Article
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  9. [Diagnostics (Basel, Switzerland) · 2023
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

20 authors at 4 institutions in 1 country.

Matteo BaucknehtDepartment of Health Sciences, University of Genoa, 16132, Genoa, Italy. matteo.bauckneht@unige.it.ORCID 0000-0002-1937-9116
Cecilia MariniNuclear Medicine Unit, IRCCS, Ospedale Policlinico San Martino, 16132, Genoa, Italy.
Vanessa CossuDepartment of Health Sciences, University of Genoa, 16132, Genoa, Italy.
Cristina CampiLISCOMP Lab, Department of Mathematics (DIMA), University of Genoa, 16132, Genoa, Italy.
Mattia RiondatoNuclear Medicine Unit, IRCCS, Ospedale Policlinico San Martino, 16132, Genoa, Italy.
Silvia BrunoDepartment of Experimental Medicine, Human Anatomy, University of Genoa, 16132, Genoa, Italy.
Anna Maria OrengoNuclear Medicine Unit, IRCCS, Ospedale Policlinico San Martino, 16132, Genoa, Italy.
Francesca VitaleNuclear Medicine Unit, IRCCS, Ospedale Policlinico San Martino, 16132, Genoa, Italy.
Sonia CartaNuclear Medicine Unit, IRCCS, Ospedale Policlinico San Martino, 16132, Genoa, Italy.
Silvia ChiolaNuclear Medicine Unit, IRCCS, Ospedale Policlinico San Martino, 16132, Genoa, Italy.
Sabrina ChiesaNuclear Medicine Unit, IRCCS, Ospedale Policlinico San Martino, 16132, Genoa, Italy.
Alberto MiceliDepartment of Health Sciences, University of Genoa, 16132, Genoa, Italy.
Francesca D'AmicoDepartment of Health Sciences, University of Genoa, 16132, Genoa, Italy.
Giuseppe FornariniMedical Oncology Unit 1, IRCCS Ospedale Policlinico San Martino, 16132, Genoa, Italy.
Carlo TerroneDepartment of Urology, IRCCS Ospedale Policlinico San Martino, 16132, Genoa, Italy.
Michele PianaLISCOMP Lab, Department of Mathematics (DIMA), University of Genoa, 16132, Genoa, Italy.
Silvia MorbelliDepartment of Health Sciences, University of Genoa, 16132, Genoa, Italy.
Alessio SignoriDepartment of Health Sciences, University of Genoa, 16132, Genoa, Italy.
Paola BarboroProteomic and Mass Spectrometry Unit, IRCCS, Ospedale Policlinico San Martino, 16132, Genoa, Italy.
Gianmario SambucetiDepartment of Health Sciences, University of Genoa, 16132, Genoa, Italy.
Ospedale Policlinico San Martino · ITUniversity of Genoa · ITInstitute of Molecular Bioimaging and Physiology · ITSuperconducting and other Innovative Materials and Devices Institute · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPositron 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.

methodsmRNA 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.

resultsML 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).

conclusionsThe 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.

Indexed as

Fluorodeoxyglucose F18Positron-Emission TomographyProstatic NeoplasmsAntigens, SurfaceGlucoseGlutamate Carboxypeptidase IIHumansMachine LearningMaleProstateAntigens, SurfaceFluorodeoxyglucose F18FOLH1 protein, humanGlucoseGlutamate Carboxypeptidase IIGlucose metabolismPositron emission tomographyPrognosisProstate cancerProstate-specific membrane antigen

Identifiers

PMID36600265
PMCPMC9811737
OpenAlexW4313546499

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LicenceCC BY
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Registered trials

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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.