Integrated analysis of gene expression changes associated with coronary artery disease

Integrative Bioinformatics Approaches to Uncover Hub Genes and Pathways Involved in Cardiovascular Diseases

Cardiovascular diseases (CVD) represent a significant global health challenge resulting from a complex interplay of genetic, environmental, and lifestyle factors. However, the molecular pathways and genetic factors involved in the onset and progression of CVDs remain incompletely understood. Here, we performed an integrative bioinformatic analysis to highlight specific genes and signaling pathways implicated in the pathogenesis of 80 CVDs. Differentially expressed genes (DEGs) were identified through the integrated analysis of microarray and GWAS datasets. Then, hub genes were identified after gene ontology functional annotation analysis and protein-protein internet (PPI) analysis. In addition, pathways were identified through KEGG and gene ontology enrichment analyses. A total of 821 hub genes related to 80 CVDs were identified, including 135 common and frequent CVD-associated genes. TNF, IL6, VEGFA, and TGFB.1 genes were the central core genes expressed in 50% or more of CVDs, confirming that the inflammation is a key pathological feature of CVDs. Analysis of hub genes by KEGG enrichment revealed predominant enrichment in 201 KEGG pathways, of which the AGE-RAGE signaling pathway in diabetic complications was identified as the common key KEGG implicated in 62 CVDs. In addition, the outcomes showed an overrepresentation in pathways categorized under human diseases, particularly in the subcategories of infectious diseases and cancers, which may be common risk factors for CVDs. In conclusion, this powerful approach for in silico fine-mapping of genes and pathways allowed the identification of determinant hubs genes and pathways implicated in the pathogenesis of CVDs which could be employed in developing more targeted and effective interventions for preventing, diagnosing, and treating CVDs. The function of these hub genes in CVDs needs further exploration to elucidate their biological characteristics.

Differential Expression of Subsets of Genes Related to HDL Metabolism and Atherogenesis in the Peripheral Blood in Coronary Artery Disease

Differential expression of genes (DEGs) in coronary artery disease (CAD) and the association between transcript level and high-density lipoprotein cholesterol (HDL-C) were studied with 76 male patients with CAD and 63 control patients. The transcript level of genes related to HDL metabolism (24 genes) and atherosclerosis-prone (41 genes) in RNA isolated from peripheral blood mononuclear cells was measured by real-time RT-PCR. Twenty-eight DEGs were identified. The expression of cholesterol transporters, ALB, APOA1, and LCAT was down-regulated, while the expression of AMN, APOE, LDLR, LPL, PLTP, PRKACA, and CETP was up-regulated. The systemic inflammation in CAD is evidenced by the up-regulation of IL1B, TLR8, CXCL5, and TNFRSF1A. For the controls, TLR8 and SOAT1 were negative predictors of the HDL-C level. For CAD patients, PRKACG, PRKCQ, and SREBF1 were positive predictors, while PRKACB, LCAT, and S100A8 were negative predictors. For CAD patients, the efficiency of reverse cholesterol transport is 73-79%, and intracellular free cholesterol seems to accumulate at hyperalphalipoproteinemia. Both atheroprotective (via S100A8) and proatherogenic (via SREBF1, LCAT, PRKACG, PRKACB, and PRKCQ) associations of gene expression with HDL-C determine HDL functionality in CAD patients. The selected key genes and involved pathways may represent HDL-specific targets for the diagnosis and treatment of CAD and atherosclerosis.

Identification and Validation of Key Gene Modules and Pathways in Coronary Artery Disease Development and Progression

The development and progression of atherosclerosis represent a chronic process involving complex molecular interactions. Therefore, identifying the potential hub genes and pathways contributing to coronary artery disease (CAD) development is essential for understanding its underlying molecular mechanisms. To this end, we performed transcriptome analysis of peripheral venous blood collected from 100 patients who were divided into four groups according to disease severity, including 27 patients in the atherosclerosis group, 22 patients in the stable angina group, 35 patients in the acute myocardial infarction group, and 16 controls. Weighted gene co-expression network analysis was performed using R programming. Significant module-trait correlations were identified according to module membership and genetic significance. Metascape was used for the functional enrichment of differentially expressed genes between groups, and the hub genes were identified via protein-protein interaction network analysis. The hub genes were further validated by analyzing Gene Expression Omnibus (GSE48060 and GSE141512) datasets. A total of 9,633 messenger ribonucleic acids were detected in three modules, among which the blue module was highly correlated with the Gensini score. The hub genes were significantly enriched in the myeloid leukocyte activation pathway, suggesting its important role in the progression of atherosclerosis. Among these genes, the Mediterranean fever gene (MEFV) may play a key role in the progression of atherosclerosis and CAD severity.

Identification of MYH6 as the potential gene for human ischaemic cardiomyopathy

The present study aimed to explore the potential hub genes and pathways of ischaemic cardiomyopathy (ICM) and to investigate the possible associated mechanisms. Two microarray data sets (GSE5406 and GSE57338) were downloaded from the Gene Expression Omnibus (GEO) database. The limma package was used to analyse the differentially expressed genes (DEGs). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, Disease Ontology (DO) and Gene Ontology (GO) annotation analyses were performed. A protein‐protein interaction (PPI) network was set up using Cytoscape software. Significant modules and hub genes were identified by the Molecular Complex Detection (MCODE) app. Then, further functional validation of hub genes in other microarrays and survival analysis were performed to judge the prognosis. A total of 1065 genes were matched, with an adjusted p < 0.05, and 17 were upregulated and 25 were downregulated with|log₂ (fold change)|≥1.2. After removing the lengthy entries, GO identified 12 items, and 8 pathways were enriched at adjusted p < 0.05 (false discovery rate, FDR set at <0.05). Three modules with a score >8 after MCODE analysis and MYH6 were ultimately identified. When validated in GSE23561, MYH6 expression was lower in patients with CAD than in healthy controls (p < 0.05). GSE60993 data suggested that MYH6 expression was also lower in AMI patients (p < 0.05). In the GSE59867 data set, MYH6 expression was lower in CAD patients than in AMI patients and lower in heart failure (HF) patients than in non‐HF patients. However, there was no difference at different periods within half a year, and HF was increased when MYH6 expression was low (p < 0.05–0.01). We performed an integrated analysis and validation and found that MYH6 expression was closely related to ICM and HF. However, whether this marker can be used as a predictor in blood samples needs further experimental verification.

Integrated Network and Gene Ontology Analysis Identifies Key Genes and Pathways for Coronary Artery Diseases

BACKGROUND

The prevalence of Coronary Artery Disease (CAD) in developing countries is on the rise, owing to rapidly changing lifestyle. Therefore, it is imperative that the underlying genetic and molecular mechanisms be understood to develop specific treatment strategies. Comprehensive disease network and Gene Ontology (GO) studies aid in prioritizing potential candidate genes for CAD and also give insights into gene function by establishing gene and disease pathway relationships.

METHODS

In the present study, CAD-associated genes were collated from different data sources and protein-protein interaction network was constructed using STRING. Highly interconnected network clusters were inferred and GO analysis was performed.

RESULTS

Interrelation between genes and pathways were analyzed on ClueGO and 38 candidates were identified from 1475 CAD-associated genes, which were significantly enriched in CAD-related pathways such as metabolism and regulation of lipid molecules, platelet activation, macrophage derived foam cell differentiation, and blood coagulation and fibrin clot formation.

DISCUSSION

Integrated network and ontology analysis enables biomarker prioritization for common complex diseases such as CAD. Experimental validation and future studies on the prioritized genes may reveal valuable insights into CAD development mechanism and targeted treatment strategies.

Weighted gene coexpression network analysis identifies the key role associated with acute coronary syndrome

The present study sought to identify potential hub genes and pathways of acute coronary syndrome (ACS). We downloaded the dataset (GSE56045) from the Gene Expression Omnibus (GEO) database and analyzed weighted gene coexpression networks (WGCNA). Gene Ontology annotation, Disease Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using R software. The protein-protein interaction (PPI) network was constructed using Cytoscape, and the Molecular Complex Detection app was employed to identify significant modules and hub genes. The hub genes were then validated in other microarrays and patients by RT-PCR. Two modules were identified and associated with coronary artery disease (CAD) and included 219 genes. After function and PPI analyses, 24 genes were identified to be potentially associated with CAD. Linear correlation was performed to calculate the relationship between the gene expression levels and coronary artery calcification score and found that CCR7 (R = -0.081, P = 0.0065), CD2 (R = -0.075, P = 0.0012), CXCR5 (R = -0.065, P = 0.029) and IL7R (R = -0.06, P = 0.043) should be validated in other dataset. By comparing the gene expression levels in different groups in GSE23561, GSE34822, GSE59867, GSE60993 and GSE129935, only two genes (CCR7 and CXCR5) showed significance. The nomogram showed that CXCR5 showed the risk of ACS. Further analysis in chest patients found CXCR5 played a key role resulting in ACS. Our WGCNA analysis identified CXCR5 as a risk factor for ACS, and the potential pathogenesis may be associated with immune inflammation.

The Integrated Landscape of Biological Candidate Causal Genes in Coronary Artery Disease

Background

Genome-wide association studies (GWASs) have identified more than 150 genetic loci that demonstrate robust association with coronary artery disease (CAD). In contrast to the success of GWAS, the translation from statistical signals to biological mechanism and exploration of causal genes for drug development remain difficult, owing to the complexity of gene regulatory and linkage disequilibrium patterns. We aim to prioritize the plausible causal genes for CAD at a genome-wide level.

Methods

We integrated the latest GWAS summary statistics with other omics data from different layers and utilized eight different computational methods to predict CAD potential causal genes. The prioritized candidate genes were further characterized by pathway enrichment analysis, tissue-specific expression analysis, and pathway crosstalk analysis.

Results

Our analysis identified 55 high-confidence causal genes for CAD, among which 15 genes (LPL, COL4A2, PLG, CDKN2B, COL4A1, FES, FLT1, FN1, IL6R, LPA, PCSK9, PSRC1, SMAD3, SWAP70, and VAMP8) ranked the highest priority because of consistent evidence from different data-driven approaches. GO analysis showed that these plausible causal genes were enriched in lipid metabolic and extracellular regions. Tissue-specific enrichment analysis revealed that these genes were significantly overexpressed in adipose and liver tissues. Further, KEGG and crosstalk analysis also revealed several key pathways involved in the pathogenesis of CAD.

Conclusion

Our study delineated the landscape of CAD potential causal genes and highlighted several biological processes involved in CAD pathogenesis. Further studies and experimental validations of these genes may shed light on mechanistic insights into CAD development and provide potential drug targets for future therapeutics.

Interleukin-1β (IL-1β) C-511T polymorphism is associated with susceptibility to coronary artery disease in type 2 diabetic patients

Variants of the interleukin-1β (IL-1β) gene have been associated with type 2 diabetes (T2D) and coronary artery diseases (CAD). However, association of IL-1β polymorphisms with diabetic patients having CAD clinical manifestation has not been studied yet. In this study, we aim to decipher the role of IL-1β common promoter variants with susceptibility/resistance to development of CAD in T2D patients. T2D patients with (n = 134) or without CAD (n = 533) were enrolled. A total of 513 essentially healthy individuals from the same population were included in this study as control. Plasma levels of IL-1β were quantified by enzyme-linked immunosorbent assay (ELISA) kit as per instructions from the manufacturer. IL-1β promoter variants (T-31C and C-511T) were genotyped by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP). T2D patients displayed higher level of plasma IL-1β in comparison to healthy controls. Prevalence of variants for IL-1β (C-511T) polymorphism was higher in diabetic patients compared to controls (CT: P < 0.0001, OR = 3.01; TT: P < 0.0001, OR = 2.45). IL-1β (C-511T) polymorphism was linked with plasma IL-1β levels. Interestingly, heterozygous mutants (CT) were most prevalent in T2D individuals with CAD compared to those without CAD ( P = 0.03, OR = 1.82). Furthermore, low-density lipoprotein (LDL) and triglycerides were elevated in T2D patients with CAD than in patients without heart-related disorder. No significant association of other polymorphism (T-31C) was noticed with susceptibility to T2D or diabetic patients with heart disorders. IL-1β (C-511T) variants are associated with elevated plasma IL-1β levels. Mutation at the IL-1β promoter region (C-511T) predisposed subjects to the development of T2D and CAD manifestation in diabetic patients.

Association between promoter DNA methylation and gene expression in the pathogenesis of ischemic stroke

To assess DNA methylation sites as well as gene expression related to ischemic stroke (IS) and comprehensively reveal their correlation and possible pathological mechanisms, we implemented (1) genome-wide DNA methylation profiling from the GEO repository related to IS with and without symptoms; (2) identification of differentially methylation positions (DMPs) and genes (DMGs), functional enrichment analysis along with DMG regulatory network construction; (3) validation tests of 2 differential methylation positions of interest as well as analogous gene expression in other datasets and in IS patients and controls; and (4) correlation analysis of DNA methylation and mRNA expression data. In total, 870 DMPs were physically located within 693 DMGs. After disease ontology (DO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, gene ontology (GO), protein-protein interaction (PPI) network construction as well as module analysis, HLA-DRB1 and HLA-DQB1 were identified. Their expression was validated in 4 other datasets but was significant in only 1, and the expression was lower in the IS group (P < 0.05). After validation in IS patients and controls, we found that these two genes showed more hypermethylation and lower expression levels in the IS group (P < 0.001). The methylation of genes was negatively associated with their expression (P < 0.05). The current study recognized a connection among DNA methylation and gene expression and emphasized the prominence of HLA-DRB1 and HLA-DQB1 in IS pathogenesis.