Evidence map›Paper›PMID 32778054›Full record

ArticleBMC cardiovascular disorders2020

Identification of key genes in atrial fibrillation using bioinformatics analysis.

Yueheng Liu, Rui Tang, Ye Zhao, Xuan Jiang, Yuchao Wang, Tianxiang Gu

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

Article in BMC cardiovascular disorders, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
0.9field-weighted citation impact, top 23% 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, 1 synthesis or guideline pooled it, 6 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Review
  4. Article
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 2 institutions in 1 country.

Yueheng LiuDepartment of Cardiac Surgery, First Affiliated Hospital, China Medical University, No.155, Nanjing Street, Heping District, Shenyang, 110001, Liaoning, China.
Rui TangDepartment of Cardiac Surgery, First Affiliated Hospital, China Medical University, No.155, Nanjing Street, Heping District, Shenyang, 110001, Liaoning, China.
Ye ZhaoDepartment of Cardiac Surgery, First Affiliated Hospital, China Medical University, No.155, Nanjing Street, Heping District, Shenyang, 110001, Liaoning, China.
Xuan JiangDepartment of Cardiac Surgery, First Affiliated Hospital, China Medical University, No.155, Nanjing Street, Heping District, Shenyang, 110001, Liaoning, China.
Yuchao WangDepartment of Cardiac Surgery, First Affiliated Hospital, China Medical University, No.155, Nanjing Street, Heping District, Shenyang, 110001, Liaoning, China.
Tianxiang GuDepartment of Cardiac Surgery, First Affiliated Hospital, China Medical University, No.155, Nanjing Street, Heping District, Shenyang, 110001, Liaoning, China. cmugtx@sina.com.ORCID 0000-0003-3443-8388
China Medical University · CNFirst Hospital of China Medical University · CN

Funding

Department of Education of Liaoning Province XLYC1802066
6 · The paper itself

Abstract

backgroundAtrial fibrillation (AF) is one of the most common arrhythmia, which brings huge burden to the individual and the society. However, the mechanism of AF is not clear. This paper aims at screening the key differentially expressed genes (DEGs) of atrial fibrillation and to construct enrichment analysis and protein-protein interaction (PPI) network analysis for these DEGs.

methodsThe datasets were collected from the Gene Expression Omnibus database to extract data of left atrial appendage (LAA) RNA of patients with or without AF in GSE79768, GSE31821, GSE115574, GSE14975 and GSE41177. Batch normalization, screening of the differential genes and gene ontology analysis were finished by R software. Reactome analysis was used for pathway analysis. STRING platform was utilized for PPI network analysis. At last, we performed reverse transcription-quantitative polymerase chain reaction (RT-qPCR) to validate the expression of key genes in 20 sinus rhythm (SR) LAA tissues and 20 AF LAA tissues.

resultsA total of 106 DEGs were screened in the merged dataset. Among these DEGs, 74 genes were up-regulated and 32 genes down-regulated. DEGs were mostly enriched in extracellular matrix organization, protein activation cascade and extracellular structure organization. In PPI network, we identified SPP1, COL5A1 and VCAN as key genes which were associated with extracellular matrix. RT-qPCR showed the same expression trend of the three key genes as in our bioinformatics analysis. The expression levels of SPP1, COL5A1 and VCAN were increased in AF tissues compared to SR tissues (P < 0.05).

conclusionAccording to the analyses which were conducted by bioinformatics tools, genes related to extracellular matrix were involved in pathology of AF and may become the possible targets for the diagnosis and treatment of AF.

Indexed as

PhenomicsAtrial FibrillationDatabases, GeneticGene Regulatory NetworksGenetic Predisposition to DiseaseHeart RateHumansPhenotypeProtein Interaction MapsAtrial fibrillationBioinformaticsMicroarrayPathophysiology

Identifiers

PMID32778054
PMCPMC7419195
OpenAlexW3048787157

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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.