Evidence map›Paper›PMID 32938406›Full record

ArticleBMC cardiovascular disorders2020

Investigation of the underlying genes and mechanism of familial hypercholesterolemia through bioinformatics analysis.

Dinghui Wang, Bin Liu, Tianhua Xiong, Wenlong Yu, Qiang She

Open access · goldAbstract read
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 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
2.0field-weighted citation impact, top 13% 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

10 citing papers in PubMed, 15 citations in OpenAlex.

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  8. Dysfunctional network of hub genes in hypertrophic cardiomyopathy patients.American journal of translational research · 2022
    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

5 authors at 1 institution in 1 country.

Dinghui WangDepartment of Cardiovascular, The Second Affiliated Hospital of Chongqing Medical University, No.1 Medical College Road, Shiyou Road Street, Yuzhong District, Chongqing, 400010, People's Republic of China.
Bin LiuDepartment of Cardiovascular, The Second Affiliated Hospital of Chongqing Medical University, No.1 Medical College Road, Shiyou Road Street, Yuzhong District, Chongqing, 400010, People's Republic of China.
Tianhua XiongDepartment of Cardiovascular, The Second Affiliated Hospital of Chongqing Medical University, No.1 Medical College Road, Shiyou Road Street, Yuzhong District, Chongqing, 400010, People's Republic of China.
Wenlong YuDepartment of Cardiovascular, The Second Affiliated Hospital of Chongqing Medical University, No.1 Medical College Road, Shiyou Road Street, Yuzhong District, Chongqing, 400010, People's Republic of China.
Qiang SheDepartment of Cardiovascular, The Second Affiliated Hospital, Chongqing Medical University, 76 Linjiang Road, Chongqing, 400010, P.R. China. qshe98@cqmu.edu.cn.ORCID 0000-0001-5049-1264
Dalian Medical University · CN

Funding

Chongqing Science and Technology Commission 81770251
6 · The paper itself

Abstract

backgroundFamilial hypercholesterolemia (FH) is one of the commonest inherited metabolic disorders. Abnormally high level of low-density lipoprotein cholesterol (LDL-C) in blood leads to premature atherosclerosis onset and a high risk of cardiovascular disease (CVD). However, the specific mechanisms of the progression process are still unclear. Our study aimed to investigate the potential differently expressed genes (DEGs) and mechanism of FH using various bioinformatic tools.

methodsGSE13985 and GSE6054 were downloaded from the Gene Expression Omnibus (GEO) database for bioinformatic analysis in this study. First, limma package of R was used to identify DEGs between blood samples of patients with FH and those from healthy individuals. Then, the functional annotation of DEGs was carried out by Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and Gene Ontology (GO) analysis. Based on Search Tool for the Retrieval of Interacting Genes (STRING) tool, we constructed the Protein-Protein Interactions (PPIs) network among DEGs and mined the core genes as well.

resultsA total of 102 communal DEGs (49 up-regulated and 53 down-regulated) are identified in FH samples compared with control samples. The functional changes of DEGs are mainly associated with the focal adhere and glucagon signaling pathway. Ten genes (ITGAL, TLN1, POLR2A, CD69, GZMA, VASP, HNRNPUL1, SF1, SRRM2, ITGAV) were identified as core genes. Bioinformatic analysis showed that the core genes are mainly enriched in numerous processes related to cell adhesion, integrin-mediated signaling pathway and cell-matrix adhesion. In the transcription factor (TF) target regulating network, 219 nodes were detected, including 214 DEGs and 5 TFs (SP1, EGR3, CREB, SEF1, HOX13). In conclusion, the DEGs and hub genes identified in this study may help us understand the potential etiology of the occurrence and development of AS.

conclusionUp-regulated ITGAL, TLN1, POLR2A, VASP, HNRNPUL1, SF1, SRRM2, and down-regulated CD69, GZMA and ITGAV performed important promotional effects for the formation of atherosclerotic plaques those suffering from FH. Moreover, SP1, EGR3, CREB, SEF1 and HOX13 were the potential transcription factors for DEGs and could serve as underlying targets for AS rupture prevention. These findings provide a theoretical basis for us to understand the potential etiology of the occurrence and development of AS in FH patients and we may be able to find potential diagnostic and therapeutic targets.

Indexed as

Computational BiologyGene Regulatory NetworksAtherosclerosisCase-Control StudiesCholesterol, LDLDatabases, GeneticDisease ProgressionGene Expression RegulationGenetic Association StudiesGenetic Predisposition to DiseaseHumansHyperlipoproteinemia Type IIPhenotypeProtein Interaction MapsSignal TransductionCholesterol, LDLAtherosclerosisBioinformatics analysisFamilial hypercholesterolemiaITGALLDL-CTLN1

Identifiers

PMID32938406
PMCPMC7493348
OpenAlexW3085196864

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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.