Evidence map›Paper›PMID 28052038›Full record

ArticleOncotarget2017

Competing endogenous RNA network analysis identifies critical genes among the different breast cancer subtypes.

Juan Chen, Juan Xu, Yongsheng Li, Jinwen Zhang, Hong Chen, Jianping Lu, Zishan Wang, Xueying Zhao, Kang Xu, Yixue Li and 2 more

Open access · diamondAbstract readComparative Study
In one paragraph

Article in Oncotarget, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
2.2field-weighted citation impact, top 13% 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

18 citing papers in PubMed, 32 citations in OpenAlex.

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  15. [Differences in expression profiles of circular RNA between luminal breast cancer cells and normal breast cells].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2018
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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

12 authors at 1 institution in 1 country.

Juan ChenCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Juan XuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Yongsheng LiCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Jinwen ZhangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Hong ChenCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Jianping LuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Zishan WangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Xueying ZhaoCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Kang XuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Yixue LiCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Xia LiCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Yan ZhangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Harbin Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Although competing endogenous RNAs (ceRNAs) have been implicated in many solid tumors, their roles in breast cancer subtypes are not well understood. We therefore generated a ceRNA network for each subtype based on the significance of both, positive co-expression and the shared miRNAs, in the corresponding subtype miRNA dys-regulatory network, which was constructed based on negative regulations between differentially expressed miRNAs and targets. All four subtype ceRNA networks exhibited scale-free architecture and showed that the common ceRNAs were at the core of the networks. Furthermore, the common ceRNA hubs had greater connectivity than the subtype-specific hubs. Functional analysis of the common subtype ceRNA hubs highlighted factors involved in proliferation, MAPK signaling pathways and tube morphogenesis. Subtype-specific ceRNA hubs highlighted unique subtype-specific pathways, like the estrogen response and inflammatory pathways in the luminal subtypes or the factors involved in the coagulation process that participates in the basal-like subtype. Ultimately, we identified 29 critical subtype-specific ceRNA hubs that characterized the different breast cancer subtypes. Our study thus provides new insight into the common and specific subtype ceRNA interactions that define the different categories of breast cancer and enhances our understanding of the pathology underlying the different breast cancer subtypes, which can have prognostic and therapeutic implications in the future.

Indexed as

Gene Regulatory NetworksBiomarkers, TumorBreast NeoplasmsCell ProliferationComputational BiologyDatabases, GeneticFemaleGene Expression ProfilingGene Expression Regulation, NeoplasticGenetic Predisposition to DiseaseHumansMicroRNAsMutationPhenotypeRNA, Long NoncodingRNA, MessengerBiomarkers, TumorMicroRNAsRNA, Long NoncodingRNA, MessengerRNA, Neoplasmbreast cancercompeting endogenous RNAsmiRNAnetwork hubssubtypes

Identifiers

PMID28052038
PMCPMC5354650
OpenAlexW2561440038

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.