Evidence map›Paper›PMID 34771663›Full record

ArticleCancers2021

Molecular Classification of Bladder Urothelial Carcinoma Using NanoString-Based Gene Expression Analysis.

Antonio Lopez-Beltran, Ana Blanca, Alessia Cimadamore, Rajan Gogna, Rodolfo Montironi, Liang Cheng

Abstract read
In one paragraph

Article in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
–field-weighted citation impact
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

13 citing papers in PubMed.

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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.

Antonio Lopez-BeltranDepartment of Morphological Sciences, University of Cordoba Medical School, E-14004 Cordoba, Spain.
Ana BlancaMaimonides Biomedical Research Institute of Cordoba, Department of Urology, University Hospital of Reina Sofia, E-14004 Cordoba, Spain.ORCID 0000-0002-4360-4352
Alessia CimadamoreInstitute of Pathological Anatomy and Histopathology, School of Medicine, Polytechnic University of the Marche Region, United Hospitals, 60126 Ancona, Italy.ORCID 0000-0001-5981-3514
Rajan GognaFaculty of Health and Medical Sciences, BRIC-Biotech Research & Innovation Centre, University of Copenhagen, 2200 Copenhagen, Denmark.
Rodolfo MontironiInstitute of Pathological Anatomy and Histopathology, School of Medicine, Polytechnic University of the Marche Region, United Hospitals, 60126 Ancona, Italy.ORCID 0000-0003-3938-610X
Liang ChengDepartments of Pathology and Urology, Indiana University, School of Medicine, Indianapolis, IN 46202, USA.ORCID 0000-0001-6049-5293

Funding

FIS (Ministry of Health), Madrid, Spain Grant PI17/01981
6 · The paper itself

Abstract

Molecular classification of bladder carcinoma is a relevant topic in modern bladder cancer oncology due to its potential to improve oncological outcomes. The available molecular classifications are generally based on transcriptomic profiles, generating highly diverse categories with limited correlation. Implementation of molecular classification in practice is typically limited due to the high complexity of the required technology, the elevated costs, and the limited availability of this technology worldwide. We have conducted a gene expression analysis using a four-gene panel related to luminal and basal subtypes in a series of 91 bladder cancer cases. NanoString-based gene expression analysis using typically luminal (GATA3+/KRT20+) and basal markers (KRT14+/KRT5+/GATA3low/-/KRT20low/-) classified urothelial bladder carcinoma samples as luminal, basal, and a third category (KRT14-/KRT5-/GATA3-/KRT20-), null/double negative (non-luminal/non-basal). These three categories were meaningful in terms of overall cancer-specific survival (

Indexed as

basalbladder cancerclassificationluminalmolecularmolecular taxonomyNanoString

Identifiers

PMID34771663
PMCPMC8583679

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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.