Evidence map›Paper›PMID 32048177›Full record

ArticleMolecular diagnosis & therapy2020

Identifying Biomarkers in Lymph Node Metastases of Esophageal Adenocarcinoma for Tumor-Targeted Imaging.

D J J M de Gouw, M Rijpkema, T J J de Bitter, V M Baart, C F M Sier, S Hernot, G M van Dam, I D Nagtegaal, B R Klarenbeek, C Rosman and 1 more

Open access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Molecular diagnosis & therapy, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
1.6field-weighted citation impact, top 17% 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

10 citing papers in PubMed, 12 citations in OpenAlex.

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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

11 authors at 5 institutions in 2 countries.

D J J M de GouwDepartment of Surgery, Radboud university medical center, Nijmegen, The Netherlands.
M RijpkemaDepartment of Radiology and Nuclear Medicine, Radboud university medical center, Nijmegen, The Netherlands.
T J J de BitterDepartment of Pathology, Radboud university medical center, Geert Grooteplein Zuid 30, 6525 GA, Nijmegen, The Netherlands.
V M BaartDepartment of Surgery, Leiden University Medical Center, Leiden, The Netherlands.
C F M SierDepartment of Surgery, Leiden University Medical Center, Leiden, The Netherlands.
S HernotLaboratory for In vivo Cellular and Molecular Imaging, Vrije Universiteit Brussel, Brussel, Belgium.
G M van DamDepartment of Surgery and department of Nuclear Medicine and Medical Imaging Center, University Medical Center Groningen, Groningen, The Netherlands.
I D NagtegaalDepartment of Pathology, Radboud university medical center, Geert Grooteplein Zuid 30, 6525 GA, Nijmegen, The Netherlands.
B R KlarenbeekDepartment of Surgery, Radboud university medical center, Nijmegen, The Netherlands.
C RosmanDepartment of Surgery, Radboud university medical center, Nijmegen, The Netherlands.
R S van der PostDepartment of Pathology, Radboud university medical center, Geert Grooteplein Zuid 30, 6525 GA, Nijmegen, The Netherlands. Chella.vanderpost@radboudumc.nl.
Radboud University Medical Center · NLLeiden University Medical Center · NLRadboud University Nijmegen · NLUniversity Medical Center Groningen · NLVrije Universiteit Brussel · BE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionTumor-targeted imaging is a promising technique for the detection of lymph node metastases (LNM) and primary tumors. It remains unclear which biomarker is the most suitable target to distinguish malignant from healthy tissue in esophageal adenocarcinoma (EAC).

objectiveWe performed an immunohistochemistry study to identify viable tumor markers for tumor-targeted imaging of EAC.

methodsWe used samples from 72 patients with EAC to determine the immunohistochemical expression of ten potential tumor biomarkers for EAC (carbonic anhydrase IX [CA-IX], carcinoembryonic antigen [CEA], hepatic growth factor receptor, epidermal growth factor receptor, epithelial membrane antigen [EMA], epithelial cell adhesion molecule [EpCAM], human epidermal growth factor receptor 2 [HER-2], urokinase plasminogen activator receptor, vascular endothelial growth factor-A [VEGF-A], and VEGF receptor 2). Immunohistochemistry was performed on tissue microarrays of LNM (n = 48), primary EACs (n = 62), fibrotic tissues (n = 11), nonmalignant lymph nodes (n = 24), and normal esophageal and gastric tissues (n = 40). Tumor marker staining was scored on intensity and percentage of positive cells.

resultsEMA and EpCAM showed strong expression in LNM (> 95%) and primary EACs (> 95%). Significant expression was also observed for LNM and EAC using VEGF-A (85 and 92%), CEA (68 and 54%), and CA-IX (4 and 34%). The other tumor biomarkers showed expression of 0-15% for LNM and primary EAC. Except for VEGF-A, nonmalignant lymph node staining was scored as slight or absent.

conclusionsHigh expression rates and correlation between LNM in EAC combined with low expression rates in healthy lymph nodes and esophagus tissues were observed for EpCAM and CEA, meaning these are promising targets for tumor-targeted imaging approaches for lymph nodes in patients with EAC.

Indexed as

AdenocarcinomaAgedAged, 80 and overBiomarkers, TumorCarbonic Anhydrase IXCarcinoembryonic AntigenCase-Control StudiesEpithelial Cell Adhesion MoleculeEsophageal NeoplasmsFemaleGene Expression Regulation, NeoplasticHumansLymphatic MetastasisMaleMiddle AgedMolecular ImagingBiomarkers, TumorCarbonic Anhydrase IXCarcinoembryonic AntigenEPCAM protein, humanEpithelial Cell Adhesion MoleculeMucin-1Vascular Endothelial Growth Factor AVEGFA protein, human

Identifiers

PMID32048177
PMCPMC7113228
OpenAlexW3006285886

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.