ArticleMolecular genetics & genomic medicine2020
A module of multifactor-mediated dysfunction guides the molecular typing of coronary heart disease.
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.
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Who cites it
7 citing papers in PubMed, 8 citations in OpenAlex.
- Early diagnosis of acute myocardial infarction via hub genes identified by integrated weighted gene co-expression network analysis.American heart journal plus : cardiology research and practice · 2025Article
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- Construction and validation of molecular subtypes of coronary artery disease based on ferroptosis-related genes.BMC cardiovascular disorders · 2022Article
- Identification and validation of glomerulotubular crosstalk genes mediating IgA nephropathy by integrated bioinformatics.BMC nephrology · 2022Article
- Article
- Alterations in Kynurenine and NADInternational journal of molecular sciences · 2021Article
- A module of multifactor-mediated dysfunction guides the molecular typing of coronary heart disease.Molecular genetics & genomic medicine · 2020Article
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Authors and funding
10 authors at 1 institution in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
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.
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