Evidence map›Paper›PMID 30588106›Full record

ArticleCancer management and research2019

The construction and analysis of ceRNA networks in invasive breast cancer: a study based on The Cancer Genome Atlas.

Chundi Gao, Huayao Li, Jing Zhuang, HongXiu Zhang, Kejia Wang, Jing Yang, Cun Liu, Lijuan Liu, Chao Zhou, Changgang Sun

Abstract read
In one paragraph

Article in Cancer management and research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.

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32citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

32 citing papers in PubMed.

  1. Article
  2. Article
  3. LncRNA LINC00466 Promotes the Progression of Breast CancerCurrent pharmaceutical biotechnology · 2025
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  8. The Immune-related ceRNA Network in Prognosis of Cervical Cancer.Asian Pacific journal of cancer prevention : APJCP · 2022
    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

10 authors.

Chundi GaoCollege of First Clinical Medicine, Shandong University of Traditional Chinese Medicine, Jinan 250014, People's Republic of China.
Huayao LiCollege of First Clinical Medicine, Shandong University of Traditional Chinese Medicine, Jinan 250014, People's Republic of China.
Jing ZhuangDepartment of Oncology, Weifang Traditional Chinese Hospital, Weifang 261041, People's Republic of China, scgdoctor@126.com.
HongXiu ZhangInstitute of Virology, Jinan Center for Disease Control and Prevention, Jinan 250021, People's Republic of China.
Kejia WangCollege of Basic Medicine, Qingdao University, Qingdao 266071, People's Republic of China.
Jing YangDepartment of Oncology, Weifang Traditional Chinese Hospital, Weifang 261041, People's Republic of China, scgdoctor@126.com.
Cun LiuCollege of Traditional Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan 250014, People's Republic of China.
Lijuan LiuDepartment of Oncology, Weifang Traditional Chinese Hospital, Weifang 261041, People's Republic of China, scgdoctor@126.com.
Chao ZhouDepartment of Oncology, Weifang Traditional Chinese Hospital, Weifang 261041, People's Republic of China, scgdoctor@126.com.
Changgang SunDepartment of Oncology, Weifang Traditional Chinese Hospital, Weifang 261041, People's Republic of China, scgdoctor@126.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundStudies have shown that long noncoding RNAs (lncRNAs) make up the major proportion of the ceRNA network and can regulate gene expression by competitively binding to miRNAs. This reveals the existence of an RNA-miRNA regulatory pathway and is of great biological significance. CeRNAs, as competitive endogenous RNAs, have revealed a new mechanism of interaction between RNAs. Until now, the role of lncRNA-mediated ceRNAs in breast cancer and their regulatory mechanisms have been elucidated to some extent. PURPOSE: In this study, comprehensive analysis of large-scale invasive breast cancer samples in TCGA were conducted to further explore the developmental mechanism of invasive breast cancer and the potential predictive markers for invasive breast cancer prognosis in the ceRNA network.

methodsAbnormal expression profiles of invasive breast cancer associated mRNAs, lncRNAs and miRNAs were obtained from the TCGA database. Through further alignment and prediction of target genes, an abnormal lncRNA-miRNA-mRNA ceRNA network was constructed for invasive breast cancer. Through the overall survival analysis, Identification prognostic bio-markers for invasive breast cancer patients. In addition, we used Cytoscape plug-in BinGo for the different mRNA performance functional cluster analysis.

resultsDifferential analysis revealed that 1059 lncRNAs, 86 miRNAs, and 2138 mRNAs were significantly different in invasive breast cancer samples versus normal samples. Then we construct an abnormal lncRNA-miRNA-mRNA ceRNA network for invasive breast cancer, consisting of 90 DElncRNAs, 18 DEmiRNAs and 26 DEmRNAs.Further, 4 out of 90 lncRNAs, 3 out of 26 mRNAs, and 2 out of 18 miRNAs were useful as prognostic biomarkers for invasive breast cancer patients (P value < 0.05). It is worth noting that based on the ceRNA network, we found that the LINC00466-Hsa-mir-204-

conclusionThis study provides an effective bioinformatics basis for further understanding of the molecular mechanism of invasive breast cancerand for predicting outcomes, which can guide the use of invasive breast cancerdrugs and subsequent related research.

Indexed as

bioinformaticscancer genome atlasdiagnosis and prognosis biomarkersinvasive breast cancerlncRNA-miRNA-mRNA ceRNA network

Identifiers

PMID30588106
PMCPMC6301306

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