A module of multifactor‐mediated dysfunction guides the molecular typing of coronary heart disease. Issue 10 (2nd August 2020)

• Record Type: Journal Article
• Authors: Li, Yuewei; Lin, Maohuan; Wang, Kangjie; Zhan, YaQing; Gu, Wenli; Gao, Guanghao; Huang, Yuna; Chen, Yangxin; Huang, Tucheng; Wang, Jingfeng
• Is Part Of: Molecular genetics & genomic medicine. Volume 8:Issue 10(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-08-02
• Subjects: Coronary heart disease (CHD) -- difference analysis -- disease classification -- GEO -- network mining -- WGCNA; Medical genetics -- Periodicals; Genomics -- Periodicals; 616.042
• DOI: 10.1002/mgg3.1415
• Languages: English
• ISSNs: 2324-9269

Abstract

Abstract: Background: Coronary 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 Results: Aiming 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. Conclusion: The screened core clusters of genes may be a target for the diagnosis and treatment of CHD as a molecular typing module. Abstract : WGCNA was performed on CHD‐related datasets downloaded from online datebases to screened out DEGs and a multi‐factor regulatory network of key genes for co‐expression. Based on the aforementioned network, the CHD samples were further classified into 5 clusters and we defined FTH1, HCAR3, RGS2, S100A9 and TYROBP as individually top genes of 5 subgroups.