Gene Expression Changes in CAD Patients Due to Smoking Using Matched Samples

Abstract Background 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 patients in Hungary have not been extensively studied. Aim To identify differentially expressed genes associated with smoking in CAD patients. Methods Eleven matched samples, based on age and gender, were selected for analysis in this study. All patients 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 median age of patients was 67 years (range: 54-75). RNA sequencing was performed on two groups: smokers and non-smokers. After quality control and filtering, gene expression data were obtained for all samples. Using DESeq2, we identified 279 differentially expressed genes with a p-value ≤ 0.05 and a log2 fold change ≥1. Of these genes, 160 were upregulated in the smokers, and 119 were downregulated compared to non-smokers. Gene ontology analysis revealed that the upregulated genes were enriched for pathways related to immune responses and activities (FDR< 0.03). Specifically, upregulated genes were involved in keratinocyte differentiation, cornification, and epidermis development. The downregulated genes were enriched for cell-cardiac muscle cell adhesion (FDR= 0.004) and epithelium development (FDR= 0.001) pathways. Conclusions This research illuminates smoking’s biological effects, aiding personalized medicine for predicting and treating smoking-related diseases. Key messages • Smoking alters gene expression in CAD patients’ blood, identifying 279 differentially expressed genes, revealing smoking’s biological effects. • The study underscores gene expression profiles’ role in personalized medicine, predicting smoking-related disease risks and tailoring CAD patient treatments.