Evidence map›Paper›PMID 25867756›Full record

ArticleScientific reports2015

MiRNA and TF co-regulatory network analysis for the pathology and recurrence of myocardial infarction.

Ying Lin, Vusumuzi Leroy Sibanda, Hong-Mei Zhang, Hui Hu, Hui Liu, An-Yuan Guo

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed
0.9field-weighted citation impact, top 27% 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

21 citing papers in PubMed, 44 citations in OpenAlex.

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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

6 authors at 1 institution in 1 country.

Ying LinHubei Bioinformatics &Molecular Imaging Key Laboratory, Department of Biomedical Engineering, Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China.
Vusumuzi Leroy SibandaHubei Bioinformatics &Molecular Imaging Key Laboratory, Department of Biomedical Engineering, Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China.
Hong-Mei ZhangHubei Bioinformatics &Molecular Imaging Key Laboratory, Department of Biomedical Engineering, Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China.
Hui HuHubei Bioinformatics &Molecular Imaging Key Laboratory, Department of Biomedical Engineering, Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China.
Hui LiuHubei Bioinformatics &Molecular Imaging Key Laboratory, Department of Biomedical Engineering, Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China.
An-Yuan GuoHubei Bioinformatics &Molecular Imaging Key Laboratory, Department of Biomedical Engineering, Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China.
Huazhong University of Science and Technology · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Myocardial infarction (MI) is a leading cause of death in the world and many genes are involved in it. Transcription factor (TFs) and microRNAs (miRNAs) are key regulators of gene expression. We hypothesized that miRNAs and TFs might play combinatory regulatory roles in MI. After collecting MI candidate genes and miRNAs from various resources, we constructed a comprehensive MI-specific miRNA-TF co-regulatory network by integrating predicted and experimentally validated TF and miRNA targets. We found some hub nodes (e.g. miR-16 and miR-26) in this network are important regulators, and the network can be severed as a bridge to interpret the associations of previous results, which is shown by the case of miR-29 in this study. We also constructed a regulatory network for MI recurrence and found several important genes (e.g. DAB2, BMP6, miR-320 and miR-103), the abnormal expressions of which may be potential regulatory mechanisms and markers of MI recurrence. At last we proposed a cellular model to discuss major TF and miRNA regulators with signaling pathways in MI. This study provides more details on gene expression regulation and regulators involved in MI progression and recurrence. It also linked up and interpreted many previous results.

Indexed as

Gene Expression RegulationGene Regulatory NetworksAnimalsComputational BiologyDatabases, GeneticHumansMicroRNAsMyocardial InfarctionRecurrenceReproducibility of ResultsRNA InterferenceRNA, MessengerSignal TransductionTranscription FactorsMicroRNAsMIRN29a microRNA, humanRNA, MessengerTranscription Factors

Identifiers

PMID25867756
PMCPMC4394890
OpenAlexW2009850919

What OpenQuestion holds

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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.