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

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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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03846-1.

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Most cited references 51

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KEGG (Kyoto Encyclopedia of Genes and Genomes) is a knowledge base for systematic analysis of gene functions, linking genomic information with higher order functional information. The genomic information is stored in the GENES database, which is a collection of gene catalogs for all the completely sequenced genomes and some partial genomes with up-to-date annotation of gene functions. The higher order functional information is stored in the PATHWAY database, which contains graphical representations of cellular processes, such as metabolism, membrane transport, signal transduction and cell cycle. The PATHWAY database is supplemented by a set of ortholog group tables for the information about conserved subpathways (pathway motifs), which are often encoded by positionally coupled genes on the chromosome and which are especially useful in predicting gene functions. A third database in KEGG is LIGAND for the information about chemical compounds, enzyme molecules and enzymatic reactions. KEGG provides Java graphics tools for browsing genome maps, comparing two genome maps and manipulating expression maps, as well as computational tools for sequence comparison, graph comparison and path computation. The KEGG databases are daily updated and made freely available (http://www. genome.ad.jp/kegg/).

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Leo Breiman (2001)

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Is Open Access

Cancer statistics, 2022

Rebecca Siegel, Kimberly D Miller, Hannah Fuchs … (2022)

Each year, the American Cancer Society estimates the numbers of new cancer cases and deaths in the United States and compiles the most recent data on population-based cancer occurrence and outcomes. Incidence data (through 2018) were collected by the Surveillance, Epidemiology, and End Results program; the National Program of Cancer Registries; and the North American Association of Central Cancer Registries. Mortality data (through 2019) were collected by the National Center for Health Statistics. In 2022, 1,918,030 new cancer cases and 609,360 cancer deaths are projected to occur in the United States, including approximately 350 deaths per day from lung cancer, the leading cause of cancer death. Incidence during 2014 through 2018 continued a slow increase for female breast cancer (by 0.5% annually) and remained stable for prostate cancer, despite a 4% to 6% annual increase for advanced disease since 2011. Consequently, the proportion of prostate cancer diagnosed at a distant stage increased from 3.9% to 8.2% over the past decade. In contrast, lung cancer incidence continued to decline steeply for advanced disease while rates for localized-stage increased suddenly by 4.5% annually, contributing to gains both in the proportion of localized-stage diagnoses (from 17% in 2004 to 28% in 2018) and 3-year relative survival (from 21% to 31%). Mortality patterns reflect incidence trends, with declines accelerating for lung cancer, slowing for breast cancer, and stabilizing for prostate cancer. In summary, progress has stagnated for breast and prostate cancers but strengthened for lung cancer, coinciding with changes in medical practice related to cancer screening and/or treatment. More targeted cancer control interventions and investment in improved early detection and treatment would facilitate reductions in cancer mortality.

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Author and article information

Contributors

Matteo Bauckneht:

ORCID: http://orcid.org/0000-0002-1937-9116

matteo.bauckneht@unige.it

Journal

Journal ID (nlm-ta): J Transl Med

Journal ID (iso-abbrev): J Transl Med

Title: Journal of Translational Medicine

Publisher: BioMed Central (London )

ISSN (Electronic): 1479-5876

Publication date (Electronic): 4 January 2023

Publication date PMC-release: 4 January 2023

Publication date Collection: 2023

Volume: 21

Electronic Location Identifier: 3

Affiliations

[1 ]GRID grid.5606.5, ISNI 0000 0001 2151 3065, Department of Health Sciences, , University of Genoa, ; 16132 Genoa, Italy

[2 ]GRID grid.410345.7, ISNI 0000 0004 1756 7871, Nuclear Medicine Unit, , IRCCS, Ospedale Policlinico San Martino, ; 16132 Genoa, Italy

[3 ]GRID grid.428490.3, ISNI 0000 0004 1789 9809, CNR, Institute of Molecular Bioimaging and Physiology (IBFM), ; 20054 Milan, Italy

[4 ]GRID grid.5606.5, ISNI 0000 0001 2151 3065, LISCOMP Lab, Department of Mathematics (DIMA), , University of Genoa, ; 16132 Genoa, Italy

[5 ]GRID grid.5606.5, ISNI 0000 0001 2151 3065, Department of Experimental Medicine, Human Anatomy, , University of Genoa, ; 16132 Genoa, Italy

[6 ]GRID grid.482259.0, ISNI 0000 0004 1774 9464, CNR-SPIN Genoa, ; 16132 Genoa, Italy

[7 ]GRID grid.410345.7, ISNI 0000 0004 1756 7871, Medical Oncology Unit 1, , IRCCS Ospedale Policlinico San Martino, ; 16132 Genoa, Italy

[8 ]GRID grid.410345.7, ISNI 0000 0004 1756 7871, Department of Urology, , IRCCS Ospedale Policlinico San Martino, ; 16132 Genoa, Italy

[9 ]GRID grid.5606.5, ISNI 0000 0001 2151 3065, Department of Surgical and Diagnostic Integrated Sciences (DISC), , University of Genova, ; 16132 Genoa, Italy

[10 ]GRID grid.410345.7, ISNI 0000 0004 1756 7871, Proteomic and Mass Spectrometry Unit, , IRCCS, Ospedale Policlinico San Martino, ; 16132 Genoa, Italy

Author information

Matteo Bauckneht http://orcid.org/0000-0002-1937-9116

Article

Publisher ID: 3846

DOI: 10.1186/s12967-022-03846-1

PMC ID: 9811737

PubMed ID: 36600265

SO-VID: c67a4247-1af9-4bd6-adc6-4c4008da5c9f

License:

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/. The Creative Commons Public Domain Dedication waiver ( http://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.

History

Date received : 7 September 2022

Date accepted : 23 December 2022

Funding

Funded by: FundRef http://dx.doi.org/10.13039/501100003196, Ministero della Salute;

Funded by: FundRef http://dx.doi.org/10.13039/501100005010, Associazione Italiana per la Ricerca sul Cancro;

Award ID: IG 23201

Award Recipient : Gianmario Sambuceti

Custom metadata

ScienceOpen disciplines: Medicine

Keywords: prostate cancer,glucose metabolism,prostate-specific membrane antigen,positron emission tomography,prognosis

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ScienceOpen disciplines: Medicine

Keywords: prostate cancer, glucose metabolism, prostate-specific membrane antigen, positron emission tomography, prognosis

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

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Supplementary Information

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Karger: Oncology

Most cited references 51

KEGG: kyoto encyclopedia of genes and genomes.

Random Forests

Cancer statistics, 2022

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