Evidence map›Paper›PMID 32231885›Full record

ArticlePeerJ2020

Pan-cancer systematic identification of lncRNAs associated with cancer prognosis.

Matthew Ung, Evelien Schaafsma, Daniel Mattox, George L Wang, Chao Cheng

Abstract read
In one paragraph

Article in PeerJ, 2020. 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
–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

2 citing papers in PubMed.

  1. Article
  2. Article
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

5 authors.

Matthew UngDepartment of Molecular and Systems Biology, Dartmouth College, Hanover, NH, USA.
Evelien SchaafsmaDepartment of Molecular and Systems Biology, Dartmouth College, Hanover, NH, USA.
Daniel MattoxDepartment of Computer Science, Dartmouth College, Hanover, NH, USA.
George L WangDepartment of Molecular and Systems Biology, Dartmouth College, Hanover, NH, USA.
Chao ChengDepartment of Molecular and Systems Biology, Dartmouth College, Hanover, NH, USA.ORCID 0000-0002-5002-3417

Funding

Translational Engineering in Cancer (TEC)P30CA023108 · NCI · DARTMOUTH COLLEGE · PI Steven D Leach · 1985 to 2026
$91.3M
Quantitative Biomedical Sciences at DartmouthT32LM012204 · NLM · DARTMOUTH COLLEGE · PI TOSTESON, TOR D · 2016 to 2020
$876k
NCI NIH HHS P30 CA023108NLM NIH HHS T32 LM012204
6 · The paper itself

Abstract

backgroundThe "dark matter" of the genome harbors several non-coding RNA species including Long non-coding RNAs (lncRNAs), which have been implicated in neoplasia but remain understudied. RNA-seq has provided deep insights into the nature of lncRNAs in cancer but current RNA-seq data are rarely accompanied by longitudinal patient survival information. In contrast, a plethora of microarray studies have collected these clinical metadata that can be leveraged to identify novel associations between gene expression and clinical phenotypes.

methodsIn this study, we developed an analysis framework that computationally integrates RNA-seq and microarray data to systematically screen 9,463 lncRNAs for association with mortality risk across 20 cancer types.

resultsIn total, we identified a comprehensive list of associations between lncRNAs and patient survival and demonstrate that these prognostic lncRNAs are under selective pressure and may be functional. Our results provide valuable insights that facilitate further exploration of lncRNAs and their potential as cancer biomarkers and drug targets.

Indexed as

LncRNAMicroarrayPrognosisRNA-seqTCGA

Identifiers

PMID32231885
PMCPMC7100599

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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.