Evidence map›Paper›PMID 35288573›Full record

ArticleScientific data2022

Comprehensive RNA dataset of tissue and plasma from patients with esophageal cancer or precursor lesions.

Kathleen Schoofs, Annouck Philippron, Francisco Avila Cobos, Jan Koster, Steve Lefever, Jasper Anckaert, Danny De Looze, Jo Vandesompele, Piet Pattyn, Katleen De Preter

Abstract readDataset
In one paragraph

Article in Scientific data, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
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

10 authors.

Kathleen Schoofs *Translational Oncogenomics and Bioinformatics Lab, Cancer Research Institute Ghent (CRIG), Ghent, Belgium.ORCID http://orcid.org/0000-0003-4711-3558
Annouck Philippron *Department of Biomolecular Medicine, Ghent University, Ghent, Belgium.
Francisco Avila CobosOncoRNALab, Cancer Research Institute Ghent (CRIG), Ghent, Belgium.ORCID http://orcid.org/0000-0002-8816-9243
Jan KosterDepartment of Oncogenomics, Academic Medical Center (AMC), Amsterdam, The Netherlands.ORCID http://orcid.org/0000-0002-0890-7585
Steve LefeverOncoRNALab, Cancer Research Institute Ghent (CRIG), Ghent, Belgium.ORCID http://orcid.org/0000-0002-6936-8239
Jasper AnckaertOncoRNALab, Cancer Research Institute Ghent (CRIG), Ghent, Belgium.ORCID http://orcid.org/0000-0001-8144-4639
Danny De LoozeDepartment of Gastroenterology and Hepatology, University Hospital Ghent, Ghent, Belgium.
Jo VandesompeleOncoRNALab, Cancer Research Institute Ghent (CRIG), Ghent, Belgium.ORCID http://orcid.org/0000-0001-6274-0184
Piet Pattyn *Department of Gastro-Intestinal Surgery, University Hospital Ghent, Belgium.
Katleen De Preter *Translational Oncogenomics and Bioinformatics Lab, Cancer Research Institute Ghent (CRIG), Ghent, Belgium. Katleen.DePreter@UGent.be.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the past decades, the incidence of esophageal adenocarcinoma has increased dramatically in Western populations. Better understanding of disease etiology along with the identification of novel prognostic and predictive biomarkers are urgently needed to improve the dismal survival probabilities. Here, we performed comprehensive RNA (coding and non-coding) profiling in various samples from 17 patients diagnosed with esophageal adenocarcinoma, high-grade dysplastic or non-dysplastic Barrett's esophagus. Per patient, a blood plasma sample, and a healthy and disease esophageal tissue sample were included. In total, this comprehensive dataset consists of 102 sequenced libraries from 51 samples. Based on this data, 119 expression profiles are available for three biotypes, including miRNA (51), mRNA (51) and circRNA (17). This unique resource allows for discovery of novel biomarkers and disease mechanisms, comparison of tissue and liquid biopsy profiles, integration of coding and non-coding RNA patterns, and can serve as a validation dataset in other RNA landscaping studies. Moreover, structural RNA differences can be identified in this dataset, including protein coding mutations, fusion genes, and circular RNAs.

Indexed as

AdenocarcinomaBarrett EsophagusEsophageal NeoplasmsMicroRNAsBiomarkersDisease ProgressionHumansPlasmaBiomarkersMicroRNAs

Identifiers

PMID35288573
PMCPMC8921197

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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.