Literature DB >> 30805932

Identification of the molecular subgroups in coronary artery disease by gene expression profiles.

Xiao-Yan Peng1, Yong Wang2, Haibo Hu3, Xian-Jin Zhang4, Qi Li5.   

Abstract

Coronary artery disease (CAD) is the most common type of cardiovascular disease and becomes a leading cause of death worldwide. Aiming to uncover the underlying molecular features for different types of CAD, we classified 352 CAD cases into three subgroups based on gene expression profiles, which were retrieved from the Gene Expression Omnibus database. Also, these subgroups present different expression patterns and clinical characteristics. To uncover the transcriptomic differences between the subgroups, weighted gene co-expression analysis (WGCNA) was used and identified six subgroup-specific WGCNA modules. Characterization of the WCGNA modules revealed that lipid metabolism pathways, specifically upregulated in subgroup I, might be an indicator of increased severity. Moreover, subgroup II was considered as an early-stage of CAD because of normal-like gene expression patterns. In contrast, the mammalian target of rapamycin signaling pathway was significantly upregulated in subgroup III. Although subgroups II and III did not have a significant prognostic difference, their intrinsic biological characteristics were highly different, suggesting that the transcriptome classification may represent risk factors of both age and the intrinsic biological characteristics. In conclusion, the transcriptome classification of CAD cases revealed that cases from different subgroups may have their unique gene expression patterns, indicating that patients in each subgroup should receive more personalized treatment.
© 2019 Wiley Periodicals, Inc.

Entities:  

Keywords:  Coronary artery disease (CAD); WGCNA module; gene expression profile; pathway; transcriptome classification

Year:  2019        PMID: 30805932     DOI: 10.1002/jcp.28324

Source DB:  PubMed          Journal:  J Cell Physiol        ISSN: 0021-9541            Impact factor:   6.384


  9 in total

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2.  Weighted correlation network bioinformatics uncovers a key molecular biosignature driving the left-sided heart failure.

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3.  Weighted Gene Co-Expression Network Analysis Identified Cancer Cell Proliferation as a Common Phenomenon During Perineural Invasion.

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4.  Bioinformatic identification of hub genes and related transcription factors in low shear stress treated endothelial cells.

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Journal:  BMC Med Genomics       Date:  2021-05-03       Impact factor: 3.063

5.  Identification of the Molecular Subgroups in Idiopathic Pulmonary Fibrosis by Gene Expression Profiles.

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Journal:  Comput Math Methods Med       Date:  2021-10-04       Impact factor: 2.238

6.  Identification of Potential Molecular Mechanism Related to Infertile Endometriosis.

Authors:  Xiushen Li; Li Guo; Weiwen Zhang; Junli He; Lisha Ai; Chengwei Yu; Hao Wang; Weizheng Liang
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7.  Lipid metabolism patterns and relevant clinical and molecular features of coronary artery disease patients: an integrated bioinformatic analysis.

Authors:  Yanhui Liao; Zhenzhen Dong; Hanhui Liao; Yang Chen; Longlong Hu; Zuozhong Yu; Yi Xia; Yuanbin Zhao; Kunpeng Fan; Jingwen Ding; Xiongda Yao; Tianhua Deng; Renqiang Yang
Journal:  Lipids Health Dis       Date:  2022-09-10       Impact factor: 4.315

8.  Identification of molecular subtypes of coronary artery disease based on ferroptosis- and necroptosis-related genes.

Authors:  Wen-Pan Liu; Peng Li; Xu Zhan; Lai-Hao Qu; Tao Xiong; Fang-Xia Hou; Jun-Kui Wang; Na Wei; Fu-Qiang Liu
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Review 9.  Rescue of Hepatic Phospholipid Remodeling Defectin iPLA2β-Null Mice Attenuates Obese but Not Non-Obese Fatty Liver.

Authors:  Walee Chamulitrat; Chutima Jansakun; Huili Li; Gerhard Liebisch
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  9 in total

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