Evidence map›Paper›PMID 21681495›Full record

ArticleClinical & experimental metastasis2011

Localization of sporadic neuroendocrine tumors by gene expression analysis of their metastases.

Nicole Posorski, Daniel Kaemmerer, Guenther Ernst, Patricia Grabowski, Dieter Hoersch, Merten Hommann, Ferdinand von Eggeling

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Article in Clinical & experimental metastasis, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
2.4field-weighted citation impact, top 12% 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

16 citing papers in PubMed, 30 citations in OpenAlex.

  1. Review
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  9. Management of neuroendocrine tumors of unknown primary.Reviews in endocrine & metabolic disorders · 2017
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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

7 authors at 2 institutions in 1 country.

Nicole PosorskiCore Unit Chip Application, Institute of Human Genetics, UKJ, University Hospital Jena, Germany.
Daniel Kaemmerer
Guenther Ernst
Patricia Grabowski
Dieter Hoersch
Merten Hommann
Ferdinand von Eggeling
Zentralklinik Bad Berka · DEJena University Hospital · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A characteristic of human gastroenteropancreatic neuroendocrine tumors (GEP-NET) is a minute unobtrusive primary tumor which often cannot be detected by common physical examinations. It therefore remains unidentified until the tumor has spread and space-occupying metastases cause clinical symptoms leading to diagnosis. Cases in which the primary cannot be located are referred to as NET with CUP-syndrome (cancer of unknown primary syndrome). With the help of array-CGH (comparative genomic hybridization, Agilent 105K) and gene expression analysis (Agilent 44K), microdissected primaries and their metastases were compared to identify up- and down-regulated genes which can be used as a marker for tumor progression. In a next analysis step, a hierarchical clustering of 41.078 genes revealed three genes [C-type lectin domain family 13 member A (CD302), peptidylprolyl isomerase containing WD40 repeat (PPWD1) and abhydrolase domain containing 14B (ABHD14B)] which expression levels can categorize the metastases into three groups depending on the localization of their primary. Because cancer therapy is dependent on the localization of the primary, the gene expression level of these three genes are promising markers to unravel the CUP syndrome in NET.

Indexed as

Gene Expression Regulation, NeoplasticAgedAged, 80 and overFemaleGastrointestinal NeoplasmsHumansMaleMiddle AgedNeoplasm MetastasisNeoplasms, Unknown PrimaryNeuroendocrine TumorsOligonucleotide Array Sequence Analysis

Identifiers

PMID21681495
OpenAlexW2016806098

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.