Evidence map›Paper›PMID 37749499›Full record

ArticleClinical proteomics2023

Identification of potential molecular targets for the treatment of cluster 1 human pheochromocytoma and paraganglioma via comprehensive proteomic characterization.

Ondrej Vit, Pavel Talacko, Zdenek Musil, Igor Hartmann, Karel Pacak, Jiri Petrak

Open access · goldAbstract read
In one paragraph

Article in Clinical proteomics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
1.0field-weighted citation impact, top 21% 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

2 citing papers in PubMed, 4 citations in OpenAlex.

  1. Plasma proteomic profiling in dogs with pheochromocytoma.Journal of veterinary internal medicine · 2026
    Article
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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

6 authors at 3 institutions in 2 countries.

Ondrej VitBIOCEV, First Faculty of Medicine, Charles University, Vestec, 25250, Czech Republic.
Pavel TalackoProteomics Core Facility, Faculty of Science, BIOCEV, Charles University, Vestec, 25250, Czech Republic.
Zdenek MusilInstitute of Biology and Medical Genetics, First Faculty of Medicine, Charles University and General University Hospital, Prague, 12800, Czech Republic.
Igor HartmannDepartment of Urology, University Hospital Olomouc and Faculty of Medicine and Dentistry, Palacky University Olomouc, Olomouc, 77900, Czech Republic.
Karel PacakSection on Medical Neuroendocrinology, Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD, 20892, USA.
Jiri PetrakBIOCEV, First Faculty of Medicine, Charles University, Vestec, 25250, Czech Republic. jpetr@lf1.cuni.cz.
Charles University · CZEunice Kennedy Shriver National Institute of Child Health and Human Development · USUniversity Hospital Olomouc · CZ

Funding

Czech Agency for Healthcare Research AZV NV19-01-00083, NU23-01-00323European Union - Next Generation EU, Programme EXCELES National Institute of Cancer Research - LX22NPO5102Ministry of Education, Youth, and Sports of the Czech Republic (MSMT CR) UNCE/MED/016 and Cooperatio program, research area BIOLOGY
6 · The paper itself

Abstract

backgroundPheochromocytomas and paragangliomas (PPGLs) are rare neuroendocrine tumors. New drug targets and proteins that would assist sensitive PPGL imagining could improve therapy and quality of life of patients with PPGL, namely those with recurrent or metastatic disease. Using a combined proteomic strategy, we looked for such clinically relevant targets among integral membrane proteins (IMPs) upregulated on the surface of tumor cells and non-membrane druggable enzymes in PPGL.

methodsWe conducted a detailed proteomic analysis of 22 well-characterized human PPGL samples and normal chromaffin tissue from adrenal medulla. A standard quantitative proteomic analysis of tumor lysate, which provides information largely on non-membrane proteins, was accompanied by specific membrane proteome-aimed methods, namely glycopeptide enrichment using lectin-affinity, glycopeptide capture by hydrazide chemistry, and enrichment of membrane-embedded hydrophobic transmembrane segments.

resultsThe study identified 67 cell surface integral membrane proteins strongly upregulated in PPGL compared to control chromaffin tissue. We prioritized the proteins based on their already documented direct role in cancer cell growth or progression. Increased expression of the seven most promising drug targets (CD146, CD171, ANO1, CD39, ATP8A1, ACE and SLC7A1) were confirmed using specific antibodies. Our experimental strategy also provided expression data for soluble proteins. Among the druggable non-membrane enzymes upregulated in PPGL, we identified three potential drug targets (SHMT2, ARG2 and autotaxin) and verified their upregulated expression.

conclusionsApplication of a combined proteomic strategy recently presented as "Pitchfork" enabled quantitative analysis of both, membrane and non-membrane proteome, and resulted in identification of 10 potential drug targets in human PPGL. Seven membrane proteins localized on the cell surface and three non-membrane druggable enzymes proteins were identified and verified as significantly upregulated in PPGL. All the proteins have been previously shown to be upregulated in several human cancers, and play direct role in cancer progression. Marked upregulation of these proteins along with their localization and established direct roles in tumor progression make these molecules promising candidates as drug targets or proteins for sensitive PPGL imaging.

Indexed as

Drug targetsIntegral membrane proteinsMembrane proteomicsNeuroendocrine tumorsParagangliomaPheochromocytomaTherapyTumor imaging

Identifiers

PMID37749499
PMCPMC10518975
OpenAlexW4387007550

What OpenQuestion holds

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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.