A Signature for Smoking Status of Coronary Heart Disease Patients through Weighted Gene Coexpression Network Analysis. (18th January 2022)

• Record Type: Journal Article
• Authors: Shang, Zhenglu; Sun, Jiashun; Hui, Jingjiao; Yu, Yanhua; Bian, Xiaoyun; Yang, Bowen; Deng, Kewu; Lin, Li
• Other Names: Huang Tao Academic Editor.
• Is Part Of: Computational and mathematical methods in medicine. Volume 2022(2022)
• Publication Date: 2022-01-18
• Subjects: Medicine -- Computer simulation -- Periodicals; Medicine -- Mathematical models -- Periodicals; 610.11
• DOI: 10.1155/2022/5777946
• Languages: English
• ISSNs: 1748-670X

Abstract

Abstract : Background . Smoking is one of the risk factors of coronary heart disease (CHD), while its underlying mechanism is less well defined. Purpose . To identify and testify 6 key genes of CHD related to smoking through weighted gene coexpression network analysis (WGCNA), protein-protein interaction (PPI) network analysis, and pathway analysis. Methods . CHD patients' samples were first downloaded from Gene Expression Omnibus (GEO). Then, genes of interest were obtained after analysis of variance (ANOVA). Thereafter, 23 coexpressed modules that were determined after genes with similar expression were incorporated via WGCNA. The biological functions of genes in the modules were researched by enrichment analysis. Pearson correlation analysis and PPI network analysis were used to screen core genes related to smoking in CHD. Results . The violet module was the most significantly associated with smoking (r = − 0.28, p = 0.006 ). Genes in this module mainly participated in biological functions related to the heart. Altogether, 6 smoking-related core genes were identified through bioinformatics analyses. Their expressions in animal models were detected through the animal experiment. Conclusion . This study identified 6 core genes to serve as underlying biomarkers for monitoring and predicting smoker's CHD risk.