Smoking-Associated Changes in Gene Expression in Coronary Artery Disease Patients Using Matched Samples

• Published in: Current Issues in Molecular Biology Vol. 46; no. 12; pp. 13893 - 13902
• Main Authors: Merzah, Mohammed, Póliska, Szilárd, Balogh, László, Sándor, János, Fiatal, Szilvia
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.12.2024; MDPI
• Subjects: Brief Report; coronary artery disease; Coronary heart disease; DEGs; Diseases; Gene expression; Genes; Genetic aspects; Hungary; Hypertension; Low density lipoproteins; matched samples; Medical research; Medicine, Experimental; Risk factors; smoking; Triglycerides; Type 2 diabetes; Hungary; coronary artery disease; DEGs; gene expression; smoking; matched samples
• ISSN: 1467-3045; 1467-3037; 1467-3045
• DOI: 10.3390/cimb46120830

Smoking is a well known risk factor for coronary artery disease (CAD). However, the effects of smoking on gene expression in the blood of CAD subjects in Hungary have not been extensively studied. This study aimed to identify differentially expressed genes (DEGs) associated with smoking in CAD subjects. Eleven matched samples based on age and gender were selected for analysis in this study. All subjects were non-obese, non-alcoholic, non-diabetic, and non-hypertensive and had moderate to severe stenosis of one or more coronary arteries, confirmed by coronary angiography. Whole blood samples were collected using PAXgene tubes. Next-generation sequencing was employed using the NextSeq 500 system to generate high-throughput sequencing data for transcriptome profiling. The differentially expressed genes were analyzed using the R programming language. Results: The study revealed that smokers exhibited non-significant higher levels of total cholesterol, low-density lipoprotein-cholesterol, and triglycerides compared to non-smokers (p > 0.05), although high-density lipoprotein-cholesterol was also elevated. Despite this, the overall lipid profile of smokers remained less favorable. Non-smokers had a higher BMI (p = 0.02). Differential gene expression analysis identified 58 DEGs, with 38 upregulated in smokers. The key upregulated genes included LILRB5 (log2FC = 2.88, p = 1.05 × 10−5) and RELN (log2FC = 3.31, p = 0.024), while RNF5_2 (log2FC = −5.29, p = 0.028) and IGHV7-4-1_1 (log2FC = −2.86, p = 0.020) were notably downregulated. Heatmap analysis showed a distinct clustering of gene expression profiles between smokers and non-smokers. However, GO analysis did not identify significant biological pathways associated with the DEGs. Conclusions: This research illuminates smoking’s biological effects, aiding personalized medicine for predicting and treating smoking-related diseases.