Evidence map›Paper›PMID 37993951›Full record

ArticleBiology direct2023

Identifying the driver miRNAs with somatic copy number alterations driving dysregulated ceRNA networks in cancers.

Renjie Dou, Shaobo Kang, Huan Yang, Wanmei Zhang, Yijing Zhang, Yuanyuan Liu, Yanyan Ping, Bo Pang

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

Article in Biology direct, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.5field-weighted citation impact, top 26% 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

2 citing papers in PubMed, 2 citations in OpenAlex.

  1. 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

8 authors at 1 institution in 1 country.

Renjie Dou *College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang, China.
Shaobo Kang *College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang, China.
Huan Yang *College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang, China.
Wanmei ZhangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang, China.
Yijing ZhangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang, China.
Yuanyuan LiuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang, China.
Yanyan PingCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang, China. pingyanyan@hrbmu.edu.cn.
Bo PangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang, China. pangbo@ems.hrbmu.edu.cn.
Harbin Medical University · CN

Funding

Heilongjiang Postdoctoral Scientific Research Developmental Fund LBH-Q20147National Natural Science Foundation of China 32170675, 82173321Special funds for the construction of higher education in Heilongjiang Province UNPYSCT-2018068
6 · The paper itself

Abstract

backgroundMicroRNAs (miRNAs) play critical roles in cancer initiation and progression, which were critical components to maintain the dynamic balance of competing endogenous RNA (ceRNA) networks. Somatic copy number alterations (SCNAs) in the cancer genome could disturb the transcriptome level of miRNA to deregulate this balance. However, the driving effects of SCNAs of miRNAs were insufficiently understood.

methodsIn this study, we proposed a method to dissect the functional roles of miRNAs under different copy number states and identify driver miRNAs by integrating miRNA SCNAs profile, miRNA-target relationships and expression profiles of miRNA, mRNA and lncRNA.

resultsApplying our method to 813 TCGA breast cancer (BRCA) samples, we identified 29 driver miRNAs whose SCNAs significantly and concordantly regulated their own expression levels and further inversely dysregulated expression levels of their targets or disturbed the miRNA-target networks they directly involved. Based on miRNA-target networks, we further constructed dynamic ceRNA networks driven by driver SCNAs of miRNAs and identified three different patterns of SCNA interference in the miRNA-mediated dynamic ceRNA networks. Survival analysis of driver miRNAs showed that high-level amplifications of four driver miRNAs (including has-miR-30d-3p, has-mir-30b-5p, has-miR-30d-5p and has-miR-151a-3p) in 8q24 characterized a new BRCA subtype with poor prognosis and contributed to the dysfunction of cancer-associated hallmarks in a complementary way. The SCNAs of driver miRNAs across different cancer types contributed to the cancer development by dysregulating different components of the same cancer hallmarks, suggesting the cancer specificity of driver miRNA.

conclusionsThese results demonstrate the efficacy of our method in identifying driver miRNAs and elucidating their functional roles driven by endogenous SCNAs, which is useful for interpreting cancer genomes and pathogenic mechanisms.

Indexed as

Breast NeoplasmsMicroRNAsRNA, Long NoncodingDNA Copy Number VariationsFemaleGene Expression Regulation, NeoplasticGene Regulatory NetworksHumansTranscriptomeMicroRNAsRNA, Long NoncodingceRNA networkDriver miRNAsDysregulationPrognosisSomatic copy number alteration

Identifiers

PMID37993951
PMCPMC10666415
OpenAlexW4388895891

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LicenceCC BY
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Registered trials

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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.