A Transcriptomic Analysis of Smoking-Induced Gene Expression Alterations in Coronary Artery Disease Patients.
Mohammed MerzahSzilárd PóliskaLászló BaloghJános SándorIstván SzászShewaye NataeSzilvia FiatalPublished in: International journal of molecular sciences (2023)
Smoking is a well established risk factor for coronary artery disease (CAD). Despite this, there have been no previous studies investigating the effects of smoking on blood gene expression in CAD patients. This single-centre cross-sectional study was designed with clearly defined inclusion criteria to address this gap. We conducted a high-throughput approach using next generation sequencing analysis with a single-end sequencing protocol and a read length of 75-cycles. Sixty-one patients with a median age of 67 years (range: 28-88 years) were recruited, and only 44 subjects were included for further analyses. Our investigation revealed 120 differentially expressed genes (DEGs) between smokers and nonsmokers, with a fold change (FC) of ≥1.5 and a p -value < 0.05. Among these DEGs, 15 were upregulated and 105 were downregulated. Notably, when applying a more stringent adjusted FC ≥ 2.0, 31 DEGs (5 upregulated, annotated to immune response pathways, and 26 downregulated, involving oxygen and haem binding or activity, with FDR ≤ 0.03) remained statistically significant at an alpha level of <0.05. Our results illuminate the molecular mechanisms underlying CAD, fortifying existing epidemiological evidence. Of particular interest is the unexplored overexpression of RCAN3 , TRAV4 , and JCHAIN genes, which may hold promising implications for the involvement of these genes in CAD among smokers.
Keyphrases
- coronary artery disease
- gene expression
- end stage renal disease
- smoking cessation
- immune response
- high throughput
- chronic kidney disease
- newly diagnosed
- ejection fraction
- cardiovascular events
- percutaneous coronary intervention
- dna methylation
- single cell
- coronary artery bypass grafting
- peritoneal dialysis
- randomized controlled trial
- cell proliferation
- prognostic factors
- single molecule
- high glucose
- heart failure
- bioinformatics analysis
- genome wide identification
- patient reported outcomes
- diabetic rats
- binding protein