Evidence map›Paper›PMID 27367417›Full record

ArticlePloS one2016

Studying Dynamic Features in Myocardial Infarction Progression by Integrating miRNA-Transcription Factor Co-Regulatory Networks and Time-Series RNA Expression Data from Peripheral Blood Mononuclear Cells.

Hongbo Shi, Guangde Zhang, Jing Wang, Zhenzhen Wang, Xiaoxia Liu, Liang Cheng, Weimin Li

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.7field-weighted citation impact, top 30% 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

4 citing papers in PubMed, 8 citations in OpenAlex.

  1. Review
  2. Article
  3. Review
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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

7 authors at 2 institutions in 1 country.

Hongbo ShiCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, 150081, PR China.
Guangde ZhangDepartment of Cardiology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, 150001, PR China.
Jing WangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, 150081, PR China.
Zhenzhen WangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, 150081, PR China.
Xiaoxia LiuDepartment of Cardiology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, 150001, PR China.
Liang ChengCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, 150081, PR China.
Weimin LiDepartment of Cardiology, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, 150001, PR China.
Harbin Medical University · CNFourth Affiliated Hospital of Harbin Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Myocardial infarction (MI) is a serious heart disease and a leading cause of mortality and morbidity worldwide. Although some molecules (genes, miRNAs and transcription factors (TFs)) associated with MI have been studied in a specific pathological context, their dynamic characteristics in gene expressions, biological functions and regulatory interactions in MI progression have not been fully elucidated to date. In the current study, we analyzed time-series RNA expression data from peripheral blood mononuclear cells. We observed that significantly differentially expressed genes were sharply up- or down-regulated in the acute phase of MI, and then changed slowly until the chronic phase. Biological functions involved at each stage of MI were identified. Additionally, dynamic miRNA-TF co-regulatory networks were constructed based on the significantly differentially expressed genes and miRNA-TF co-regulatory motifs, and the dynamic interplay of miRNAs, TFs and target genes were investigated. Finally, a new panel of candidate diagnostic biomarkers (STAT3 and ICAM1) was identified to have discriminatory capability for patients with or without MI, especially the patients with or without recurrent events. The results of the present study not only shed new light on the understanding underlying regulatory mechanisms involved in MI progression, but also contribute to the discovery of true diagnostic biomarkers for MI.

Indexed as

Disease ProgressionGene Expression ProfilingGene Regulatory NetworksBiomarkersComputational BiologyHumansLeukocytes, MononuclearMicroRNAsMyocardial InfarctionTranscription FactorsBiomarkersMicroRNAsTranscription Factors

Identifiers

PMID27367417
PMCPMC4930172
OpenAlexW2460567163

What OpenQuestion holds

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LicenceCC BY
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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.