Evidence map›Paper›PMID 31725681›Full record

Observational studyMedicine2019

Identification of breast cancer-related circRNAs by analysis of microarray and RNA-sequencing data: An observational study.

Chun-Hua Zhao, Le Qu, Hui Zhang, Rui Qu

Abstract readObservational Study
In one paragraph

Observational study in Medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 1 pooled it
–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

17 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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.

Chun-Hua ZhaoTechnology Department.
Le QuClinical Laboratory, Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, Jiangsu, China.
Hui ZhangClinical Laboratory, Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, Jiangsu, China.
Rui QuClinical Laboratory, Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, Jiangsu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAn increasing number of studies indicate that circular RNAs (circRNAs) participate in tumorigenesis. The aim of this study was to elucidate the regulatory mechanisms of circRNAs in breast cancer based on the construction of the circRNA-related ceRNA network.

methodsThe expression profiles of circRNAs, miRNAs, and mRNAs were obtained from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. A ceRNA network was constructed by Cytoscape. The interactions among proteins were analyzed using the STRING database, and hub genes were extracted using the cytoHubba application. The functions of the differentially expressed mRNAs (DEmRNAs) were analyzed by the Kyoto Encyclopedia of Gene and Genomes (KEGG) and the Gene Ontology (GO) database.

resultsIn total, 7 differentially expressed circRNAs (DEcircRNAs), 27 differentially expressed miRNAs (DEmiRNAs), and 102 DEmRNAs were selected for the construction of the ceRNA network of breast cancer. We established a protein-protein interaction network and identified 6 hub genes. Then, a circRNA-miRNA-hub gene regulatory module was established based on 2 DEcircRNAs, 2 DEmiRNAs, and 2 DEmRNAs. GO and KEGG pathway analyses indicated the possible association of DEmRNAs with breast cancer onset and progression.

conclusionsThe circRNA hsa_circ_0000519 is likely critical in the pathogenesis of breast cancer and may serve as a future therapeutic biomarker.

Indexed as

Breast NeoplasmsFemaleHumansMicroRNAsProtein Interaction MapsRNA, CircularRNA, MessengerSequence Analysis, RNAMicroRNAsRNA, CircularRNA, Messenger

Identifiers

PMID31725681
PMCPMC6867785

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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.