Evidence map›Paper›PMID 32743916›Full record

ArticleMolecular genetics & genomic medicine2020

A module of multifactor-mediated dysfunction guides the molecular typing of coronary heart disease.

Yuewei Li, Maohuan Lin, Kangjie Wang, YaQing Zhan, Wenli Gu, Guanghao Gao, Yuna Huang, Yangxin Chen, Tucheng Huang, Jingfeng Wang

Open access · goldAbstract read
In one paragraph

Article in Molecular genetics & genomic medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
0.6field-weighted citation impact, top 38% 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

7 citing papers in PubMed, 8 citations in OpenAlex.

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  6. Alterations in Kynurenine and NADInternational journal of molecular sciences · 2021
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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

10 authors at 1 institution in 3 countries.

Yuewei LiDepartment of Cardiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
Maohuan LinDepartment of Cardiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
Kangjie WangDivision of Vascular Surgery, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
YaQing ZhanDepartment of Anesthesiology, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
Wenli GuDepartment of Cardiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
Guanghao GaoDepartment of Cardiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
Yuna HuangDepartment of Cardiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
Yangxin ChenDepartment of Cardiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
Tucheng HuangDepartment of Cardiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
Jingfeng WangDepartment of Cardiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.ORCID 0000-0003-4598-5256
Sun Yat-sen University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCoronary atherosclerotic heart disease (CHD) is the most common cardiovascular disease and has become a leading cause of death globally. Various molecular typing methods are available for the diagnosis and treatment of tumors. However, molecular typing results are not routinely used for CHD. METHODS AND

resultsAiming to uncover the underlying molecular features of different types of CHD, we screened the differentially expressed genes (DEGs) associated with CHD based on the Gene Expression Omnibus (GEO) data and expanded those with the NCBI-gene and OMIM databases to finally obtain 2021 DEGs. The weighted gene co-expression analysis (WGCNA) was performed on the candidate genes, and six distinctive WGCNA modules were identified, two of which were associated with CHD. Moreover, DEGs were mined as key genes for co-expression based on the module network relationship. Furthermore, the differentially expressed miRNAs in CHD and interactions in the database were mined in the GEO data set to build a multifactor regulatory network of key genes for co-expression. Based on the network, the CHD samples were further classified into five clusters and we defined FTH1, HCAR3, RGS2, S100A9, and TYROBP as the top genes of the five subgroups. Finally, the mRNA levels of FTH1, S100A9, and TYROBP were found to be significantly increased, while the expression of HCAR3 was decreased in the blood of CHD patients. We did not detect measurable levels of RGS2.

conclusionThe screened core clusters of genes may be a target for the diagnosis and treatment of CHD as a molecular typing module.

Indexed as

Gene Regulatory NetworksTranscriptomeAdaptor Proteins, Signal TransducingCalgranulin BCoronary DiseaseFerritinsGenomicsHumansMembrane ProteinsMicroRNAsOxidoreductasesReceptors, G-Protein-CoupledReceptors, NicotinicRGS ProteinsAdaptor Proteins, Signal TransducingCalgranulin BFerritinsFTH1 protein, humanHCAR3 protein, humanMembrane ProteinsMicroRNAsOxidoreductasesReceptors, G-Protein-CoupledReceptors, NicotinicRGS2 protein, humanRGS ProteinsS100A9 protein, humanTYROBP protein, humanCoronary heart disease (CHD)difference analysisdisease classificationGEOnetwork miningWGCNA

Identifiers

PMID32743916
PMCPMC7549572
OpenAlexW3046229892

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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.