Evidence map›Paper›PMID 34657123›Full record

ArticleCell death discovery2021

Time-ordered dysregulated ceRNA networks reveal disease progression and diagnostic biomarkers in ischemic and dilated cardiomyopathy.

Ziyi Bai, Haoran Sun, Xiuhong Li, Jie Wu, Hao Yuan, Guangde Zhang, Haixiu Yang, Hongbo Shi

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cell death discovery, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–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

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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

3 citing papers in PubMed.

  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.

Ziyi Bai *College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Haoran Sun *College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Xiuhong LiCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Jie WuLaboratory of Medical Genetics, Harbin Medical University, Harbin, China.
Hao YuanCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Guangde ZhangDepartment of Cardiology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China. zhangguangde@ems.hrbmu.edu.cn.ORCID http://orcid.org/0000-0002-6101-8461
Haixiu YangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China. yanghaixiu@ems.hrbmu.edu.cn.ORCID http://orcid.org/0000-0002-7161-1656
Hongbo ShiCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China. shihongbo@ems.hrbmu.edu.cn.ORCID http://orcid.org/0000-0002-0538-3026

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Ischemic cardiomyopathy (ICM) and dilated cardiomyopathy (DCM) are the two main causes of heart failure (HF). Despite similar clinical characteristics and common "HF pathways", ICM and DCM are expected to have different personalized treatment strategies. The underlying mechanisms of ICM and DCM have yet to be fully elucidated. The present study developed a novel computational method for identifying dysregulated long noncoding RNA (lncRNA)-microRNA (miRNA)-mRNA competing endogenous RNA (ceRNA) triplets. Time-ordered dysregulated ceRNA networks were subsequently constructed to reveal the possible disease progression of ICM and DCM based on the method. Biological functional analysis indicated that ICM and DCM had similar features during myocardial remodeling, whereas their characteristics differed during progression. Specifically, disturbance of myocardial energy metabolism may be the main characteristic during DCM progression, whereas early inflammation and response to oxygen are the characteristics that may be specific to ICM. In addition, several panels of diagnostic biomarkers for differentiating non-heart failure (NF) and ICM (NF-ICM), NF-DCM, and ICM-DCM were identified. Our study reveals biological differences during ICM and DCM progression and provides potential diagnostic biomarkers for ICM and DCM.

Identifiers

PMID34657123
PMCPMC8520530

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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.