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Identification of the molecular subgroups in coronary artery disease by gene expression profiles
Author(s) -
Peng XiaoYan,
Wang Yong,
Hu Haibo,
Zhang XianJin,
Li Qi
Publication year - 2019
Publication title -
journal of cellular physiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.529
H-Index - 174
eISSN - 1097-4652
pISSN - 0021-9541
DOI - 10.1002/jcp.28324
Subject(s) - transcriptome , coronary artery disease , biology , gene , gene expression , disease , subgroup analysis , cad , computational biology , bioinformatics , genetics , medicine , meta analysis , biochemistry
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.