Clinical Studies
Subgroup Identification with Gene Expression Profiles of Adipose Tissue in Patients with Coronary Artery Disease
2021 Volume 62 Issue 6 Pages 1199-1206
Details
2021 Volume 62 Issue 6 Pages 1199-1206
Among many diseases, coronary artery disease (CAD) is the primary cause of mortality and morbidity worldwide. With the aim of revealing the underlying genetic characteristics of the CAD subtypes, we recruited patients with CAD and categorized them into subgroups according to the transcriptome expression profiles of the adipose tissue.
With the removal of the batch effect, consensus clustering was employed to determine the subgroup numbers. Subgroup-specific genes were determined to conduct analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Weighted gene co-expression network analysis (WGCNA) revealed the subgroup-specific WGCNA modules. Moreover, gene set enrichment analysis (GSEA) was conducted. Overrepresentation enrichment analysis (OEA) of subgroup-specific signatures was also conducted to reveal the significant gene module associated with the corresponding clinical characteristics.
After the removal of the batch effect, 77 CAD objects were divided into three subgroups. It was observed that the patients in subgroup III tended to be fat. After analyzing the dominant pathways of each subgroup, we discovered that the protein digestion and absorption pathway was specifically upregulated in subgroup I, which might result from the lowest proportion of the epicardial adipose tissue (EAT) sample. Moreover, subgroup II patients had genetic characteristics of high expression of complement and coagulation cascades and TNF signaling pathway. Furthermore, Th17 cell differentiation was significantly upregulated in subgroup III, indicating that Th17 cell differentiation is related to the clinical characteristics of body mass index (BMI).
In conclusion, the genetic classification of CAD subjects indicated that subjects from different subgroups may exhibit specific gene expression patterns, suggesting that more personalized treatment should be applied to patients in each subgroup.
