Evidence map›Paper›PMID 32428028›Full record

ArticlePLoS computational biology2020

Dynamical network analysis reveals key microRNAs in progressive stages of lung cancer.

Chao Kong, Yu-Xiang Yao, Zhi-Tong Bing, Bing-Hui Guo, Liang Huang, Zi-Gang Huang, Ying-Cheng Lai

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.6field-weighted citation impact, top 39% 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

1 citing paper in PubMed, 8 citations in OpenAlex.

  1. 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

7 authors at 5 institutions in 2 countries.

Chao KongThe Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi, P. R. China.ORCID 0000-0003-2685-797X
Yu-Xiang YaoInstitute of Computational Physics and Complex Systems, School of Physical Science and Technology, Lanzhou University, Lanzhou, China.
Zhi-Tong BingEvidence Based Medicine Center, School of Basic Medical Science of Lanzhou University, Lanzhou, China.
Bing-Hui GuoBeijing Advanced Innovation Center for Big Data and Brain Computing, LMIB and School of Mathematics and System Sciences, Beihang University, Beijing, China.ORCID 0000-0002-0540-3779
Liang HuangInstitute of Computational Physics and Complex Systems, School of Physical Science and Technology, Lanzhou University, Lanzhou, China.ORCID 0000-0002-3346-339X
Zi-Gang HuangThe Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi, P. R. China.ORCID 0000-0001-9648-3067
Ying-Cheng LaiSchool of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, Arizona, United States of America.ORCID 0000-0002-0723-733X
Lanzhou University · CNMinistry of Civil Affairs · CNArizona State University · USBeihang University · CNChinese Academy of Sciences · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Non-coding RNAs are fundamental to the competing endogenous RNA (CeRNA) hypothesis in oncology. Previous work focused on static CeRNA networks. We construct and analyze CeRNA networks for four sequential stages of lung adenocarcinoma (LUAD) based on multi-omics data of long non-coding RNAs (lncRNAs), microRNAs and mRNAs. We find that the networks possess a two-level bipartite structure: common competing endogenous network (CCEN) composed of an invariant set of microRNAs over all the stages and stage-dependent, unique competing endogenous networks (UCENs). A systematic enrichment analysis of the pathways of the mRNAs in CCEN reveals that they are strongly associated with cancer development. We also find that the microRNA-linked mRNAs from UCENs have a higher enrichment efficiency. A key finding is six microRNAs from CCEN that impact patient survival at all stages, and four microRNAs that affect the survival from a specific stage. The ten microRNAs can then serve as potential biomarkers and prognostic tools for LUAD.

Indexed as

Adenocarcinoma of LungBiomarkers, TumorComputational BiologyDatabases, GeneticDisease ProgressionGene Expression ProfilingGene Expression Regulation, NeoplasticGene Regulatory NetworksHumansKaplan-Meier EstimateLung NeoplasmsMicroRNAsPrognosisRNA, Long NoncodingRNA, MessengerTranscriptomeBiomarkers, TumorMicroRNAsRNA, Long NoncodingRNA, Messenger

Identifiers

PMID32428028
PMCPMC7295246
OpenAlexW3028522379

What OpenQuestion holds

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LicenceCC BY
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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.