Evidence map›Paper›PMID 29416676›Full record

ArticleOncotarget2018

Long non-coding RNA

Xin Chen, Chao Yang, Shengli Xie, Edwin Cheung

Open access · diamondAbstract read
In one paragraph

Article in Oncotarget, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 1 pooled it
2.2field-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

20 citing papers in PubMed, 1 synthesis or guideline pooled it, 39 citations in OpenAlex.

  1. Pooled it
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  5. Review
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  7. Article
  8. The Non-Coding RNAInternational journal of molecular sciences · 2020
    Review
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  18. Long Non-Coding RNA ZFAS1 as a Novel Potential Biomarker for Predicting the Prognosis of Thyroid Cancer.Medical science monitor : international medical journal of experimental and clinical research · 2019
    Article
  19. Article
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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

4 authors at 2 institutions in 2 countries.

Xin ChenGuangdong Key Laboratory of IoT Information Technology, School of Automation, Guangdong University of Technology, Guangzhou, China.
Chao YangGuangdong Key Laboratory of IoT Information Technology, School of Automation, Guangdong University of Technology, Guangzhou, China.
Shengli XieGuangdong Key Laboratory of IoT Information Technology, School of Automation, Guangdong University of Technology, Guangzhou, China.
Edwin CheungFaculty of Health Sciences, University of Macau, Macau, China.
Guangdong University of Technology · CNUniversity of Macau · MO

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Identification of prognostic biomarkers helps facilitate the prediction of patient outcomes as well as guide treatments. Accumulating evidence now suggests that long non-coding RNAs (lncRNAs) play key roles in tumor progression with diagnostic and prognostic values. However, little is known about the biological functions of lncRNAs and how they contribute to the pathogenesis of cancer. Herein, we performed weighted correlation network analysis (WGCNA) on 380 RNA-seq samples from prostate cancer patients to create networks comprising of microRNAs, lncRNAs, and protein-coding genes. Our analysis revealed expression modules that associated with pathological parameters. More importantly, we identified a gene module that is involved in protein translation and is associated with patient survival. In this gene module, we explored the regulation axis involving

Indexed as

co-expressionlncRNAnetworkprognostic markerprostate cancer

Identifiers

PMID29416676
PMCPMC5787418
OpenAlexW2773381134

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.